Don’t fret. I had to look it up, too. Polysemy is the association of one word with two or more distinct meanings. Monosemy is a one-to-one match between a word and a meaning, and is found more often in specialized vocabularies dealing with science and technology. That said, we have plenty of polysemy to pass around in science, and it can overpower anything that so-called precision medicine has to offer.

So how did I stumble on this word-of-the-month – polysemy? In May 2017, I was referenced in the Journal of Volcanology and Geothermal Research. I’m not kidding. In my fiery young days as a trauma surgeon in the crater of Los Angeles, I might have been accused of having explosive moments, but Volcanology? Really?   I thought it was a mistake when I received the online notice that I’d been quoted. But curiosity won out, and when I clicked on the beckoning prompt, I discovered that this geothermal Alan Hollingsworth and I were one and the same.

And there it was — polysemy — scattered throughout the article as if it rolled from our tongues on a daily basis.

It seems that volcanologists are struggling with the exact meaning of a particular word – overpressure – which, from my position of volcanic ignorance, seems fairly important to nail down.  Yet, to quote from the article, “This (polysemy) is likely to be a particular problem in analysis of geothermal resources, where reservoir engineers, volcanologists and structural geologists may each confidently use overpressure but mean different things.”

So, of course these authors (from Norway, U.K. & Italy) went straight to PubMed and searched “breast cancer” to find support for their statement regarding the problem of precise definitions. Probably not. I have no idea how my editorial in the The Breast Journal caught their attention. Nonetheless, in 2015, my article dealt with – who knew? – polysemy. I was still 2 years away from hearing that word for the first time, but my title was, “The Beginning of Wisdom is the Definition of Terms,” a phrase I stole from Socrates who, by golly, was smart enough to avoid writing anything down. Socrates was openly opposed to literacy, believing that words short-circuited the learning process, forcing an illusion of knowledge while compressing big concepts into small containers (at least we think this is what he believed – after all, he wrote nothing).

My editorial intent was to discuss how clumsy definitions pollute even the most sophisticated statistical analyses. If we’re all talking about different things when we analyze “multicentricity,” for instance, we’re simply looking for trouble. Or, what about “local recurrence?” Do we mean breast parenchymal recurrence, chest wall recurrence after mastectomy (solitary vs. diffuse), or regional node recurrence? Precise definitions are lagging way behind precision medicine. Yet, as Socrates (allegedly) said, “The beginning of wisdom…” Well, you get the point.

Here’s another polysemic term – “breast cancer.” I’ve been on a campaign lately trying to undo the confusion about “breast cancer” in the historical autopsy studies. The overdiagnosis crowd is having a field day as breast cancer experts shoot themselves in their feet with misrepresentation of this very important data. These anti-screening epidemiologists and their ilk don’t have to lift a finger when clinical experts announce from the podium: “Breast cancer can be found in autopsy series 30% of the time, similar to prostate and thyroid cancer.”

If we define “breast cancer” to include DCIS then, yes, 30% can be found in the literature, although this is at the high end of a wide range (and appears to include some cases of ADH and borderline lesions). But let’s be sticklers for accuracy because we’re not talking about the usual DCIS:invasive ratio in autopsy series that is seen clinically or through screening. The autopsy findings are almost entirely DCIS. Most everyone agrees that there is an element of overdiagnosis in DCIS, the degree of which is under study.

But in contrast, the bitter controversy surrounds the degree of overdiagnosis with invasive breast cancer, and one of the most important bits of information to support or deny overdiagnosis is the disease reservoir in autopsy studies. When the term “breast cancer” is applied to invasive disease only, the autopsy numbers plummet to 1% (range 0 to 1.8%). This is the same as disease prevalence in the living, thus implying an overdiagnosis rate that hovers somewhere around zero. Under the true definition of an overdiagnosed cancer (one that never progresses) there are only two fates for these so-called “pseudo-cancerous” growths – quiescence or regression. The autopsy data, mostly from the pre-mammographic era, effectively rules out quiescence when it comes to invasive cancer. This forces the screening critics to focus on proving that tumor regression is real, in spite of the complete absence of a clinical correlate.

When anti-screening propaganda that draws on the autopsy data is tossed into the public arena, I submit that the author(s) use “breast cancer” in the broadest sense possible to stay within the bounds of legitimacy (and I’ll make the case next month that these same individuals would be the first to state that “DCIS is not really cancer” in another setting). How convenient it is to make DCIS “cancer” whenever it supports your case, then deny that that DCIS is cancer when it hurts your case. I can’t do anything about this contingency that uses sly semantics, given that their statements can be defended merely by capitalizing on polysemy. But what disturbs me is when pro-screening breast cancer experts unwittingly regurgitate the same misleading information.

This 1% rate of invasive breast cancer at autopsy is not even in the same ballpark as prostate cancer, the latter probability approximating age at the time of death. An 80 year-old male has an 80% chance of occult prostate cancer at death. A female, however, has only a 1% chance of invasive breast cancer at autopsy. How can we conceivably call these numbers “similar?” Yet, that claim seems to be the norm today thanks to polysemy.

When I was a surgery resident in the 1970s, it was common to describe nearly all breast cancer patients as having “infiltrating intraductal carcinoma.” And this was long before the word “oxymoron” spread through the country like a contagion. Our awareness of whether we were talking about in situ disease or invasive disease was basically zilch (same treatment for everyone), so the phrase flourished. Of course, mammography had not yet brought DCIS into the limelight, but it’s still a good example of semantics gone awry.

I’m going to save Part Two (with specifics) on this topic for next month, but the fact is that when we are discussing overdiagnosis, or disease reservoirs, it is absolutely mandatory to make clear if we’re talking about DCIS, invasive disease or a combination of the two. Otherwise, we might as well go back to using “infiltrating intraductal carcinoma.”

(to be continued August 2017)

 

 

 

 

The American Society of Breast Surgeons announced a new policy statement at its annual meeting in April 2017, regarding the use of breast MRI pre-operatively for women newly diagnosed with breast cancer. Unlike most guidelines that describe what physicians ought to do, this one is quite the opposite – what NOT to do. That is, don’t routinely perform breast MRI pre-operatively to map tumor extent.

