Do These Genes Make Me Look Dense?

CHAPTER 23 — from Mammography and Early Breast Cancer Detection: How Screening Saves Lives by Alan B. Hollingsworth, MD (McFarland & Company, Jefferson, NC, 2016).

…a history of mammography leading to current controversies, a primer on the epidemiology of screening, and a polemic designed to endorse the need for multi-modality imaging in order to improve early detection.

LINK to Publisher’s Page: http://www.mcfarlandbooks.com/book-2.php?id=978-1-4766-6610-5

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Shortly before the new year of 2004, an educator from Connecticut underwent her annual screening mammography, as she had been doing for years. Like before, the results were “All clear.” Six weeks later, she was diagnosed with Stage IIIC breast cancer. She was stunned to learn, for the first time, that she had very dense breast tissue on mammography, the feeble explanation as to why her cancer had been invisible (probably detectable several years earlier with other modalities).

 

Nancy M. Cappello, PhD has since joined the list of solo game-changers, that is, non-celebrities who have risen from anonymity to turn their misfortune into far-reaching upheavals that have permanently altered how we manage breast cancer. In the footsteps of Rose Kusher (ending the one-step biopsy/frozen section/mastectomy approach), Betty Rollin (First You Cry, the book and movie that introduced many to the emotional impact of mastectomy), Susan Komen and her sister Nancy Brinker (initially, the promotion of screening mammography, then breast cancer research in general), Nancy Cappello set fire to sweeping reforms as to how women are informed about their breast density.

 

As of this writing, nearly half of the 50 states have passed legislation requiring radiologists to inform patients when mammograms are dense (white on X-ray) and to describe additional imaging modalities that might help, most notably ultrasound. Federal legislation is being considered as well. Through the Are You Dense? Organization (RUdense.org) that Dr. Cappello founded, Connecticut became the first state to require that high density information be given to patients, then she has encouraged all states to do the same. Additionally, Connecticut became the first state to pass legislation requiring third party payors to cover ultrasound screening for women with dense breast tissue.

 

What added fuel to Dr. Cappello’s conflagration was the discovery that the medical profession knew about the problem of breast density all along, but did precious little to inform the public.

 

I sympathize without excuse. I do have mixed emotions about legislated physician practice, but this bundle of provocative data about density was dropped into a black hole, it seems. As an aside, it has always been a curious phenomenon that some information new to the scene is processed quickly and new standards adopted overnight, while other innovations or ideas are ignored. After decades of medical literature warning about the danger of breast density, it was like the boy who cried “Wolf,” with readers becoming numb to the data, if they paid attention at all.

 

And for those historians of breast density who have already groaned at the pun, it was Dr. John N. Wolfe who first drew attention to mammographic density patterns, publishing his classification system of N1, P1, P2 and DY that decorated the bottom of mammography reports starting in 1976(1). I read my first mammogram report with the Wolfe pattern noted in 1980, my first year in private practice. The reaction was straightforward at that time – so what? There were no good alternative methods for imaging, and the focus then was not on mammographically invisible cancers. In fact, Dr. Wolfe proposed his system entirely on the basis of imparted risk for breast cancer, yet there were no interventions available at the time short of preventive mastectomy. In fact, Dr. Wolfe was so convinced that women with the DY pattern were headed for breast cancer (45% lifetime risk by his calculations) that he felt they should seriously consider preventive mastectomies. And with the introduction of breast implants at about this same time, “subcutaneous mastectomies with implants” were already being performed for reasons far less impressive than Wolfe DY patterns.

 

But then, we entered a silent period, where at the clinical level, Wolfe patterns were gradually dismissed, sometimes as an old-fashioned “folly.” Certain investigators, though, were using different classifications schemes, but still coming up with the same conclusion – denser breasts translated to greater risk. Still, the entire focus was still on imparted risk. It was only later that clinicians began to appreciate that breast density was double jeopardy. Not only was density imparting elevated risk, but also it was responsible for hidden cancers missed by the very mammography that defined density in the first place.

 

What is most peculiar in re-tracing my steps during this era is that no one was arguing anything different. It wasn’t controversial, rather, it was esoteric. The risk data for density was consistent no matter how the density levels were described – the highest density breasts had roughly a 4-6 fold relative risk for breast cancer when compared to the lowest density breasts. And, lagging behind, only a handful of studies prior to 1990 showed the danger of density when it came to early detection. The vast majority of clinicians believed the “90% Sensitivity” for mammography, independent of density levels.

 

A word about 4-6 fold relative risk. Recall from the Number Games chapter that relative risks (RRs) are fractions with a numerator and a denominator. When the word hit the street about the 4-6 fold risk, many patients were terrorized by these numbers. The problem with this particular RR is that the number applies to the highest density compared to the lowest density, the latter being present in only 10-15% of the population (hardly the average woman). In epidemiology-speak, the “referent” was this low density group, not the average woman. Indeed, the “average” density patient is at 2-fold risk for breast cancer when compared to predominantly fatty breasts.

 

How can someone be “average risk” and “2-fold risk” at the same time? By switching out the denominators. That’s why they are called relative risks. The RR will change if you alter either numerator or denominator. Forget the math if it’s not your thing – if we compare women with extremely dense tissue to the average patient (not the low density group) we get a more acceptable RR of 2.0.   This degree of risk is more in line with having a first-degree relative diagnosed with breast cancer at age 45, whereas an RR of 4.0 would be like having two first-degree relatives diagnosed with breast cancer at 45.

 

While nearly all the focus was on the imparted risk of mammographic density, pioneering radiologists interested in ultrasound saw immediate application for screening women with dense breasts. Radiologist Dr. Thomas Kolb, then at Columbia University in New York City, was certainly at the forefront and became an activist for screening ultrasound. Within a decade, at least 10 large studies, totaling 60,000 patients, had been published(2), all showing similar results – the number of additional cancers discovered after negative mammograms and negative clinical exam ranged from 2.71 per 1,000 to 4.61 per 1,000. Roughly speaking, this is a relative 50-100% improvement over mammograms alone. The Society of Breast Imaging made its recommendation for ultrasound screening accordingly.

 

The evidence for MRI screening began trickling in about this same time as well, so at my facility, we created a “Breast Density” brochure in the early 2000s, describing the double jeopardy of density as well as the recommendation to consider multi-modality imaging with either ultrasound or MRI. At the time we initiated this program, there were no screening guidelines for multi-modality imaging at all, so we developed a scoring system that combined risk and density for patient selection, giving equal weight to both. After all, if one is going to recommend a second tier of imaging, then it should be based on the probability that the first tier is going to fail(3) – and the most powerful predictor for first tier failure is breast density.

 

While the primary interest in breast density seemed fixated on the associated cancer risk rather than the hidden cancer rate, even the risk agenda failed to generate momentum. To this day, one has to seek out maverick modifications of our mathematical models (e.g., Tice modification of the Gail model) in order to incorporate density levels into formal risk assessment. This, after hundreds of articles have confirmed the relationship of density and risk.

 

In my presentations about multi-modality screening, primarily MRI, I would offer the overwhelming evidence that mammographic density is a risk factor, then call it, “The Rodney Dangerfield of Risk Factors.” It doesn’t get any respect. Yes, it’s talked about all the time, but try to work the density level into your formal risk assessment program, and you’ll need to have those maverick models at your disposal. (Addendum: After publication of this book, in 2017, the Tyrer-Cuzick model released version 8.0 that included breast density in calculating breast cancer risk.)

 

In 2009, a study published in the Journal of the National Cancer Institute(4) described a meta-analysis of 47 studies of breast density related to breast cancer risk, involving 28,521 cancer patients and 3 different ways to categorize density levels – all 3 methods showed the same thing – a 4-fold risk for breast cancer when the top category is compared to the bottom category, and approximately a 2-fold risk when compared to the average patient. Once we add the possibility of invisible (or “missed”) cancers on top of the risk problem, the Rodney Dangerfield reference was really an understatement. Everything has changed now, with heightened awareness of density, not via 47 studies combined into one meta-analysis, but through one woman trying to reach 50 states.

 

The double jeopardy concept seems to trip up even the experts. When the American Cancer Society issued their 2007 guidelines for breast MRI screening, they treated mammographic density as a risk factor, placing it in the “insufficient evidence” category along with “15-20% lifetime risk” and other modest risk factors. Density was not considered as a determinant of “first tier” sensitivity for women, that is, an independent predictor of mammographic failure. Fortunately, the recommendation for adding ultrasound to screening high-density women was adopted by the Society of Breast Imaging, but then again, unfortunately, experts are no longer the voice of authority. The Society of Breast Imaging guidelines have gone largely unheeded, while we wait for “neutral epidemiologists” to weigh in.

 

No one has attempted an ultrasound screening trial where the endpoint is mortality reduction, given all the problems we’ve seen already with mammography, not the least of which is obsolete technology by the time the requisite follow-up is complete. Instead, we have relied on prospective trials (non-randomized) where participants undergo multi-modality imaging, and then cancer yields are recorded for each method individually – mammography, ultrasound and MRI – and in combination. Improved cancer yields are reflective of a mortality reduction, though purists will be quick to point out that we can’t rule out the impact of the Big Four biases.