 

The operative word here is “routinely,” with the implication being that surgeons are free to order MRI in selected cases, thus avoiding the more oppressive guideline that has been suggested by some – don’t use MRI at all. This new policy statement sounds perfectly reasonable at first glance. But at second glance, we’re facing an unusual problem – no one has clearly demonstrated specific subsets of patients where MRI yields are higher, that is, more easily justified. We all believe we know where MRI is most likely to help, but so far, attempts to demonstrate these subsets – based on patient age, histology, breast density, etc. – have not shown sharp distinctions in MRI yields or outcomes. It is very difficult to predict in advance when MRI is going to be of benefit.

 

Once upon a time, in a faraway medical world, there was an ethereal entity called a “Center of Excellence.” Physicians aspiring to this status would band together and outline a strategy of superior performance, publish results, and then allow the medical community to analyze these outcomes and adopt the protocols if true excellence was demonstrated. As a result, quality of care improved accordingly. Today, this approach has been replaced with Guideline Medicine (allegedly evidence-based medicine – however, with enormous gaps in our facts, guidelines are often a matter of opinion). There is a subtle distinction between these two approaches. Guideline medicine tends to bring the Average Joe up to a basic standard. As for rising above that standard…not so much. Instead, guidelines have the potential to fall into the trap of the “tall poppy syndrome,” that is, cutting the heads off more vigorous poppies to make sure that all are the same height.

 

One other oddity about Guideline Medicine is unique to certain disciplines, especially radiology and surgery – one can have vastly different outcomes in studies due to variations in interpretive skills and surgical technique. Prospective, randomized trials work wonderfully when evaluating a new drug because the manufacturing process of that drug is standardized, and the participants (patient, physician and sponsor) are blinded. Not so for radiology and surgery. Imagine what clinical trials would be like for new chemotherapeutic agents if each participating hospital were responsible for manufacturing their own drug, and if all participants were unblinded.

 

My point is that the words “prospective, randomized trial” in surgery and radiology do not have the depth of validity as when studying a drug in double or triple-blinded prospective RCTs. This is especially true when it comes to the technology behind MRI and the very difficult interpretive demands. After the introduction of breast MRI to the clinical sphere, it took no time for breast MRI experts to warn us all: “Bad MRI is worse than no MRI at all when it comes to the breast.” This warning went largely unheeded, and the introduction of breast MRI was every bit as chaotic as the introduction of mammography (which prompted government intervention and FDA monitoring that has been in place now for nearly 25 years).

 

In the early days of breast MRI, experts routinely refused to accept outside studies, and any patient referred for biopsy was re-evaluated with another MRI where biopsy was often not indicated. At our facility with breast-dedicated MRI (Aurora Imaging), these repeat MRIs were often normal. Yet, during this era (which is not completely behind us), the surgical literature was flooded with outcomes – both good and bad – based on substandard MRI. Some of these oft-cited studies include patients who underwent research MRI 25 years ago, a full decade before its clinical introduction.

 

Back to the recent meeting of the ASBrS. Through coincidence, I happened to be speaking on the topic of pre-op MRI at this same meeting where the new MRI policy was announced. My talk was delivered in one of the pre-meeting courses devoted to Advanced Imaging. In my presentation, I showed 5 examples of FAKE NEWS and ALTERNATIVE FACTS about pre-op breast MRI. Time constraints, however, limited me to a discussion of only 2 of the 5. As occurs whenever I give this talk, attendees tell me they are surprised by what these “landmark” studies actually reveal, or fail to reveal. Other than the obvious problems of terrible interpretations or “research” MRI in its early years (or unilateral MRI performed in one study often quoted for its mysterious lack of contralateral benefit), listeners to my analyses usually agree that these studies are nothing more than a string of caveats held together by gaping holes.

 

That doesn’t mean I have unequivocal proof of outcome benefit with pre-op MRI. It only means that the criticism of pre-op MRI from commonly quoted available studies is severely limited, and you can’t overcome this with the revered “meta-analysis,’ which is merely an example of “garbage in – garbage out.”

 

Nevertheless, two days after my presentation, I heard the justification for the society’s new policy on MRI. The 2 studies quoted were the very 2 I had analyzed in my FAKE NEWS talk days earlier, but with the usual lack of in-depth analysis. We were told that pre-op MRI “causes more mastectomies, doesn’t alter re-excision rates, delays patient care, etc., etc.” Sadly, this is true if one takes certain anti-MRI literature at face value, ignoring those of us who have published in-depth critiques of these studies, while at the same time offering our different outcomes at “centers of excellence,” that today are being converted to “level poppy fields.” At my facility in OKC, there is no delay at all with pre-op MRI, there is no increase in mastectomies and, improbably, the conservation rate is even higher after false-positive findings on MRI!

 

Our surprising (and counter-intuitive) results come from the fact that all newly diagnosed patients are told by the radiologist and the nurse navigator that the purpose of the MRI is to help confirm candidacy for conservation. This simple introduction is a game-changer. Because the breast-dedicated MRI is located in the breast center (and not used for any other purpose), we are able to perform the MRI within days of diagnosis when the patient’s head is still spinning, and therapeutic options have not even been discussed yet. Post MRI work-up is completed quickly and before presentation at interdisciplinary conference. This approach is contrasted to the patient who is presented at a conference without MRI, and then the surgeon decides later if she needs an MRI, whereupon the study’s pivotal role is greatly magnified. Here, false-positives cause anxiety which, in turn, result in mastectomy.

 

But using our approach of “up front” MRI, the false-positives are handled so quickly that the opposite is true – the patient who was leaning toward mastectomy sees how much effort went in to proving conservation safety, and she actually switches her choice from mastectomy to conservation. As we published nearly 10 years ago (Am J Surg 2008; 196:389-397) in what was the largest series of pre-op MRI at the time (n=603), our overall conservation rate was 60%, but when we had a false-positive MRI, the conservation rate was 70%! And for those who criticize our work at Mercy-OKC because we have no control group with routine consecutive use – yes, there are certain claims we cannot make with regard to the impact of MRI. However, there are inter-group comparisons that are extremely helpful with regard to cancer yields and outcomes, as exemplified by this remarkably paradoxical 70% that goes against what the vast majority believe about breast MRI. And that result comes with ZERO selection bias, by virtue of our policy of routine pre-op MRI.  (Note: the definition of a false-positive was a mere callback, with or without biopsy.  For those patients who went on to a biopsy that proved benign, the conservation rate was a surprising 86%, although now we have selection bias in favor of patients strongly committed to conservation.)

 

No published study suffers more from excessive selection bias than the oft-quoted COMICE Trial (one of the 2 fake news studies I mentioned). While I’ve heard speakers at the podium describe this as a trial of “all comers,” nothing could be further from the truth. Ignore the fact that MRI was poor quality, interpreters were not expert, biopsies of enhancements were often not performed (sending patients straight to mastectomy), and surgeons were barely engaged – yes, ignore all that – the mere study design in Materials and Methods is bizarre. First of all, patients were selected as possible candidates for conservation and referred to study coordinators, but then, a staggering 70% were excluded from the study. Even though the vast majority of women are candidates for conservation, somehow 70% still didn’t make the cut. The breakdown goes like this: 1,360 did not meet inclusion criteria, 1,173 refused to participate, and a remarkable 1,338 were excluded for “other reasons.” What other reasons? Incredible. When the great majority of women qualify for conservation, why were these women excluded? In all, 3,871 patients were excluded from the trial, while enrolling 1,623, and by the time we get to the number that underwent MRI, 816 were analyzed (not many more than our local experience in OKC at the time). This 70% exclusion rate (after referral) is given startling perspective when compared to the landmark NSABP B-06 that proved the equivalency of lumpectomy, wherein a mere 12% were excluded. Wow.

 

Does 70% exclusion make sense? No. But understanding why the COMICE trial was organized in the first place might shed some light. This was a trial in the U.K. where the National Health Service was growing weary of the high re-excision rates after lumpectomy. They wanted to see if they could get this rate below 10%. This was the ultimate goal of the COMICE Trial. Is it possible that the extraordinary exclusion rate was to identify patients with a very low probability of re-excision, that is, trying to get as close to 10% as possible without MRI, and then to see if MRI could push the number below 10%? I don’t know the answer to that question, but if so, it stacks the deck heavily against any MRI benefit through the selection of patients with very low re-excision potential. My point is that COMICE was not a trial of “all comers.” Both groups had a 19% re-operation rate, whereupon one of the principal investigators announced to the media, “This ends the debate. MRI offers no benefit,” as if there were only a single endpoint in the universe.

 

Then, critics of MRI jumped on board by announcing to their shock and dismay: “MRI caused more mastectomies in the COMICE trial.” Again, wow. 100% of patients entered the trial as conservation candidates. Possible outcomes of MRI were unidirectional. That is, there was no other outcome measured for MRI – it was impossible to look at the impact of MRI in converting a patient headed toward mastectomy to breast conservation instead. COMICE re-created the exact scenario I described above wherein false-positives cause anxiety which cause mastectomy, by putting the MRI at the very end of the process and magnifying its role. This is in sharp contrast to how MRI can be used in the newly diagnosed patient, up front and before options are discussed.

 

(Let me give a nod here to my radiology partner, Dr. Rebecca Stough, with whom I’ve co-directed the MRI program – surgeon + radiologist – since its inception. Note the word “program” where we have multiple quality measures in place that are still not generally considered routine. It was Dr. Stough who first hypothesized the “up front, radiologist-directed MRI” to explain why our data was different than other reports.)

 

Is Mercy-OKC the only poppy with its head chopped off? Hardly. Recognizing that COMICE bore no resemblance to real world experience (and the prospective RCT called the MONET trial had its own serious limitations), EIBIR-EuroAIM/European Society of Breast Imaging designed a trial to look at the overall impact of MRI in BOTH directions – conservation-to-mastectomy and mastectomy-to-conservation. Furthermore, they decided to minimize selection bias by including “all comers” (this time for real) in a study of consecutive patients, some getting MRI, some not. Prospective, but not randomized. “Lower level evidence,” the pundits will say, but the real world functions at this lower level whether we like it or not. Furthermore, to put the critical mind at ease, one of the harshest critics of pre-op MRI anywhere on the planet – public health expert Nehmat Houssami, PhD – was on the Steering Committee. So let’s give a little credence to this study where results were announced in the month prior to the new policy statement by the American Society of Breast Surgeons.

 

Although not yet in print, well-known expert, Dr. Franceso Sardanelli of the University of Milan presented the data in Vienna (European Congress of Radiology) on March 1, 2017. Known as MIPA – Multicenter International Prospective Meta-analysis of Individual Woman Data —  this study of 2,425 patients is old school – rather than using the Average Joe for interpretations, they followed the “Center of Excellence” pathway, selecting 34 centers in 14 countries where MRI expertise was in place. Of note, the U.S. site was in Northwest Arkansas under the direction of noted MRI expert, Dr. Steven Harms (who had been our mentor at Mercy-OKC). Too, this was a study of modern MRI (2012-2016), as opposed to primordial MRI done in many studies that are often quoted. Of special note, the MRI was ordered early, up front, by the radiologist in 68% of cases. The goal was to study the overall impact of MRI, multiple endpoints, and in both directions. This is not easy to do. One must know what the patient was planning prior to the MRI, which is a challenge when the MRI is ordered early in the process. Nevertheless, the study focused heavily on this Bi-directional Analysis, something that the single-focused (“get those re-excisions under 10%”) COMICE organizers had no interest in whatsoever.

 

Results? There was a statistically significant lower re-excision rate with MRI – 8.3% with MRI vs. 13.4% without MRI (p<0.001). Interestingly, these numbers are nearly identical to what we have been reporting for many years. Our re-excision rate has been 9% for 15 straight years, never varying more than 1% for a given year, in spite of a 100% turnover in surgical personnel.  Furthermore, in the MIPA Trial, MRI did NOT cause a statistically significant increase in the number of mastectomies. Yes, 13.7% underwent more surgery (mastectomy), but as we have been squawking about relentlessly for the past 15 years, that’s only half the story – 12.7% underwent less surgery than had been planned prior to the MRI (the 1% difference not statistically significant). Ideally, those changes in management will be for the better, that is, those converted to mastectomy should have needed to do so, and those converted to conservation should have better results as well (time will tell).

 

So, if you thought this debate was over, I’m putting my poppy head back on, and will start crowing again about 9 endpoints in the evaluation of pre-op MRI benefit, 2 of which have never been studied – 1) the multiple re-excision rate (3 or more operations), and 2) the rebound mastectomy rate after positive margins.