 

We have very good offerings for imaging beyond mammography available now, barely utilized, though with much improved outcomes:

Mammographic density and no other risk factors = ultrasound

Mammographic density plus additional risks = ultrasound or MRI (perhaps alternating)

Women at very high risk, regardless of density = MRI

 

Nancy Cappello saw the problem shortly after her own diagnosis. Breast density is double jeopardy. It is a risk factor, and it is a predictor of mammographic failure.

Several questions often arise with regard to breast density. First, “How did I get it?” And, “what can I do about it?”

Baseline breast density is a product of your genes, though environmental influences do cause alterations. Interestingly, some of these alterations are associated with a similar change in breast cancer risk, raising the question as to whether or not density can be used as a surrogate measure for risk-reducing strategies. Each pregnancy lowers breast density a bit, and breast-feeding does as well, both known to be protective factors when occurring in the younger age groups.

 

Other factors don’t fit so well. Body fat (BMI = body mass index) is a confounding variable that doesn’t always match up with breast density risk. In fact, for many years, it slowed down acceptance of breast density as a risk factor. Ethnicity doesn’t always match either. Asian women, in general, have a much higher density level than African-American women, yet the breast cancer risks are higher for African-Americans. Postmenopausal hormone replacement therapy, especially estrogen and progesterone, can result in an increase in mammographic density. And, the use of SERMs (Selective Estrogen Receptor Modulators – tamoxifen or raloxifene) can lower breast density, perhaps reflecting when they are also preventing breast cancer.

 

Taking “statins” to prevent cardiovascular events and death is widely accepted, even though, as with any preventive health measure, it is the minority who benefit. Yet, the surrogates – blood lipid levels – have become endpoints unto themselves. “We’ve successfully treated your hyperlipidemia” ignores the fact that the goal is something else entirely – that is, reducing the probability of cardiovascular events and death. The lack of such a surrogate may be one of the reasons why so few women accept a recommendation to take the SERMS, which are FDA-approved to reduce the risk of breast cancer. Fairly good evidence suggests that the same women who have a lowering of their breast density pattern while on SERM therapy are the same ones benefitting most from the drug. Perhaps someday, breast density will become an official surrogate, and pharmacologic risk reduction will enjoy greater popularity.

 

A common misconception is that “young women have dense tissue, while older women do not,” thus explaining the superior efficacy of mammography in older women. While these differences may be true in general, exceptions are quite common. Some women who start screening in their 30s will have low density mammograms, and we see 80 year-olds with mammograms where we can’t see a thing. Some women gradually become less dense after menopause, others do not, especially if they take estrogen-plus-progesterone hormone replacement therapy. And, there is no sharp loss of density at menopause. In fact, if you look at density in the 40s as a whole and compare it to density in the 50s, there is very little difference. The point is that breast density is a highly individual situation. I’ve included an assessment of density in my risk assessment program for 20 years. My reasoning for this is based on the double jeopardy issue, with a determination of the individual’s estimated sensitivity for breast cancer detection should it occur, a separate issue from density as a risk factor.

 

Now, one quibble with the Are You Dense? educational efforts. As often happens in medicine, we are confronted by continuums for which we must create artificial (and subjective) classification schemes. Are You Dense? has opted to use the dichotomy approach – two groups, dense and non-dense, with 50% density as the dividing line, an approach used in some clinical trials as well. The problem here is that the 50% point is where subjectivity is at its worst.

 

Picture a bell curve, which is what we nearly have in breast density, from 0 to 100%, with most women bunched in the middle. Then, you draw a line straight down from the peak of that curve, and you have the majority of women clustered right at the dividing line. Not only do radiologists routinely differ at this point in the dividing line, studies have shown that the same radiologist will call the density level different from year-to-year in the same patient, even when the density level is unchanged. This is no one’s fault – it’s simply the nature of subjectivity applied in a quantitative fashion to a phenomenon that has a strong qualitative feature as well. In other words, it’s not merely how much density is present, but what is the nature of that density – small patches of white? Large patches of white? Diffuse haziness? Net-like strings of white? Net-like strings of white interconnecting small white patches with a diffusely hazy background?

 

Once breast density became an accepted risk factor and predictor of mammographic failure, an entire industry arose in order to quantify it in a meaningful fashion, using software that spits out exact percentages of density, a number that can be translated to both “risk levels” and “sensitivity levels.” The resilient Dr. Dan Kopans, surfacing again on this issue, has spent considerable effort trying to educate the world about the subjectivity of breast density, pointing out the extreme complexity that underscores all attempts to simplify the problem, that is, the qualitative issues are every bit as important as the quantitative.

 

For the past 20 years, the American College of Radiology has required that interpreters of mammography describe the degree of breast density, dividing into 4 categories. The definition for each category has changed slightly over time, but what used to be Levels 1-4 are now called Levels A-D, generally based on quartiles of density: 0-25%, 25-50%, 50-75%, 75%-100%, though with some modifiers. Unfortunately, these percentages have not until recently been listed in a straightforward fashion on the radiology report. Instead, radiologists were directed to dictate “in code” using a formal lexicon. For instance, “scattered fibroglandular densities” was code for a Level 2 (now called Level B) mammogram, or 25-50%. Interestingly, only Level A does not include a disclaimer about reduced sensitivity for the detection of cancer. Levels B, C and D each have different wording that describes increasing concern about lowered sensitivity with increasing density. Primary care physicians grew immune to the redundant terminology that was actually “code,” but you can imagine Nancy Cappello’s shock to discover that this density information rarely made it to the patient.

 

In my practice, as I am reviewing the mammograms for the double jeopardy, I attempt to incorporate the quality of density as well as the quantity, the latter having already been addressed by the radiologist with A, B, C and D levels. In my mind’s eye, I picture a small invasive cancer, about 1.0 to 1.5cm, and I move that imaginary cancer around both the MLO views and the CC views while asking myself a simple question: Is there anywhere this (imaginary) cancer could hide? Invasive lobular carcinoma can prove problematic to this approach, as the diffuse growth pattern in some lobulars allows them to hide anywhere they want. This approach is still subjective, but it’s not a false dichotomy. Generally speaking, the overall percentage of white reflects the imparted risk level, while the qualitative pattern of the white allows me to predict the probability that a small cancer will be detected (Sensitivity).

 

Importantly, this “roving” 1.0 to 1.5cm (imaginary) tumor approach to mammographic review is based on the premise from the previous chapter that overall density pattern is only an indirect predictor of invisible cancers – the real problem is the density immediately adjacent to the tumor that could completely encase the cancer. This is why even Level B density (scattered fibroglandular densities, or 25-50% dense) was originally accompanied by a disclaimer on radiology reports that sensitivity could be compromised, while the dichotomy approach would call these patients “non-dense.” When you add the possibility that the origin of cancer might be primarily within these patches, it can render some uncomfortable conclusions about mammography used alone.

 

I would be remiss if I didn’t briefly mention the controversial entity of DCIS (ductal carcinoma in site) that is a product of screening mammography, usually presenting in the form of a calcium cluster, that is, tiny white dots clustered into a group on X-ray. Calcium appears on mammography better than ultrasound or even MRI, so mammography has held the top spot in the hierarchy of screening. But now that DCIS is dropping in popularity as a worthy goal, ultrasound has gained momentum in that a higher percentage of ultrasound-discovered cancers are small invasive cancers, rather than DCIS. To be specific, mammography-discovered cancers will be DCIS about 25-40% of the time, while ultrasound-discoveries are DCIS only 10-20% of the time. So, some observers claim superiority of ultrasound over mammography in the very high density groups, given one or the other. This introduces the speculative, but attractive, notion of screening with ultrasound alone in selected patients.

 

Certainly, mammograms alone in this highest density group (greater than 75%) have failed to deliver. I once encountered a breast center that had a disclaimer at the bottom of its reports: “4% to 8% of breast cancers are not visible on mammography.” I did not make any friends when I openly stated that the only way to make that statement true in a patient with extreme breast density was to add a zero to both numbers, that is “40% to 80% of breast cancers are not visible.” People thought I was kidding, or at least exaggerating. I was not. Years later, when DMIST demonstrated the sub-group of film screen technique in young women with dense breasts, and only 27% Sensitivity (a 73% miss rate), I was vindicated, although still not popular.

 

With the great advantage of lower cost and greater patient comfort when compared to breast MRI, whole breast screening ultrasound has some distinct advantages. The drawback is specificity, with more benign biopsies (note: I do not use the pejorative term, “unnecessary biopsies”) than occur with mammography or MRI. Most studies show that an ultrasound-generated biopsy will be malignant only 5-10% of the time (compared to 20% for mammography and 30-40% for MRI).

 

Furthermore, there is a bigger difference in technique and interpretations with ultrasound than mammography, very much related to experience and skill. To that end, a new development (albeit in research status for 40 years) is “automated whole breast ultrasound,” which gives a standardized picture of the entire breast that should be very desirable for the screening setting. While targeted, handheld ultrasound will still be better for diagnostic problems, the automated whole breast ultrasound approach should be a great addition for the screening tools available for women at average risk, but with dense mammograms. That said, preliminary data suggests that a physician using hand-held ultrasound will detect more cancers than the automated approach.

 

Although single-center studies and multi-center studies have revealed that screening ultrasound increases cancer yields by 3-4 per 1,000 above the 5-7 cancers found by mammography, I’m only going to review one of the most important studies – ACRIN 6666 (5).