 

But as to our original question as to whether or not we can properly select patients as to who needs a pre-op MRI, my reference is a high quality database that I’ve kept meticulously for 15 years, wherein I compare final pathology to pre-op MRI in real time (n=1,800), and I’ll confess – I can’t tell. (Some hints, yes, but this will require formal analysis).

 

I’m fine with those who practice breast surgery without MRI. I’ve never claimed that it should be the standard of care. In fact, Ben Anderson, MD, has made the point that, in underdeveloped countries, you can practice good conservation surgery without pre-op mammography. This should be no surprise. After all, newfangled mammography was not a requirement for entry to the B-06 trial, and the independent impact of mammography on the management of palpable cancers has never been studied to the degree we are demanding of MRI. Fisher Theory was a pre-mammography construct. The attacks against pre-op MRI based on the notion of biology trumping anatomy apply to tumor mapping with mammography as well. How can an unsuspected 1.0cm invasive cancer found in the opposite breast on pre-op mammography prompt lumpectomy, radiation and node sampling, but if that same 1.0cm tumor is found on pre-op MRI, it is deemed “subclinical?”

 

While I am completely comfortable with surgeons who do not use pre-op MRI, I do bristle with borderline restraint when those who are opposed to anyone having pre-op MRI say, in effect: “We can’t make it work, so you have to stop doing it.”

 

So how about the middle of the road – selective use for pre-op MRI, the original topic of this wordy blogatorial? I’m fine with those who opt for selective use for MRI. It’s an alluring proposition. My point is to remind everyone that, at this point in time, we are not basing selective use on published results. At present, the difference between young and old, ductal and lobular, dense and non-dense, simply doesn’t jump out of my database, nor has it been clarified in the published literature where others have failed to define the best candidates for MRI. Pre-op MRI is a decision best left to the individual physicians and/or institution.

 

As a post-script: Our surgeon-radiologist combo directing the MRI “program” at Mercy-OKC once wrote about our methodology as being more important than the exact technology (brand) utilized for breast MRI (Hollingsworth AB, Stough RG. Conflicting Outcomes with Preoperative Breast MRI: Differences in Technology or Methodology? Breast Diseases: a Year Book Quarterly 2010; 21: 109-112) I believe any reader of this article will be surprised at the exhaustive steps we took to avoid the inherent danger of false-positives, specifically to avoid unnecessary mastectomies. Our approach has been anything but the Average Joe.

 

 

 

What is “early breast cancer?”  By convention, most clinicians use the term to describe breast cancer diagnosed at an early stage, usually Stage 0-II.  However, some would argue that Stage II is not particularly “early.”  Our goal in screening is to minimize the number of Stage II, III, and IV cancers while maximizing Stage I (Stage 0 in situ disease is a more controversial goal).  Stage I breast cancers are “invasive” tumors that are smaller than 2.0cm with lymph nodes negative.

“Early diagnosis” implies early-stage, but has other implications as well.  The ubiquitous statement that “early diagnosis is the key” implies that a screening intervention, be it a focused self-exam or a radiologic image, can detect a tumor earlier than what would have occurred naturally when a lump becomes obvious.  And this is where things get murky.  What does “early” mean, and with it, what does “earlier” mean?

Certainly, “early” does NOT mean early by the calendar.  In fact, it can be argued that the duration of a cancer residing in the breast prior to diagnosis is inversely proportional to its aggressiveness.  A slow-growing tumor that is late to spread might be existing in the breast for many years, while a very aggressive tumor can appear within a few months of a normal mammogram. Better to have the breast cancer that’s been sitting silently for years with very slow growth than the one that pops up this quickly.

So, if it’s not the calendar that marks “early,” what is it?  While tumor size is an important indicator of “early,” and thus predictive of curability, there is another component — biology.  Each tumor has its own set of built-in instructions that are not evident through the traditional staging system.  Eventually, we will abandon the current system of anatomic staging (and perhaps even the organ where cancer arose) and tumors will be described by this internal system of instruction, based on the genetics and biology of the tumor cells as well as how these instructions interact with the host.  Therapies will be designed accordingly, and we will no longer be discussing “early stage” disease.

In the meantime, however, tumor size and its biology go hand-in-hand, with biology already dictating whether or not “early diagnosis” (through screening) does any good.  For screening to work, the intervention must reliably find cancer earlier than what would occur naturally, but that’s not all — the biology must be vulnerable.  Oddly, the most aggressive cancers might not be vulnerable to early detection, having disseminated lethal cells throughout the body from the git-go.  On the other hand, some cancers grow so slowly that early detection might look good on paper, but the cancer would still have been eradicated successfully if the patient had waited until the lump became palpable.

Critics of breast cancer screening, in fact, claim that those are the only two types of biology — 1) systemic early on, prior to early detection through screening, and 2) localized to the breast and cured without screening.  Most of us, however, believe that breast cancer has a wide spectrum of different biologies, and that some breast cancers are vulnerable to early detection a la Goldilocks — not too hot and not too cold — but just right, such that early detection identifies tumors that are local at the point in time when screening occurs, but left to their own devices, would have later become systemic.

And while the lay public might think this “Goldilocks biology” is true for ALL breast cancers, in fact, using mathematical models beyond the scope here, one can easily show that this vulnerable biology is driving only a MINORITY of breast cancers.  Otherwise, four decades of mammographic screening would have had a stronger impact on breast cancer deaths overall.  Still, we screen to capture this vulnerable group of breast cancers.

So, do we need “earlier” detection?  Probably not.  We already have the tools to find breast cancer at 5mm (0.5cm), or “early Stage I,” which should translate to a 95% cure rate. But this cannot be done in most patients with mammograms alone.  What we desperately need is more reliable detection — identifying the cancers that are currently being missed by mammography.

It is unsettling to think that these critical tools to help mammography are sitting idle most of the time – screening ultrasound, MRI and molecular imaging. Cost and feasibility are prohibiting widespread application of these tools that, together, could lower breast cancer mortality to a greater degree than mammography. Strange – for once, we don’t need more R&D or any technologic “breakthroughs” – we need to figure out a way to make these tools cost-effective.

My proposal since 1993 has been the development of an intermediate, inexpensive screening tool to be used post-mammography, identifying patients who need a second form of imaging in spite of a negative mammogram. For a detailed look at progress in this area, I encourage you to read my book on mammographic screening, detailed elsewhere on this web site. There, I offer the history of mammography, how it got to the point of such intense controversy, and how we can revolutionize screening so that a Stage II, III or IV diagnosis is a rarity.

 

 

 

Although many variations of the Hippocratic Oath hover around graduating medical students today, none of these versions specifically states that the individual patient is to be held in higher regard than public health policy. There’s a reason for this — public health policy didn’t exist 2,400 years ago in ancient Greece, at least not in the way we think of it today. Regardless, an oath of some sort (half of U.S. schools still use a version from Hippocrates) is administered in all of the existing medical schools in the United States, with the implication being that proper care of the individual patient is held above all else.

As straightforward as reverence for the individual patient might seem, there is a concerted effort to plunge a final dagger into the heart of the Hippocratic Oath. Why? The short answer: money. Technology has advanced so rapidly and become so costly that we can no longer think about what is “best” for our individual patients. Society comes first. This, of course, is the basic tenet of socialism, an economic platform with a philosophic foundation, be it right or wrong or somewhere in between.

Gregg Bloche, MD, JD, is a Professor of Law at Georgetown who previously served as a health care advisor to President Obama. His best-selling book raised collective eyebrows in the medical community, though not necessarily due to disagreement. His complete book title is: The Hippocratic Myth: Why Doctors Have to Ration Care, Practice Politics, and Compromise Their Promise to Heal. The author does not call for the death of the Hippocratic Oath; instead, he makes the point that the Oath is already dead, so now, let’s admit it. In a word, limited resources have already prompted a radical change in medicine, and many physicians are practicing for the good of the whole, rather than for individual patients, whether they admit it or not, whether they know it or not.

As it applies to this blog, I was surprised when reading the book to find that Dr. Bloche used screening for breast cancer with MRI as one example, this being a special area of interest and expertise for me. The facts in the book are presented correctly, acknowledging the better sensitivity of MRI in the detection of breast cancer, but pointing out the difficulty in justifying cost, not to mention the arbitrary cut-off for who qualifies and who doesn’t. I can find no fault with breast cancer screening as portrayed by Dr. Bloche in what has become a health care manifesto of sorts.

But here’s where paths diverge. Rather than throwing up my hands and bemoaning the fact “Oh, we can’t afford this or that,” I look for ways to make the superior MRI affordable for society, that is, “cost-effective” in today’s parlance. It’s not that hard if you think about it.

The first problem is the nearly universal belief that the only way to approach aggressive screening with MRI is through risk stratification, offering MRI only to women at the highest levels of risk. All international trials followed this reasoning. Yet, if one reviews cancer yields in the highest of all risks – women positive for mutations in the BRCA genes – only 3 women out of 100 will be found to have cancer on a single screening MRI, missed by mammography. Forgetting costs for a moment, this is actually considered a “high” yield, given that mammography identifies cancer in only 5 women out of 1000 (0.5%) in the general population. Even though 3% with MRI is six-fold the yield in general populations screening, it is not good enough to ensure cost-effectiveness. What now? Curse the insurance companies? The government? ObamaCare? Write books about the death of the Hippocratic Oath?

There are alternatives. First, lower the cost of breast MRI. This transition is in progress now, with the implementation of “fast” MRI for screening. With shorter study times (10 minutes instead of 30 minutes), the cost can be lowered significantly. Rather than several thousand dollars, some are offering the service at less than $500. Now, a Catch-22. Even though insurance covers high-risk screening with MRI, the coding system used universally in billing does not have a different code for screening MRI vs. diagnostic MRI. Therefore, the system shoots itself in the foot as radiologists are ready to lower costs, but can only bill at the higher rate due to the single code. Bottom line: those wanting to take advantage of “fast MRI” for screening must pay cash, and it doesn’t count toward the deductible.

Next, risk-based guidelines need an overhaul. The question should not be, “What is a woman’s breast cancer risk over the course of her lifetime?” Rather, we should be asking, “What is the risk that a woman’s mammogram is harboring an invisible cancer, on the very day of the normal screen?” This may or may not relate to long-term risk. It most certainly relates to the level of breast density on X-ray, a factor not even included in current MRI guidelines! To this end, my research collaborators are focused on computer analysis of subtle asymmetries on “normal” mammograms that currently escape expert radiologists as well as the so-called CAD, “computer-aided detection,” in common use today.

A different approach to the same problem of “current risk” vs. “lifetime risk” would be the development of a blood test to detect mammographically occult breast cancer. I have spent 20 years assisting basic scientists in the ongoing development of a screening blood test in which biomarkers would indicate the presence of cancer independent of mammographic findings. If either the “ultraCAD” approach above or the blood test prove successful, cancer yields on MRI could emerge as 10% or greater (with “missed cancers” very rare), vastly superior to anything remotely possible through risk-stratification, and easily cost-effective.

It speaks to the spirit of individualism that there’s a way to make this work. We already have the remarkable technology of MRI that can decrease the mortality of breast cancer well beyond what mammography can do by itself. So, rather than issuing guidelines that restrict care, it’s up to us to figure out how to make MRI (and other imaging approaches) cost-effective for potential “second tier” use in all women. After all, the majority of newly diagnosed breast cancer patients have no identifiable risks, a fatal flaw for those who trust entirely in risk stratification while proudly espousing “personalized medicine.”

One last point – while 100% of medical schools in the U.S. administer a professional oath of dedication to the individual patient, Hippocratic or not, as noted in the opening paragraph, it is interesting that only 50% of British medical students do the same.

The individual patient is teetering in a precarious balance weighed against societal resources, and there can be no doubt that some tottering is well underway.

 

Without criminal intent, several organizations have decided that screening women below age 50 does more harm than good. Most importantly, the U.S. Preventive Services Task Force led this crusade with their 2009 recommendations that flip-flopped from their 2002 recommendations. “New data” was the alleged reason, but if you read my book, Mammography and Early Breast Cancer Detection, you will learn what really happened.

 

The so-called “new data” on the beneficial side of screening was the results from a single trial that, while supporting mammography for young women, did not alter the 2002 calculations one fraction. In 2002, the Task Force calculated a 15% relative reduction in mortality through screening women in their 40s. After the new data was added, then the 2009 Task Force calculated an identical 15% mortality reduction. No difference in benefit, so why the dramatic change in policy? Answer: the harms of screening.