 

The American College of Radiology Imaging Network designed a trial to study ultrasound as a complement to mammography in high-risk, high density patients. Notice that participants had to have both – traditional risk factors in addition to the inherent risk of breast density. Although there is not a control group in a study like this for cancer yields (a no imaging at all group), care was taken to account for as many variables as possible. For instance, patients were randomized as to which study was performed first (mammography or ultrasound), and the radiologists were blinded as to the results of one study when interpreting the other.

 

Kudos to the trial designers who addressed every possible criticism that I can dream up concerning current multi-modality guidelines. First, the study designers realized the age-discrimination inherent when lifetime risks are used as a sole criterion. The 60-year-old patient with risk factors, and at peak short-term incidence of breast cancer, often won’t qualify for MRI because of fewer remaining years in her lifetime. So, the ACRIN 6666 team came up with short-term risk calculations, as well as standard long-term calculations. Then, designers also realized the “qualitative” problem of density, allowing certain patients to qualify for the trial if there were large patches of white even though overall density was less than 50%. And then, one of the most subtle, but perceptive, entry requirements allowed an adjustment in traditional risk requirements based on density level, that is, the extremely dense patient did not need as many traditional risk factors to qualify, and conversely, the lower density patients needed higher calculated risk. I can’t say it made any difference in the outcome, but I will offer personal testimony to this – these entry requirements were so sophisticated in design that they could be used for all forms of multi-modality imaging. Enough praise to Dr. Wendie Berg and her team, moving on to results.

 

From 2004 to 2006, 2,662 women (with both density and traditional risks) at 21 sites underwent 3 rounds of double screening with mammograms and bilateral, whole breast ultrasound, at 0, 12 and 24 months. From the boatloads of data generated, let’s go with this:

 

33 cancers were detected by mammography alone, 32 detected by ultrasound alone, and 26 were seen on both mammography and US. Clearly, the modalities are detecting different aspects of cancer, as less than a third of the cancers were seen on both modalities. This suggests a powerful complementary role to ultrasound. In fact, ultrasound appears equal to mammography, if picking one or the other.

 

A breakdown of tumor size shows that mammography detected tumors with a mean size of 1.15cm whereas US detected tumors with a mean of 1.0cm. This close comparison in size indicates that US is not finding cancers much earlier than mammography, it is capturing the cancers that ought to be large enough to be seen on mammography, but were missed, presumably due to density. Yet, there is an important difference: 75% of the cancers were invasive by ultrasound, while only 52% were invasive by mammography. Because these ultrasound-discovered invasive cancers were small and usually node-negative (4% with positive nodes, compared to 33% node positivity for mammograms alone), ultrasound could again be declared the winner in head-to-head competition. That said, ACRIN 6666 was not looking for a “winner” when it came to these two modalities.

 

Beyond digital mammography, ultrasound generated 5.3 cancers/1,000 the first year and 3.7 per 1000 in each of the second and third screens. What about sensitivity, documenting the benefit from a different angle? Discounting the first screen (as a prevalence screen), the next two incidence screens revealed a sensitivity of 76%…combined! That’s right. Both mammography and ultrasound together missed 24% of cancers. Sensitivity of mammography alone was 52%. Granted, this is not your average patient population, but “risk levels” have no influence on sensitivity levels. Density, on the other hand, is the primary determinant of sensitivity.

 

But that’s not where the story ends. ACRIN 6666 designed a sub-study, where women could undergo a single breast MRI at the conclusion of the 3 screens with mammography and ultrasound. Only 612 women of the 2,662 moved ahead with this aspect of the trial, so a separate set of statistics was created for this group. An additional 9 cancers were discovered on the single MRI (8/9 invasive with an average size of 0.85 cm, all node negative). Converted to our 1,000 standard, this was an additional cancer yield of 14.7 per 1,000. Had all 2,662 women participated, an extrapolation would indicate that 39 additional cancers would have been detected, in a group that had been cleared as “good to go and cancer-free” with 3 sets of negative mammograms and 3 sets of negative ultrasounds.

 

The smaller tumor size and negative nodes with MRI is a little more impressive than the difference between ultrasound and X-ray, now comparing 0.85cm with MRI to the 1.15cm of mammography. So, MRI has lowered the threshold of detection, which automatically hurts the other two forms of imaging when it comes to sensitivity. To the point, when sensitivity for mammography and ultrasound are now recalculated while including the MRI-detected cancers, then the sensitivity for mammography and ultrasound combined was only 44%. A 56% miss rate using clinical exam, mammography and ultrasound together three times over the course of 24 months!

 

These study results are both sobering and confusing, if those two descriptors can fit in the same sentence. As a result, the 2012 headlines surrounding this landmark study were mixed, as though the investigators interviewed were baffled by their own data, leaving the journalists bewildered, yet eager as ever to report.

 

Consequently, some headlines pronounced ACRIN 6666 a clear victory for screening ultrasound, while others proclaimed the superiority of breast MRI. But one headline was never used and never considered: “Mammograms alone are good enough.”

 

Wendie Berg, MD, PhD, breast radiologist, was the Study Chair and Principal Investigator for ACRIN 6666. In January 2014, two years after the trial results had been released, she underwent a digital mammogram with 3-D tomosynthesis, which showed Level C density (50-75%), but no cancer. Because of her family history, she decided to proceed with adjunct breast imaging, and in April 2015, she publicly announced that a 0.9cm invasive carcinoma had been discovered using breast MRI. A new activist group was formed – DENSE (Density Education National Survivors’ Effort) (6).

 

 

  1. Wolfe JN. Breast patterns as an index of risk for developing breast cancer. AJR 1976; 126:1130-1139. Dr. John Wolfe (1923-1993) was a professor of radiology at Wayne State University School of Medicine in Detroit when he published his landmark paper. His N1 pattern today would be called “predominantly fatty” (estimated lifetime risk for breast cancer – 2%), with the P-1 pattern being less than 25% prominent ducts, P-2 being greater than 25% prominent ducts, and DY (dysplastic) for dense fibro-glandular tissue. Acknowledgement for the bad pun goes to Karla Kerlikowske, MD who wrote a New England Journal of Medicine editorial in 2007, titled, “The Mammogram That Cried Wolfe.” I should have known a pun so obvious would not be original on my part, but in truth, I found Dr. Kerlikowske’s editorial in my files after I had written this chapter. So, it probably was not an original thought on my part, but instead, a subliminal repository from which I drew.
  2. While all pioneering authors on breast ultrasound screening deserve mention, space limits us to the first authors on papers that led to the Society of Breast Imaging recommendations: Paula Gordon, Thomas M. Kolb, W. Buchberger, Stuart S. Kaplan, Isabelle Leconte, Pavel Crystal, V. Corsetti, W. Berg (see #5), and Kevin Kelly. Dr. Thomas Stavros played a key role in the development of breast ultrasound, as well as many others.
  3. Hollingsworth AB, Stough RG. Breast MRI screening for high-risk patients. Semin Breast Dis 2008; 11:67-75. In this article, we gave our initial experience with MRI screening, using a point system that selected patients for auxiliary imaging equally weighted for risk and density. The scoring system also delineated the MRI interval, that is, MRI performed annually, every 2 years, or every 3 years. While this was intended for MRI, the principles are the same for ultrasound. Our preliminary findings were unsettling in that our MRI-discovered cancers were almost entirely in patients who would later prove not to qualify for MRI based on American Cancer Society guidelines introduced in 2007.
  4. Cummings SR, Tice JA, Bauer S, et al. Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk. J Natl Cancer Inst 2009; 101: 384-398.
  5. Berg WA, Zhang Z, Lehrer D, et al. for the ACRIN 6666 Investigators. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated cancer risk. JAMA 2012; 307:1394-1404.
  6. Dr. Berg joined with JoAnn Pushkin and Cindy Henke-Sarmento in forming DENSE, their educational website called DenseBreast-info.org.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Nothing Like a False Dichotomy to Promote Truth

I can’t recall the last time I read a pro-screening article in one of the major journals. It’s simply not cool. In contrast, the anti-screening militia is using every weapon imaginable in order to re-program the medical community and the American public to believe that “less is more” when it comes to the early detection of breast cancer. But no matter how fashionable it is to be an anti-screening iconoclast, or a “personalized medicine” advocate who favors less screening through miraculous prescience as to who is truly headed for breast cancer, the fact remains that less screening means more lives lost to breast cancer.

Recently, an editorial was published in JAMA (2016; 315:977-978), one of the Big Four journals, entitled “A Public Health Framework for Screening Mammography: Evidence-based vs. Politically Mandated Care,” written by a public health aficionado (Kenneth W. Lin, MD, MPH) and a lawyer (Lawrence O. Gostin, JD). The punchline seems straightforward enough – politics should play no role in medical guidelines. But there’s more than a punchline here.

The authors can’t say enough to extol the virtues of the U.S. Preventive Services Task Force and their evidence-based conclusion that we should screen with mammography every 2 years (rather than annually) starting at age 50 (rather than 40). That is, screening women in their 40s results in more harm than benefit. As I’ve stated on many occasions, evidence-based medicine can give you objective measures of benefits and harms, but that final step – weighting harms versus benefits – is 100% subjective. You can’t put “unnecessary biopsies” on one end of the teeter-totter and “lives saved” on the other end, using any semblance of science. That doesn’t stop evidence-based crusaders from doing so, however. In fact, it doesn’t seem to occur to them that the last step is totally subjective – instead, they speak of their scientific purity from beginning to end.