 

The “harms” are things like “false-positives,” which are mostly the routine call-backs that come with screening. We call back roughly 10% of those screened, then after diagnostic views, only 5% are left with either short-interval follow-up or a biopsy that proves to be benign. However, the full 10% are “harmed,” according to the Task Force. Others weighed in with publications demonstrating permanent psychological damage from a benign biopsy (Why didn’t they measure the permanent psychological harm from a delayed diagnosis of breast cancer?) By inflating the harms, then magically balancing how many call-backs equal a saved life, the 2009 Task Force issued their reversal in policy – no routine screening under age 50.

 

The position of many of us who practice breast cancer screening is that the “15% relative mortality reduction” for women in their 40s is understated, perhaps greatly understated. Why? Too many reasons to list here (again, refer to my book), but let me offer the two most important. The first is that these calculations are based on obsolete technology used in the 1970s and 1980s primarily, with screening studies ending by the 1990s. With the later introduction of digital mammography, and more importantly, 3-D tomosynthesis mammography, our detection rates are much improved. One of the reasons it was hard to demonstrate breast cancer mortality reduction in younger women was that too many breast cancers were missed on mammography. But if we were missing half of detectable cancers in this group in the early days of screening (and we were!), yet still could demonstrate a mortality reduction, think what we could accomplish by finding the other half. Or even half of the other half!

 

The second reason is the “Intent to Treat” rule, or in this case, the “Intent to Screen” rule that haunts all prospective, randomized trials for screening, the gold standard in research. Outcomes are based on the group to which people are assigned in these “high quality” studies, not to what patient volunteers actually did. In the case of screening mammography trials, this is a huge issue. Many women assigned to mammography were not compliant, and many assigned to no mammography opted to have mammograms anyway. Toss them out of the study? No. That would be scientific malfeasance. No, instead, they are counted to the group to which they were originally assigned. That’s right. Women who did not get mammograms stay in the mammography group, and those who had mammograms were counted in the no-mammography group. One has to read the small print in these studies to learn the outcomes for those patients who were compliant, and guess what – the mortality reduction is always improved beyond the official study outcomes when measuring the benefit in the compliant patients only. But these are unofficial results. Meanwhile, the official “15% mortality reduction” is a deceptive tool used to mislead the masses as to the minimal benefit, and yet from the standpoint of scientific purity, it’s right on the mark.

 

Now, I’m finally to my point. I have always carried a burden of concern for women under age 40 diagnosed with breast cancer. To me, it has been callous disregard for a large number of women that our guidelines for screening begin at 40, when 5% of eventual victims of breast cancer are under age 40. My favorite word here (for decades now) has been disenfranchised. These young women are disenfranchised by a screening establishment that left them high and dry.

 

In 2007, this was partially corrected by the introduction of high-risk guidelines for screening breast MRI (added to mammography) where there is now a starting age of 30, and for some, age 25. Finally! But one problem – this only addresses the young women with risk factors, that is, very high risk. Unfortunately, this is only about one-fifth of the eventual patients who will be diagnosed with breast cancer prior to the age of 40 (or, 1% of the 5%).

 

I once made my “disenfranchised” statement at a national meeting where an ad hoc committee had been formed to discuss screening guidelines. It went over like a lead balloon. A participant turned to me and said, “These women are not disenfranchised, they have clinical exam and self-exam.” Really?! That may be a good way to diagnose Stage II and Stage III breast cancer, but it’s not the way to save lives. Even more remarkable here is the fact that there has never been a single study that remotely indicates that self-exam or clinical exam saves lives.

 

So, how big is this problem that has been shoved under the rug for years, based on the misconception that we’re only talking about a tiny minority? 5% of eventual victims sounds rather small, doesn’t it? Well, in 2015, the American Cancer Society estimated that 231,840 new cases of INVASIVE breast cancer (I’m not even going to count DCIS) were diagnosed in the U.S. That means that 11,592 of these women were under the age of 40. How does that compare to other cancer numbers?

 

Let’s take cervical cancer where one hears about Pap smears and vaccines and so forth all the time, as an entire industry surrounds the 11,955 diagnosed in 2013. Think of it. We offer NOTHING to the 11,592 women in their 20s and 30s who are bound for breast cancer (unless at very high risk), while an entire industry is focused on the same number of women who are headed toward cervical cancer, all ages included.

 

(As an aside, this is the reason that I have pursued blood testing and alternative imaging options for younger women in my research.)

 

But now we face a problem that absolutely dwarfs the 11,592 disenfranchised young women headed toward breast cancer. Now, we have the Task Force leading the way – without criminal intent, mind you – to disenfranchise an additional 20% of eventual breast cancer victims from the benefits of screening. Instead of ignoring a mere 12,000, we’re now going to let nature take its course into worse stages of breast cancer with 57,960 women every year. And I hate to tell you what’s next – “no screening beyond 70 or 75,” whereupon 116,000 women to be diagnosed with breast cancer every year will be relying on the unreliable self-exam.

 

How does this 57,960 eventual breast cancer patients under the age of 50 stack up to other cancers? According to American Cancer Society stats for estimated cases in 2016, here’s the list of some cancer types all ages included, most of which have no proven screening options: Uterine cancer (60,000), kidney cancer (62,000), all types of leukemia (60,140), pancreatic cancer (53,070), thyroid (64,300). The point is that we are going to deny a staggering number of eventual breast cancer victims (57,960) the benefit of early detection with screening, based on flawed and archaic data, all in the name of “evidence-based medicine” and scientific purity that is, in fact, covert cost containment.

 

And notice one other tidbit: I’ve limited my discussion here to the number of women diagnosed with invasive breast cancer, to avoid the controversial aspects of ductal carcinoma in situ (DCIS). But if we include DCIS, as can be justified by its greater significance in younger women, then we’re talking about an additional 60,000 diagnoses each year (all ages), bringing our total number of breast cancers to nearly 300,000 per year. In this all-inclusive projection, the exclusion of screening women under 50 disenfranchises 75,000 eventual breast cancer victims every year. It only takes 13 years to deny early detection through screening to 1 million women headed for breast cancer. This is the recommendation of the U.S. Preventive Services Task Force, a rotating group of government-funded non-experts (this assures neutrality) who crank out recommendations for over 100 preventive health care measures.

 

To admit to a 15% mortality reduction (while the reality is probably closer to a 30% mortality reduction), with breast cancer screening under the age of 50, but then turn around and deny screening to these 57,960 headed toward invasive disease each year, well, let’s hope the Task Force appreciates the fact that there is no criminal prosecution in the establishment of guidelines.

How would you answer this: “What’s the best way to screen for breast cancer?”  Nearly everyone, including breast cancer specialists and radiologists, will routinely answer this question with “Mammography.”  But they’re wrong, at least by one definition of “best.”  The question is not as simple as it seems.  If one is using “best” to mean, “the most practical,” then YES, it’s screening mammography.  Mammography has the infrastructure and expertise in place so that we can screen all women in the U.S. who are interested in doing so — and, mammography is the only imaging modality which has revealed in prospective, randomized trials that fewer women die of breast cancer when screened.

But what if “best” means “best?”  That is, what if “best” means detecting cancers most reliably at the earliest stage?  (I think this is what most women hear when the word “best” is used, but those clinicians who are answering the question with “mammography” as the answer are using “best” to mean something else entirely.)  In fact, the best method for detecting breast cancer for the individual patient is MRI (magnetic resonance imaging).  Very close at the heels of MRI is MBI, or molecular breast imaging, a nuclear medicine study.  In fact, MBI can lay claim to having fewer false-positives and thus, preferred over MRI.  Both MRI and MBI require the injection of a contrast agent — gadolinium for MRI and a radionuclide for MBI.  Although MRI is widely accessible, with MBI playing “catch-up,” only time will tell about the safety of annual or biennial gadolinium vs. radionuclide, and this may be the deciding difference.

Then, for those women with dense breast tissue (another topic for later), ultrasound will actually find more cancers than mammography.  For those with extreme density (over 75% “white” on X-ray), the fact is that mammography comes in dead last of all available options.  For women with more modest levels of density, but still more than 50% of the area on mammography being “white,” it’s a toss-up as to whether mammography or ultrasound will find the most cancers.  And when it comes to the new 3-D mammography (a definite improvement in cancer detection), ultrasound still identifies additional cancers missed by this new technology.

Mammography is far from “best” when one is talking about early detection capability.  The bigger picture here is troubling — the so-called best recommendation for the general population is not always what is best for the individual.  Currently, MRI screening is used only in high-risk individuals, where published detection levels (sensitivity) are 90% compared to only 40% using mammography alone.  The same gap would be present in normal risk individuals as well, but screening the general population with MRI is so impractical that it has never seriously been considered…until now.  A new “fast MRI” may make screening larger numbers of women with MRI more practical.  Dr. Christiane Kuhl (Germany) has presented the first data on screening the general population with MRI, and it’s impressive — for women cleared by clinical exam and negative mammograms, and nearly all with screening ultrasound as well, Dr. Kuhl and her group identified 11 cancers per 1,000 patients at normal risk, roughly double the detection rate of mammography…after mammograms had already deemed these women as A-Okay.

The key is going to be identifying patients for ultrasound and MRI screening in a cost-effective manner.  And it is for this reason that I am involved in two major research efforts to properly select patients for additional imaging, based not on future risk, but the current probability that a cancer has been missed by mammography.  This blog will return to these research projects again and again.  And for detailed information as to how and why breast cancer screening needs to be overhauled, check out my book: Mammography and Early Breast Cancer Detection: How Screening Saves Lives.

The term “precision medicine” is so overused today that it suffers the same fate as any buzz phrase that is tossed about carelessly – loss of impact. Whether you call it “precision medicine” or “personalized medicine,” the intent is the same – to customize medical care for the individual. Effective treatments are applied only to those who benefit. Who can argue with that?

In its purest sense, it’s the ideal approach to medicine. Imagine a patient newly diagnosed with cancer whose tumor is analyzed at the molecular level, whereupon the exact combination of therapeutic agents designed for the tumor’s particular profile is utilized, and the cancer is eradicated. Great strides are being made in that direction.

Paradoxically, however, when it comes to breast cancer screening, the use of precision medicine to justify doing less benefits society at the expense of the individual. This is not the alleged intent of precision medicine; it’s the exact opposite.  Precision medicine is supposed to benefit the individual.  Let’s see how so-called precision works when it’s applied to the justification to do less screening for the early diagnosis of breast cancer. This is not hypothetical, by the way. The term “precision medicine” is often used in the defense of new breast cancer screening guidelines where only those women at increased risk are to consider mammography in their 40s.

The primary care physician, caught in the storm of controversy, is currently being pressured to say this: “Since you don’t have a family history for breast cancer, I’m going to recommend waiting until you’re age 50 to begin mammographic screening.” Or, this: “Since your mother had breast cancer, I’m going to recommend that you begin screening at age 40.” It sounds perfectly reasonable, but the logic is deeply flawed if one understands the numbers behind these recommendations.

For the decade of the 40s, the general incidence of breast cancer is only 1.5% over the course of these 10 years. That is, only 15 women out of 1,000 are going to develop breast cancer during their 40s (compared to 25/1,000 in their 50s, and 35/1,000 in their 60s). In order to improve cancer detection rates, the recommendation has been made to selectively screen only those women with risk factors.

However, the majority of women who develop breast cancer have no identifiable risk factors, so this logic is flawed from the git-go, even before we get to actual numbers.

This selective approach for breast cancer is to be sharply distinguished from lung cancer where “precision” screening using chest CT works quite well because of the tight correlation of smoking to the disease being addressed through screening. In lung cancer, 80% of patients have a smoking history. But more importantly, the level of lung cancer risk is 20-fold in smokers over the general population. Thus, smokers are advised to screen, while non-smokers are advised to do less, that is, nothing. Inevitably, there are those who believe we should adopt the same approach for all cancer types – screen only those at risk.

But breast cancer is not even in the same ballpark. Instead of 80% who have risk factors as in lung cancer, only 20-25% of newly diagnosed breast cancer patients have a positive family history. So, our target population is far weaker than what is seen in lung cancer. But it gets worse – the power of risk due to family history in breast cancer is often negligible. Rather than the 20-fold risk of smoking and lung cancer, the usual risk seen for breast cancer (with one first-degree relative with the disease) is more in the range of 2-fold, or one-tenth the power of smoking and lung cancer risk.

With the exception of patients who have multiple family members affected by breast cancer (with or without testing positive for a breast cancer predisposition gene), the usual “high risk” patient for breast cancer is not strikingly elevated above and beyond baseline risk. Again, unlike lung cancer where the baseline risk is low (in non-smokers), the baseline risk for breast cancer is high for all women.