Authors Lin and Gostin simply gush about the high-quality evidence used in the Task Force process, then use the age-old false-dichotomy approach to point out the evils of political interference. It’s as though there’s no valid position that opposes the Task Force and its recommendations that is not politically motivated. In fact, quite a few physicians, believe the Task Force is way out of line, and it has nothing to do with politics. In fact, we can barely be civil about it (based on scientific evidence, by the way), and it has paradoxically hurt the cause of screening. Witness the total frustration of Dr. Dan Kopans, a pioneering mammography expert, who called the 2009 Task Force “idiots,” whereupon he was promptly blacklisted. Oh, you didn’t know that there are blacklists in medicine? Dr. Kopans can’t get a word published in mainstream journals anymore. He has been throttled to “preaching to the choir” in the friendly radiology journals where the readers agree that the Task Force are “idiots.”

Many of us believe that the Task Force is politically motivated to begin with, making the false dichotomy in this JAMA article even that much more bizarre – both sides are products of politics. I agree that we need to keep politics out of medicine, and a good starting place would be to dissolve the U.S. Preventive Services Task Force. Or, at least modify their structure so that they have to include members with expertise in the areas being governed. A committee of bean counters has no place telling the farmers how to grow those beans.

The epidemiologists and public health care aficionados have hijacked many areas of medicine, including breast cancer screening. They have no room for the experts. Experts are, by definition, motivated entirely by financial gain. To the Task Force, it is inconceivable that someone examined the evidence and chose a specialty based on that evidence. No, instead, doctors were once starry-eyed innocents until they picked a specialty whereupon they morphed into greedy, biased individuals who can no longer weigh evidence objectively. “You can’t have providers establishing policy!” is the battle cry of the hijackers who now control preventive medicine.

It’s not quick or simple to expose the shaky foundation of the Task Force guidelines. My favorite point of argument is that the 2009 guidelines – not screening during the decade of the 40s – was reversed from 2002 based on so-called new evidence. But the 2002 group who recommended screening in the 40s calculated a 15% relative reduction in mortality, which was completely unchanged when re-evaluated in 2009. Not a single percentage point difference. The 2009 group (the Task Force rotates members frequently) and their “new evidence” found nothing new with regard to benefit. What did change then? Well, harms. That’s right. In spite of better mammography, fewer call-backs, easier biopsies, better pathology, the Task Force used new modeling methods to calculate greater harms than had been considered in the past. So, the benefits of mammography as performed 30-40 years ago in the historical trials were balanced (subjectively) against modern and mysterious harms, such as permanent psychological damage after a benign biopsy. Benefits minimized, harms inflated. It was an easy formula.

The more you know about this travesty of common sense, the more disturbing it is. And to make matters even more disgusting, the new 3-D mammography does exactly what mammography critics have been asking for: 1) it lowers the callback rate, and 2) it raises the detection rate. What does that do for that delicate balance of harms-to-benefit ratio? What does it do to the teeter-totter? Harms are lower, benefits are greater, so you’d think this 3-D development would be welcomed with open arms.

Well, the Task Force has already spoken in 2015 – there’s not enough evidence to rule on 3-D tomosynthesis mammography. Okay, then when will there be enough evidence? Answer: never. Why? Because the Task Force considers any breast imaging modality used in screening to be “unproven” unless it has been shown to lower mortality in a prospective, randomized trial. And, since there will NEVER be prospective, randomized trials for each and every new development in breast imaging, the Task Force is sitting in the catbird seat with their “Insufficient Evidence” ranking. So, they will always be playing catch-up, rendering their opinions on obsolete technology.

Is everyone who is against the Task Force simply motivated by politics? Hardly. There is a legitimate and vehement argument against the Task Force that isn’t about emotion, politics, or gamesmanship. It’s about a deep and abiding belief – held by those physicians who actually practice breast cancer screening – that the Task Force is dead wrong, and that their influence is going to result in thousands of breast cancer deaths each year. In my abiding opposition to the Task Force, I decided to write down all my objections – the result was a 400-page book, due out in late 2016 or 2017 from McFarland Publishing (Jefferson, NC). The working title is: Mammography and Early Breast Cancer Detection: How Screening Saves Lives. If you read that book, you won’t be calling the organized opposition to the Task Force “political.” Instead, you will appreciate that it’s the Task Force that’s the “political animal in the room.”

CLINICAL TRIALS – the Good, the Bad, and the Semi-Ugly

We all do it. And, we’ll keep doing it. What is “it?” Judging from the title above, you’d think I’m talking about clinical trials for medical research, but that’s not exactly what I’m getting ready to examine. “It” is the bragging about clinical trials. Not the bragging about the number and scope of available trials where I’m as guilty as anyone. Rather, I’m talking about the new trend of boasting about a high percentage of one’s patients entering clinical trials, be it an individual physician or an institution.

Somewhere along the line, the public came to equate clinical trials with “better care,” “more advanced care,” or “cutting edge care.” And from there, it was just a hop, skip and jump to design a marketing strategy based on that misconception. So, my first point is this – a physician can provide top quality, cutting edge patient care without participating in a single clinical trial! That said, all improvements in medical care are 100% dependent on clinical trials, so it is critical that we have these studies in place. And, it is critical to enroll patients. It is so critical, in fact, that the Commission on Cancer, the organization that accredits hospital-based cancer programs, sets a mandatory minimum for enrollment in clinical trials (lest we get lazy about it).

In spite of public perception, though, clinical trials are not to be confused with “better care.” In fact, the volunteers randomized to the study group are at risk that something unforeseen could go wrong, prompting a too-late wish to have been assigned to the safer placebo group. In the earliest days of Herceptin™ research, access to the clinical trials was difficult. It helped to be living in Southern California where the drug was developed. Nevertheless, I had one newly diagnosed breast cancer patient in her 30s who wanted to be part of the clinical trials that, in the end, proved Herceptin™ to be a miracle drug for many. But it wasn’t so miraculous for my patient who traveled to California to enroll in the trial where she was given Herceptin™. She proved to be in that small group of patients where researchers discovered the potential of cardiac damage, especially when Herceptin™ was combined with Adriamycin™. She did not make it back to Oklahoma alive, instead, dying of the cardiac damage from the treatment regimen in her clinical trial. Her pioneering spirit is a great benefit to those women today where meticulous care is taken to avoid cardiac injury, and it can be argued that lives are saved accordingly. Yet, I still recall her unbridled enthusiasm to be part of a clinical trial where the experimental treatment ended her life, not the cancer.

The point is this — clinical trials are research trials, and some people are harmed in the study group, a fact often lost in the PR. “We turn more patients into guinea pigs than any other facility in the state” doesn’t quite have the same ring to it as what we hear on TV, does it?

Granted, in oncology research, terminal patients are given access to new treatments that are not otherwise available to others in the same boat. And in Phase 1 (is it safe?) and Phase 2 trials (is it effective?), a volunteer can usually receive the proposed new treatment rather than placebo. But in Phase 3 trials, where the new agent is further along in development, a volunteer is less likely to receive the experimental drug, given the mandate in late stage trials for a control group that receives the “standard of care.”

Yet, 50% of late stage trials fail, and 75% of early stage trials fail. Failure doesn’t necessarily mean “harm” was done, rather, it’s usually a simple lack of benefit. From the patient’s standpoint, that’s not a very attractive selling point. But from the researcher’s standpoint, it’s all good. Even a failed trial offers information. But what the public may not appreciate is that there are perks for the researchers, whether or not the trial is successful.

First and foremost, there’s dollars to be made. Researchers earn salary support and institutional income with each patient enrolled. Most academic centers are highly dependent on this income. The more patients who enroll, the better. Pharmaceutical-sponsored research can be very lucrative, whether the trial is successful or not. Many years ago, I experienced an encounter I call “The Graduate” moment for me, when a colleague was critical of my chosen line of research (it was non-income-producing), and told me to remember just one word – “Pharmaceuticals.” For Dustin Hoffman, it was “plastics,” but close enough. In a word, I was being told to consider switching my research focus to something else wherein a drug company could be pulled into play for the benefit of the university’s pocketbook.

Even if a researcher’s heart is not stirred by profit or job security, most scientists are happy with recognition. Thus, being the “top recruiter” to a clinical trial can bring such honors as “first author” on a paper, or a presentation at a meeting, or any of the other recognitions that define success in the academic setting.

On rare occasion, the desire for fame (with or without fortune) can be so strong that patient records are falsified in order to pump up the numbers recruited to clinical trials. One of the more famous examples occurred when, in the early 1990s, Dr. Roger Poisson at Saint-Luc Hospital in Montreal falsified data so that more women were admitted to NSABP clinical trials, most notably the B-06 trial that was the landmark study proving the equivalency of lumpectomy and mastectomy. Although falsified records were only documented in relatively few cases, his entire contribution of patients to the B-06 had to be tossed out, and all outcomes re-calculated. Because Dr. Poisson had been a top recruiter (personally adding 354 patients to the 2,163 in the trial), it threw the entire conclusion of the B-06 into doubt, prompting a nationwide panic about the safety of lumpectomy. And when NSABP head Dr. Bernard Fisher did not jump through the hoops fast enough to suit the federal government who had helped sponsor the research, he was stripped of his position by the feds. Later exonerated, the scandal may have contributed to the fact that Dr. Fisher never won the Nobel Prize (not yet anyway, he’s still alive in his 90s, while Dr. Poisson passed in 2013). Dr. Fisher has won every other award medicine has to offer, but the Nobel committee can be pretty stuffy when it comes to scandals.

But such extremes aside, there’s something semi-creepy about creating hierarchies based on enrollment in clinical trials wherein the researchers are guaranteed benefit, but the patients are not.

When an institution boasts 90% enrollment of its patients in clinical trials, hopefully it implies that the facility is participating in a large number of available trials so that there’s “something for everyone,” and that’s fine and dandy. But there’s another way to get 90% enrolled in clinical trials, even if only a few trials are open. Informed consent can be cast with an optimistic spin, such that the patient, at a highly vulnerable point in his or her life, may acquiesce to the pressure in order to please the doctor, a phenomenon well-documented. Here, the susceptible patient coupled to a convincing sales pitch does the trick.

The “clinical trial” has become so sacred that to proffer even a weak word of caution might be considered heresy. But remember, I stated at the beginning, “We all do it.” My facility brags about clinical trials just like everyone else. I suppose it’s better than bragging about nebulous qualities, like “the best doctors with the highest cure rates.” But make no mistake — boasting about clinical trials has an undeniable self-serving feature. The majority of researchers and basic scientists have their hearts in the right place, motivated independently of the perks from clinical trials. Still, there’s a used car salesman component here – you, the consumer, may or may not get a good deal, or you may even get a lemon, but you can rest assured the dealer and the dealership will not lose.

Double Trouble from the Task Force

In April 2015, we were treated to the “new” breast cancer screening guidelines from the U.S. Preventive Services Task Force. We learned back then that there would be no major changes from the 2009 Task Force, demonstrating remarkable consistency. Because the 16-member Task Force rotates its volunteers, there is a changing of the guard each time new guidelines are announced. In the past, this has translated to waffling on the screening recommendations, with each new Task Force changing what the last group had done, beginning in 1989. This time, not so much. Maybe the one physician who served as liaison to the new group made the difference.

The Big Two guidelines stayed the same: 1) Start screening at 50 (no apology rendered to the 25% of eventual breast cancer victims under age 50 who are thus excluded from screening), and 2) Screen every 2 years rather than annually.

Other controversies were settled with an “I” for “insufficient evidence.” Given that the Task Force insists on prospective, randomized trials with a proven mortality reduction before they can accept what all other rational thinkers accepted long ago, the Task Force will always be playing catch up. For instance, in 2009, the Task Force issued an “I” for digital mammography. In spite of this, essentially every breast center in the U.S. switched from the older film screen technique to digital, so in 2015, the Task Force simply deleted this technology from their list upon which to pass judgment. They were so late to the party that it would have been an embarrassment to admit that there was STILL no evidence to support digital.

They replaced the digital issue with an “I” for tomosynthesis, even after admitting the early data on tomo (3-D mammography) is exactly what critics have been demanding of mammography – better detection and fewer call-backs. That said, by the time the Task Force meets again on this issue, every breast center in the U.S. will have already replaced their 2-D units with 3-D. Tomosynthesis is the greatest single advance made in mammography technology since its introduction. The evidence is clear to those of us who don’t demand a prospective, randomized trial for each and every step we take.

As usual (since 2009 at least), the Task Force recommendations prompted a media storm, with many believing that the Task Force is the “official” policy even though few had even heard of them prior to 2009. In a way, they are “official.” The Affordable Care Act uses Task Force guidelines, so who cares if every other organization in the U.S. recommends screening earlier than 50. The Task Force “C” recommendation for (not) screening at 40-49 will mean no insurance coverage through ObamaCare. After the disastrous 2009 introduction of this “less is more” recommendation for screening 40-49, the Task Force softened the definition of “C” such that many thought they had reversed their position. They did not. The wording is kinder and gentler, but a C is a C is a C. Screening in the decade of the 40s is still a “C.” And C’s don’t count when it comes to the Affordable Care Act.

After the 2009 media brouhaha died down, we continued screening as we had before, based on the American Cancer Society guidelines, only to have that rug halfway pulled from beneath us as well when the ACS changed the starting age to 45 (covered in another blogatorial).

Then, in January 2016, it started all over. Double trouble from the Task Force. The Task Force announced their new guidelines – AGAIN – and we went through the same media storm as we’d done 9 months earlier. What had happened during this gestational period?

Pretty much nothing. As it turns out, the April 2015 announcement was only their “draft,” whereupon they invited public comment (getting more than they bargained for). Of course, this is largely for show, because they didn’t change a thing, nor would you expect them to after their expert analysis of numbers. In fact, it gave them the opportunity to field the criticisms in advance, and put their answers in writing, as part of the official 2016 guidelines. Good strategy – allow the critics to expose their best arguments ahead of time. Nothing like having the last word.

Yet, the sad truth is that the “less is more” approach may be applicable to many aspects of medicine and breast cancer in particular, but it translates to more breast cancer deaths when applied to screening. The benefit of screening, as calculated by mortality reductions, did not change for the Task Force from 2002 to 2009. What changed was a new way to calculate harms, such that the balance tipped away from screening.

Many of us believe, however, that the harms have been grossly exaggerated, while the benefits understated, and the result is going to be more breast cancer deaths. For women currently in their 30s, opting for Task Force guidelines, it has been calculated that 2,000 more women will die each year of breast cancer (currently at 40,000 per year). Drop in the bucket? Not if you’re one of the 2,000. Furthermore, women currently in their 30s are facing 50 years of breast cancer risk, on the average, so when you multiply 2,000 per year X 50 – ugh – 100,000 more breast cancer deaths over the next 50 years? Inconceivable?

Not really. The calculations were made by two prominent radiologists who have expertise in screening (RE Hendrick and MA Helvie in the February 2011 edition of the American Journal of Radiology), using the same database that the Task Force used. The “100,000 more deaths” tells us what would occur if all women were compliant with the standard “start at 40, then every year” guidelines, but then backed off according to Task Force recommendations. So, rest easy. If we look at a more practical number than 100% compliance, taking into account that many women don’t bother with mammograms at all, Drs. Hendrick and Helvie calculated “only” 64,889 more deaths over the next 50 years. Ah, that’s better. As Jimmy Fallon might say, “Thank you, Task Force, for reducing the number of call-backs, unnecessary biopsies, overtreatment of breast cancer, and for saving the system so much money…now, if we could figure out why more women are dying of breast cancer, we’d be even happier.”

Putting the Elderly Out to Pasture – at age 70

On December 17, 2015, JAMA Surgery published an opinion piece written jointly by a surgical oncologist (Dr. Ismail Jotoi) and the chief architect of the Canadian Screening Trials (Dr. Anthony B. Miller). To add gravity and authority to their cause, they invoke the timeworn admonition in their title (in Latin, of course) – Primum Non Nocere – “Above all, do no harm.” The irony is too much – if we follow their advice to quit screening at 70, then approximately 1,000 additional breast cancer deaths will occur each year, give or take a few. This is a conservative estimate, by the way, given that mammographic compliance isn’t that great to begin with. Primum Non Nocere?

This overworked “do no harm” is mostly used today when someone wants to preface their opinion that everyone should stop doing something the author believes is harmful. It’s a sure bet to strike a chord because there’s potential harm in everything we do in medicine. The phrase is attributed to the Hippocratic Oath, which actually stated, “to abstain from doing harm.” To follow this admonition literally, of course, one would have to cease practicing medicine.

So, what was Primum Non Nocere intended to mean? Originally, it stated that if you weren’t sure whether or not a therapeutic intervention would help your patient, then it’s better to do nothing than risk harm. Of course, when the Hippocratic Oath was widely adopted, doing harm was the norm. Very few medical practices of the day actually benefitted the patient, so it’s best we forget about the original meaning. Today, the phrase is meant to imply a weighing of risks vs. benefits. If the risks (harms) outweigh the benefits, “don’t do it.”

And this is where the controversy in mammography screening lies – harms vs. benefits – with anti-screening forces exaggerating harms to the point of publishing studies of permanent psychological damage after a benign biopsy. At the same time, benefits are minimized, such as the insistence on quoting mortality reductions based on “invitation to screen” rather than the benefit as measured in those who actually screen.

The authors in this opinion piece seem to have no restrictions on what they’re willing to call a “harm.” For instance, “lead time” is listed as a harm, complete with a reference. Lead time is NOT a harm, but a built-in bias.  For the sake of clarity, “lead time” is one of the primary epidemiologic biases that can be controlled through prospective, randomized trials with mortality as the endpoint. Unchecked, lead time may exaggerate the benefit of screening, but that is not a direct harm to the patient. To list it as such is a remarkable display of anti-screening bias, the irony being that it is misusing a term involving bias.

Only one of the epidemiologic biases can actually result in harm, and this is “overdiagnosis bias,” which can result in overtreatment. As for the other biases – lead time bias, length bias, selection bias, etc. – they don’t harm anyone. Furthermore, these biases have been held in check with meta-analyses of all prospective, randomized trials of mammographic screening where there is a proven mortality reduction. It is bizarre that “lead time” would be newly tagged as a harm. What’s next? Mammographic screening contributes to global warming?

Dr. Anthony Miller can be forgiven for his opinions. After all, he crossed over to the Dark Side of the Force decades ago, and can only interpret the world of screening in one way – a good clinical exam is good enough. He has spent his entire career promoting clinical exam over mammography, in spite of the fact that there’s not a shred of evidence that lives are saved through physical exam alone. His own CNBSS studies are used to denigrate mammography, even though quality in the Canadian trial was so outrageously bad that only 32% of the breast cancers diagnosed in the mammography limb were actually discovered by mammography. No wonder clinical exam looks good to Dr. Miller – his mammograms missed most of the cancers! A typical screening trial will flip-flop the numbers seen in Canada, that is, 68% of the cancers will be found on mammography while 32% will be felt on exam. One can trace Dr. Miller’s writings on the superiority of clinical exam over mammography back over 4 decades, even before he knew the results from his own trial. Absolutely nothing is going to alter his opinion.

The authors have loaded this opinion piece with their own personal bias. For instance, the claim is made that the UK Age Trial “failed to show a significant benefit from screening.” The reference for this statement is an article Dr. Jatoi wrote in 2011. Yet, in the latest 2015 update from that trial, widely accessible through Lancet Oncology (2015; 16:1123-1132), the data shows something else entirely. For background, the UK Age Trial was designed to study screening women in their 40s. (No other age groups, just 40-49, so I’m not sure why it was even included in this article about women 70 and older.) With far better design than the Canadian trial and vastly superior mammography to the Canadian trial, when Dr. Jatoi wrote his 2011 article noting “no benefit,” there was a clear trend toward benefit even though it had not yet reached statistical significance. Then, in September 2015, the final results were announced –

For the first time ever for women in their 40s, a statistically significant 25% reduction in breast cancer mortality was demonstrated for the 1st 10 years after diagnosis for those in the mammography limb. Beyond 10 years, this difference gradually declined to 12% (and barely lost its statistical significance). It was totally predictable that the anti-screeners would call the trial a failure. But what really happened? Did mammography save lives?

In fact, women in the control group (no mammography), after a decade of no screening in the Age Trial, began their routine mammographic screening as done in the UK, starting at 50. Thus, the “no mammography” became a “mammography group.” In fact, there was zero difference between the two groups from age 50 on, diluting the earlier benefit. No surprise…the screening phase of the study is over by then, and both groups are getting screened. This is a problem in long-term follow-up of all screening studies, in that controls screen anyway, and women in the screening group may stop screening. Oh, yes, another interesting feature of the AGE Trial – there was no measurable overdiagnosis, in spite of the never-ending emphasis and exaggeration of this bias.

In another controversial statement, the authors make the claim that, with improved systemic therapies, we can back off on screening – “with modern adjuvant systemic therapy, a less sensitive screening modality such as CBE (clinical breast exam) may now prove as effective as mammography.” It is true that when that day comes in the future when systemic therapy cures all breast cancers, we will no longer need screening. But it is disastrously premature to state that this day has come already, or even that we are easing into an era where we can back off on screening. The 40,000 women who died of breast cancer this year would not be nearly as enthusiastic about those systemic therapies as these authors seem to be.

I don’t have time or space here to go over the Harvard study that showed, overwhelmingly, that most breast cancer deaths occur in unscreened women. Systemic therapies and screening are complementary, and until that 40,000 number is dramatically reduced, we cannot back off on either screening or systemic therapies. In fact, we need to be doing more in both categories. One does not preclude the other, nor do they need to operate in an inverse proportion. I could easily claim that if we screened all women with breast MRI, we could back away from systemic therapies. And though I could defend that position to a degree, it would be every bit as misleading as the inverse, claiming that systemic therapies allow us to back off on screening.

Since there is very little data from the prospective, randomized trials on the mortality reduction for screening women in their 70s, what would rational thought tell us? If we divide screening into the decades, the benefit of mammography gains momentum over time. It takes fewer and fewer screens to save a life as women age. Screening in the 50s is more effective than the 40s, while screening in the 60s is better than the 50s (by quite a bit). Would any rational person on the planet truly believe that this accelerating benefit would come to an abrupt stop at age 70? In fact, barring co-morbidities, it is likely that the effectiveness is every bit as good, if not better, than for women in their 60s. Yet, we’re told Primum Non Nocere, and to drop mammography at its peak performance level; instead, adopt clinical exam for which there is no evidence anywhere that lives can be saved with exam alone.

A full 25% of eventual breast cancer victims are over the age 70. We’re already being told by some to stop screening under age 50, which is another 25%. So, if the anti-screeners get their way, screening only between 50 and 70, we will be excluding 50% of the eventual breast cancer victims from the potential of early detection. Compare this to the current 5% of eventual victims excluded by virtue of being under age 40 at the time of diagnosis. I submit that the leap from 5% to 50% will be deadly for many women, several thousand a year, whereupon the term Primum Non Nocere takes on a new twist.

It only takes a few pages of the written word to sling mud. In contrast, there are so many misunderstandings, false claims, erroneous misrepresentations of available data, and flat-out errors, that it takes a book to wipe the mud away. That’s why I wrote one – the working title is
Breast Cancer Early Detection: Past, Present, Prologue (McFarland & Company, Inc., Publishers) with release estimated late 2016.

Have No Fear: George Orwell is Here

One of the bureaucratic divisions in Orwell’s classic 1984 is the Ministry of Truth which, of course, is anything but. Instead, it drowns the public in propaganda.

Today, we’re experiencing an anti-screening campaign of Orwellian proportions. And it’s being led by physicians, no less, who claim to be rising above emotion, and dealing only in scientific truth. Indeed, they have appointed themselves as the Ministry of Truth.

They are supported in their efforts by well-meaning journalists who, of course, have no interest in “dog bites man.” “Mammography saves lives” is stale and boring. But pull a switcheroo, and make that “mammography doesn’t save lives,” and you’ve got yourself a story.

Witness the Oct. 12, 2015 issue of TIME magazine where the cover states: “What If I Decide To Just Do Nothing?” The article itself brings up many valid and controversial points, but let me direct you to the sidebars, which I offer here verbatim: 1 in 800 – The chances of a woman in the U.S. getting diagnosed with invasive breast cancer. Or, 1 in 4,566 – The chances that a woman in the U.S. will die from breast cancer.

Don’t you feel better now? After all, you’ve been frightened out of your wits for many years with scare tactics that claimed, “1 in 8 women will be diagnosed with breast cancer.” Isn’t it refreshing to know the number is a mere “1 in 800?”

The cardinal rule in quoting risks happens to be: don’t ever toss a risk into the ring unless you attach it to “time.” Risks expressed as percentages without a time frame are, by definition, reckless and misleading. Critics of breast cancer screening love to point out how we’ve been terrifying the public for years with overstated risks. “One in 8” is the risk of developing breast cancer over a lifetime calculated through age 85 or 90, yet this has not always been made clear. So, to undo the damage, we apparently have a respectable news magazine doing the opposite – understating risk without proper explanation as to length of time.

How does “1 in 800” make sense? In fact, it is true if you’re talking about the risk of being diagnosed with breast cancer over the course of one year. Even then, “1 in 800” is under-kill, as this would only apply to a woman in her 40s. So, here’s the truth: There’s a “1 in 800” chance of being diagnosed with breast cancer during the next 12 months, if you’re a woman in her 40s. TIME’s sidebar is a shameful misrepresentation of risk. The same is true for their “1 in 4,566,” which is only accurate if talking about one year. In fact, the “lifetime risk” for dying of breast cancer is “1 in 35.”

Today’s counter-mantra seems clear: We are going to undo the sins of the past, even if it means misleading patients in the opposite direction. After all, “1 in 8” was also heavily promoted without specifying the duration of time to which this applied. So, we’re simply getting even.

The difference, however, is that lives will be lost by listening to the new Ministry of Truth.

New ACS Screening Guidelines – Are we falling into line…or for a line?

For many years, we have been relying on the American Cancer Society (ACS) to uphold the familiar screening mammography guidelines – that is, “annual mammograms starting at age 40, and continue as long as you are healthy,” updated in 2003, but no real change since 1997. From 1983 to 1997, the 40-49 recommendation had been “every 1-2 years.”

 

The original recommendation from the ACS, by the way, was in 1976, when data were really slim – back then, it was “annual mammograms for women after 50, but for women in their 40s, there should be a family history of breast cancer in a first-degree relative.” The “baseline mammogram at 35-39” was adopted in 1980, but dropped in 1992.

 

In 2009, the U.S. Preventive Services Task Force recommended major cutbacks in screening – start at 50, and every 2 years thereafter. This launched a new battle between pro-screeners vs. screening minimalists. Comparable to the Mammography Civil War in the 1990s, neither side was willing to budge.

 

On October 20, 2015, the American Cancer Society caved, ostensibly at least, to the anti-screening tsunami, announcing its new guidelines in JAMA – in brief, “begin at age 45, screen annually to 54, then switch to every 2 years (biennial).”

 

At least that’s the “all meat no potatoes” version of the new guidelines. Cleverly embedded in the new guidelines are the old guidelines, but you probably won’t hear about that. In fact, the ACS stated that women should still have the option to begin at 40, and that they should have the option to continue annually. These options are not buried in fine print – no, in fact, they are in the Abstract along with the same level of “Qualified Recommendation” as all but one of the other guidelines. The only recommendation listed as “Strong” was the 45 starting age. Switching to biennial screening was “Qualified”… the recommendation to screen as long as overall health is good and life expectancy of 10 or more years was “Qualified”… and no need for clinical exam in women without risk factors was “Qualified.” The old recommendations are listed right there as “Qualified” with all the others, but the old “option” was suppressed within minutes of the ACS announcement. And since most rely on the media for their medical updates, well, precious few will be able to quote the true ACS guidelines.

 

Why will the old guidelines, now “optional” be suppressed? Because the War on Cancer is coming to an end. Today, there is a powerful contingency that has declared War on the Harms Associated with Cancer Detection and Treatment, no matter how many lives may be lost in the process. The politically correct focus is on doing no harm, an impossibility in medicine, by the way. More accurately, it’s about harm:benefit ratios, a completely subjective judgment call, balancing apples against oranges. But the screening minimalists do everything they can to attach numbers and justifications for their position. In an accompanying Editorial to the new guidelines, Drs. Keating and Pace, who are deeply engaged in informing patients of the harms of screening, don’t even acknowledge the fact that the old guidelines are left as an “option” for women who want to continue what they have done for many years. Instead, they dwell on the harms, their favorite topic, as evidenced by their “informed consent” which results in a statistically significant “improvement” in women opting to do less screening.

 

The American Cancer Society, for the first time ever, broke the data down into 5-year increments, rather than the usual 10-year approach. Highly sophisticated analyses, transparency and independent review, all the wonderful things that go into formulating guidelines are there, like never before. All of this was in line with the 2011 Institute of Medicine guidelines on how to create guidelines. And to help themselves, the ACS drew from several other guideline resources including the “GRADE” terminology to distinguish between “strong” and “qualified” recommendations. Guidelines on how to create Guidelines, then Guidelines on how to communicate these Guidelines to the People intended to benefit – this is “evidence-based medicine” in all its glory. But when it comes to the end – harms vs. benefits – it’s a total judgment call whether to screen or not, at what ages, and at what intervals.

 

On the bright side, the ACS included observational studies, something that makes purists like the Task Force sick to their stomachs. Benefits are greater in these studies because (pick one): 1) women actually get mammograms here, rather than be “invited to screen,” plus those mammograms are performed using modern technology, or 2) the extra benefit is due to lead time bias, length time bias, overdiagnosis, and selection bias.

 

In contrast to the Task Force that didn’t utilize expert opinion, the ACS asked 22 experts and 26 relevant organizations to review the guidelines in advance. When it was all said and done, with the world literature review performed by the Duke University Evidence Synthesis Group (a separate article in JAMA), the ACS recommendations appear to be bullet-proof.

 

In reviewing how the ACS arrived at age 45, they addressed the issue from multiple angles, pointing out that incidence and mortality from breast cancer during ages 45-49 are actually closer to the 50-54 group than to the 40-44 group (barely). Frankly, the whole thing smacks of a predetermined compromise, thus the early decision to use 5-year blocks of time. “Let’s shoot for peace and harmony here. We must stop the madness that prevails today in conflicting recommendations. We’ll address the problem in 5-year intervals, allowing us to justify the compromise age of 45. The rest doesn’t matter so much, since the greatest controversy is the starting age.” Or, so I imagine.

 

The lead author for the ACS, Dr. Kevin Oeffinger, claims that this is a great victory for personalized screening – “The evidence simply no longer supports one-size-fits-all,” he is quoted as saying in TIME magazine. A terribly proud moment, considering only about 1,000 women will die each year if these guidelines are followed. And what a great month to make the announcement – Breast Cancer Awareness Month. When your 44-year-old friend is diagnosed with Stage IIB breast cancer, by self-exam (since the ACS no longer endorses clinical exam), you have now been made aware that you can tell her: “Oh, but think of all the heartache of unnecessary biopsies you’ve spared other women!”

 

We already disenfranchise 5% of eventual breast cancer victims by offering nothing to women under 40 to help with early detection, unless they are at very high risk (and most who develop breast cancer are not at high risk). By moving to 45, another 7% will be disenfranchised, bringing our total to 12%. And if the Task Force were to get their way, starting at 50, the total number of eventual breast cancer victims left out in the cold without screening will be an astounding 23%.

 

Dr. Stephen Feig, pioneering breast radiologist, has warned that “personalized screening” is a euphemism for “restricted screening.” Where “personalized screening” can be beneficial is when risk factors and breast density become the foundation to do “something more.” To use it to “do less” is deceptive. Call it what it is – limited and restrictive screening – there’s nothing beneficial in it for the average risk patient, which by the way, is the population where most breast cancers occur.

 

Admittedly, the ACS took a leadership role in 2007 when they introduced high-risk screening guidelines using breast MRI, starting at age 30. This is “personalized screening” in the good sense. They will be releasing new guidelines for these women in the near future. My hope is that they don’t “personalize” high-risk screening to the point that it’s pointless.

 

As a final point, we don’t need “more screening” as much as we need “better screening,” and this is happening already. 3-D tomosynthesis mammography is in the first stage of implementation, and the numbers coming in are remarkably good, with improvements in both Sensitivity and Specificity (fewer false-negatives and fewer false-positives). So, the recommended “informed consent” as proposed by screening minimalists are already “misinformed consents.” And, with the addition of whole breast ultrasound and breast MRI applied to a broader population, mortality reductions will be far superior compared to what is accomplished today. Too bad that the anti-screeners gained so much momentum at precisely the point in time when screening is getting an upgrade, or perhaps being revolutionized.

 

One thing for sure, not a single additional life will be saved by switching to the new American Cancer Society policy of “personalized screening,” which is designed to spare women anxiety from false-positives and theoretical “overdiagnoses.” Oh yes, and to save money. We’ve finally “fallen into line” with other countries around the world that have committed to screening mammography. And what a line it is!

 

 

Informed or Misinformed Consent?

In the September 8, 2015 edition of JAMA, an editorial was offered by Harald Schmidt, PhD, MA, calling into question “incentives” for mammographic screening. Writing from the Center for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Dr. Schmidt raises an interesting question – should we be giving away free T-shirts or movie tickets to reward patients for having their screening mammograms?

 

I share some discomfort about certain promotional activities. Over the years, I have been repeatedly approached to offer a “free mammogram” at health fairs and silent auctions for fund-raising events. Others in the community were doing it, so “why don’t you?” Well, a mammogram can generate a false-positive, with a requisite biopsy that can backfire if a borderline lesion is found that ends up being called “cancer,” and the next thing you know, bilateral mastectomy or radiation therapy is being performed. That’s why. I’ve never been comfortable with such flippancy when it comes to a screening mammogram. It is a two-edged sword.

 

But Dr. Schmidt takes his position further – calling into question media-awareness campaigns, e-mail or phone call reminders, and the concept of default mammogram orders with electronic medical records. In his argument, Dr. Schmidt endorses the recommendations of the U.S. Preventive Services Task Force to the degree of, oh, roughly 100%. And that’s where we part.

 

Anti-screeners are taking great delight in performing studies that show how women – when “truly informed” about the risks versus the limited benefits of mammography – will opt for less screening, or no screening at all. But what they call a “thorough informed consent,” I call baloney. The benefits of mammography in these consents are based on a technology that doesn’t even exist anymore. ALL the prospective, randomized controlled trials that the Task Force holds in such dear esteem were accomplished with film screen mammography during an era prior to quality assurance. Only the U.K.’s AGE trial made it into the 1990s with active screening. The other 8 prospective RCTs took place starting in the 1960s, peaking in the 70s and 80s. We are two generations past that now, and the only thing the Task Force can do is watch the tail end of progress pass them by as they pronounce “Insufficient Evidence” (as they recently did for 3-D tomosynthesis mammography). Because of their insistence on prospective RCTs (excluding all other evidence), no matter how absurd or antiquated the study, they were never able to endorse digital mammography. The technology came and went (or is leaving, at least, being replaced with 3-D tomosynthesis) while they awaited results from ethereal prospective RCTs that are never going to occur.

 

After grossly minimizing the benefits of screening, often based on “invitation to screen” rather than actual screening, the harms are greatly exaggerated (including lifetime psychological damage from a benign biopsy).   It is demoralizing to read the informed consents that are being laid on unsuspecting women where benefits are minimized and harms maximized. Dr. Schmidt emphasizes the very important need to make women aware of “overdiagnosis” with mammography. Fine. What number should we use, since the published range is from 0 to 50% in a debate that is highly contentious? If overdiagnosis is occurring at the same rate we see in prostate cancer, then explain how, at autopsy, over 50% of men will have occult prostate cancer undiagnosed and subclinical, while only 1% of women will die with an undiagnosed invasive breast cancer.

 

Grossly exaggerated overdiagnosis rates are just one example of how women are being frightened out of mammography. These are not informed consents – they are propaganda. Not a single life will be saved by this social tsunami, but you can readily calculate the number of deaths as women choose to back away from screening after their “misinformed consents.” 2,000 more breast cancer deaths per year is a fairly accurate number that will result if Task Force guidelines are followed rather than American Cancer Society (and all other professional societies that address breast cancer).

 

Yes, we have a resource allocation problem, and this is why some clinicians are drawn to the concept behind (or membership on) the Task Force. It’s hard to argue with the basic philosophy that the U.S. has been throwing money at diseases for many years without a proven advantage in many instances. Task Force members and their ilk, while claiming to be unbiased judges of quality care, are not immune from human bias. In this case, their bias is such that when it comes to spending money for preventive health measures, “We have to put a lid on it.”

 

The challenge is making sure that the Task Force is not talking about the lid to your coffin.

 

 

Publish or Perish Has Morphed Into the H-Index

If you saw the movie Moneyball and the impact of mega-statistics on major league baseball, then you won’t be surprised that Big Data (and Little Data) has invaded academic medicine as well, not to mention science in general. No longer is that iffy word “judgment” to be utilized to assess competence as distinct from brilliance. Today, it’s done with numbers.

Once, a researcher could get away with racking up large numbers of publications, using all sorts of tricks. The goal was simple, that is, get as many papers in print as possible. But someone caught on that quality ought to count, too.

Now, we have the h-index, another approach to converting performance into numbers that can be used for promotion, to award grants, or simply for bragging rights.

The h-index is an attempt to reward not only the number of published papers, but also how often those papers are cited by others. (Did your mother ever tell you not to base your self-worth on others? Well, if you are a scientist, your mother was wrong.)

In its attempt to include both quality and quantity, the h-index reflects both the number of publications and the number of citations per publication. For example, an h-index of 10 means that among all publications by one author, 10 of these publications have received at least 10 citations each. The h-index has little value when used across different fields. That is, it works only when comparing scientists in the same field. At least, that’s the way it was intended.

Of course, over the past 10 years since its introduction, it has been studied to the hilt, with statistics generated about the statistics, and at least some evidence that the h-index doesn’t help one twit, that is, that the old-fashioned method of counting papers worked just as well. But for some, it has become a goal unto itself. It certainly has become a science unto itself, with mathematical variations and versions out the wazoo.

The reason I bother to mention it on this web site is the fact that one way a researcher can pump their h-index number is through self-citation. Every time you publish a paper, you quote yourself to no end (my favorite variation here is the innate ability of some to not only quote themselves, but also to do so incorrectly, gradually building a case out of thin air).

Alternatively, you can promote your work directly to the media through various options, create a buzz about your work, then sit back and wait for others to cite your work, given its importance now confirmed.

Another way to pump numbers is to become embroiled in a controversy. This may explain why some experts enjoy generating controversial theories, sometimes oddball theories, just to get a reaction. For our purposes here, nothing will get you cited more frequently than announcing, “Screening mammography does not save lives.”

Then, of course, there are the inevitable rankings. You knew it had to happen. Is my number bigger than yours? Nobel laureate chemist Harry Kroto ranks a lowly 264th on the h-index for chemists, so he’s not too hot on the old h-index idea. Is this the route we really want to go? Is this how promotions and grants should be decided?

In the end, I think our mothers were right. It’s best not to worry about what others think about you. But still, you have to wonder if physicist Jorge E. Hirsch’s mother tried to teach him that lesson. Dr. Hirsch is the physicist who, in 2005, introduced the h-index   (h = Hirsch). Today, several software programs track your number, re-calculating on a regular basis as citations appear in the scientific literature. Appropriately enough, one of the programs is called Publish or Perish.

Will $2 million Revolutionize Breast Cancer Screening?

The breast cancer screening “industry” in the U.S. is sometimes pinned to a 6-8 billion dollar figure that is supposed to reflect high cost and low gain. That is, few lives saved for such an impressive price tag. And when compared to $6 billion, $2 million seems piddly. But it’s $2 million that has been awarded by the National Cancer Institute in a R-01 grant to my collaborators (“inventors”) and me to see if we can revolutionize how we screen for breast cancer.

First, some background. Here sits a breast MRI machine, less than 20 feet from my office. I have to consider, every day, that if we were able to screen the entire population with this device, very few women would ever die of breast cancer. So, while everyone waits for a “breakthrough” to radically alter the treatment of breast cancer, I’m looking at that breakthrough in the past tense. It’s already been done. Yet, many experts either ignore it or criticize it.

And for sure, it’s not the long term answer – the final answer will come when we are able to cure or control metastatic breast cancer 100% of the time. Or, alternatively, when we can immunize against cancer, so that the disease becomes an historical relic. Then, we won’t need to screen. Early diagnosis won’t be required. But that day is not yet on the horizon. For the next 50 years, at least, early diagnosis through screening will be important. And, we could do so much better than the status quo with the technology currently at our disposal. What a shame that it is nearly impossible to use. Strict guidelines and stricter insurance companies make it difficult to identify patients who qualify for MRI screening.

Currently, women qualify only when they are at very high risk for breast cancer. Sounds both obvious and justifiable, but in fact, it’s an inefficient approach piggy-backed onto inefficient baseline screening. First of all, it excludes the vast majority of women who are headed toward breast cancer, the 80% who do not have a family history. Then, under current guidelines, women qualify on the basis of “lifetime risk,” an unwieldy number that declines as you age, given that you are “passing through” your risk. So, young women with risks qualify more easily, but as time goes on, when individuals actually enter the danger zone where incidence peaks, their remaining lifetime risk may not allow them to be screened with MRI.

Risk-based screening is on the tip of everyone’s tongue, but for me, it leaves a bad taste even though my area of expertise is breast cancer risk assessment. Why? Because the difference in cancer yields between very high-risk women vs. normal-risk women is not great enough to warrant completely separate approaches. If you screen 100 women with MRI who have already had a normal mammogram, you will find 1 cancer in a population of “normal risk” women. If you screen 100 women at the very highest level of risk, e.g., BRCA-positive women, you will find 3 cancers. All that work to find 3 cancers instead of one.

My point is this: research should focus on how to identify women that have mammographically-occult breast cancer on the day of the negative mammogram, NOT using the surrogate of breast cancer risk spread out over a lifetime. For 20 years, my only idea to make this happen has been through a low cost screening blood test that would tell you to proceed with MRI if mammograms were negative. And, I am recently encouraged by new developments in this area, and will be writing about it more in the future.

But I’ve always been haunted by another fact, well-known to all breast radiologists, but seldom discussed. Often, when you diagnose breast cancer, you can look back one year earlier and see a subtle change in the density level in the area where cancer has recently been diagnosed. In fact, so many attorneys were taking advantage of this, and successfully swaying juries into rendering guilty verdicts against radiologists, that a group of experts wrote an article on the topic, admitting that 58% of the time, you can detect “something” happening in a zone where cancer will be diagnosed 1-2 years later. Yet, these changes are too minor to hold the radiologist accountable. If breast radiologists called back everyone with such subtle changes for diagnostic work-ups, then they would be calling back the majority of patients being screened.

The article was designed to address the absurd standard to which radiologists were being held by the courts, not simple perfection, but prescience beyond perfection. But when I first read the article, my “take home” message was entirely different. What if those women had undergone breast MRI? I suspect nearly 100% would have been positive for cancer. But again, you can’t do MRI on all those with such subtle changes. Or can you?

One year ago, a publication caught my eye in The Breast Journal, where an accomplished computer scientist (Bin Zheng, PhD) and his mentor (Hong Liu, PhD) had been working on image analysis through computers in the detection of subtle differences in the comparison of one breast to the other, and over time. Using their invention, they reported being able to find women at nearly 10-fold short-term risk for breast cancer based on mammographic density changes. This is not CAD (computer-assisted diagnosis), already in current use where specific lesions are identified by the computer. This is a “second line” computer analysis, an “ultra-CAD” if you must, identifying changes after routine CAD has “signed off” on normal mammograms.

It was a brilliant approach, and my mind raced back to the 58% who have subtle changes in the year(s) prior to diagnosis. This “ultraCAD” would serve the same purpose as a screening blood test, that is, in the efficient selection of patients for MRI, not based on future long-term risk, but based on the high probability of a current malignancy missed by mammography.

And to my surprise, these computer scientists were working out of an Advanced Cancer Imaging Lab located only 30 miles away at my alma mater, the University of Oklahoma, Norman campus, only a short walk to the basketball arena and a short jog to the football field. As always, basic scientists need clinical collaborators in order to lift their inventions from laboratory into actual practice. I contacted Dr. Zheng, and we ran a quick pilot study that included 30 patients with normal mammograms, but 5 of whom actually had cancer discovered on MRI. Dr. Zheng’s system identified 9 of 30 as “very high short-term risk,” and all 5 of the cancers were included in the 9. Had we used his system to select patients for MRI in the first place, we would have only performed 9 MRIs instead of 30 to find the cancers. This is efficiency. I won’t take up a lot of space here, describing what this means in terms of cancer yields on MRI, but in summary, if it works, 1) the cancer yields will dwarf anything ever accomplished through risk stratification, and 2) it will open up MRI screening to all women, not just the minority who have risk factors.

The National Cancer Institute seemed to agree that we may be onto to something. In July, 2015, the NCI awarded us over $2 million for a 5-year study that will involve approximately 10,000 breast images. In keeping with NIH policy, “Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R-01CA197150. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.”

Our grant title is: “Increasing Cancer Detection Yield Using Breast MRI Screening Modality.” Breast MRI can detect more than 90% of breast cancers at an early stage, twice the number detected by mammography in head-to-head comparisons. While molecular biologists whittle away at the secrets locked inside the cancer cell, going for the eventual cure, it’s well past time to take full advantage of the miracle of multi-modality imaging, something invented in the past, yet presently offered only to a select few.