 

Here’s how the numbers work in the decade of the 40s:

Risk of breast cancer over 10 years if there are no identifiable risk factors – 1%

Risk of breast cancer over 10 years in the general population of women – 1.5%

Risk of breast cancer over 10 years with one first-degree relative with breast cancer – 2%

 

Do we really limit screening to the group with 2% 10-year risk and ignore the women at 1%, understanding that this maneuver will exclude the majority of women with breast cancer? Certainly, the yield improves when you limit screening to higher risk patients, but “cancer yields” don’t tell you how many women were excluded to get the boost from 1% to 2%. Cancer Detection Rates (CDRs), or cancer yields, can be very misleading.

The cost savings with “precision medicine” in this instance is staggering. If we take 100,000 women in the general population at baseline risk, we will need to perform one million mammograms over the course of 10 years to identify 1,500 cancers. But if we limit screening to only those women with a positive family history, we will need to perform only 200,000 mammograms over 10 years rather than 1,000,000. And, our cancer yields go up! 2% rather than 1.5%. Great. But we will only find 400 cancers (2% of 200,000), while missing the majority of breast cancers with this “precision” approach. Had we simply screened everyone, “without” precision, we would have encountered 1,500 cancers, instead of only 400.

Actual breast cancer deaths are calculated differently, and require taking into account cancers missed by mammography, but a reasonable estimate is that there will be an additional 1,000 breast cancer deaths every year in the U.S. if we restrict screening to a starting age of 50.

In a roundtable committee on screening, I once stated that women in their 30s were already disenfranchised from early diagnosis of breast cancer (5% of eventual breast cancer patients), but to start screening at 50, we would disenfranchise a much larger group of women in their 40s (an additional 20% of eventual breast cancer patients). I was challenged by a colleague who said, “These women are not disenfranchised, they have physical exam.” Wow. Clinical exam usually detects Stage II disease (or worse) in this population, and there’s not a shred of evidence that lives are saved. Yet, the evidence is strong that mammographic screening lowers mortality in the 40s, even if the benefit is not as great as later in life (even the Task Force admits to a 15% relative reduction in mortality for women screening in their 40s).

When it comes to breast cancer screening for women in their 40s, the admonition to do less in the name of precision medicine benefits society by lowering health care costs, but does little for the individual and could cause substantial harm, the exact opposite of what “precision medicine” claims to be.

For a detailed look at the fallacy of risk-based screening as used to justify doing less, refer to my book — Mammography and Early Breast Cancer Detection, Chapter 20 — Risk-based Screening — It Feels So Right, But Wait…

 

 

In a recent article in the New England Journal of Medicine[1], H. Gilbert Welch and associates draw from the Surveillance, Epidemiology, and End Results (SEER) program, spanning the years from 1975 to 2012, to estimate the extent of overdiagnosis in mammographic screening. Using yet another variation of indirect deduction, this time focusing on tumor size, it was determined that “only 30 of the 162 additional small tumors per 100,000 women that were diagnosed were expected to progress to become large,” implying that 81.5% of women were overdiagnosed by screening. This conclusion sets a record high for the calculated extent of overdiagnosis. Given that there are no patient-specific data about mammography use and compliance in these massive SEER reviews, one has to wonder how far these indirect methodologies can take us. Is the next stop 100% overdiagnosis?

While criticisms of the indirect methodology will be levied, then defended ad nauseum, one question always looms in this debate when discussing the overdiagnosis of invasive breast cancer (as distinct from DCIS) – why don’t we see evidence of these overdiagnosed tumors clinically? Of course, the epidemiologists have beaten that argument down to their own satisfaction by reminding us that these overdiagnosed tumors are all removed, making it impossible to directly measure overdiagnosis.

Still, what is happening at the level of the individual patient? At the level of histology and tumor biology? For such an allegedly pervasive phenomenon, there must be a clinical correlate. In fact, the only true overdiagnosis that can be directly documented would be more accurately labeled as misdiagnosis, e.g., when a complex sclerosing lesion is confused with invasive carcinoma, something that can trip up even the experts[2]. But this scenario doesn’t even begin to explain the staggering numbers for overdiagnosis being generated indirectly and likewise spilling into proposed informed consents for screening.

Using the strict definition of overdiagnosis – tumors that never progress – there are only two scenarios that could be happening at the histologic level, that is, tumor regression or, alternatively, tumor quiescence where the cancer simply reaches a certain size and stops. Either way, the tumor does not become clinically evident during the life of the patient.

Of these two possible explanations, the focus recently has been more on complete tumor regression, a shifty target since the evidence has already disappeared. Dr. Welch and associates will sometimes reference themselves in claiming that evidence supports regression[3], yet these are not studies where direct observation confirms that cancers melt away. Instead, the authors simply layer more indirect evidence on top of the indirect premise, creating a circle of self-validation.

And this is where a different perspective emerges through direct patient care. While there is some evidence histologically for “burned out” DCIS, the microscopic evidence from which to theorize the same for invasive regression is scant, at best. In contrast to the claim that we can only use indirect methods to study overdiagnosis, we clinicians have the occasional opportunity to witness tumor regression in the clinic by virtue of those women who refuse biopsy of screen-detected abnormalities. Or, after biopsy, patients refuse further treatment. Curiously, some will return for follow-up, often still declining further intervention. For us, this sequence happens once or twice a year, mounting to a fair number of instances over time. The collective experience of thousands of radiologists over decades seems to be similar to our facility where we have never seen a single case of tumor regression.

The more plausible explanation would be the option of tumor quiescence – that is, indolent tumors that reach a certain size and stop growing, remaining silent throughout the life of the patient. The easily accessible repository in which to document this phenomenon – directly – would be autopsy studies, in which these cancers should be evident in numbers out of proportion to expected disease prevalence. Oddly, there is so much momentum by the overdiagnosis bandwagon that it is not uncommon to hear it stated that autopsy studies of occult breast cancer show “pretty much the same” as what one sees in prostate cancer where overdiagnosis is readily evident. But this is not the case.

In the 1990s, there was enormous enthusiasm for the detection and treatment of DCIS, as it was felt that we could nip invasion in the bud every time. To counter this trend, an epidemiologist set about to show how common DCIS could be found in autopsy studies. Indeed, the review[4] indicated modest potential for overdiagnosis in DCIS (range – 0 to 14.7% in seven autopsy studies, drawn largely from the pre-mammographic era). As one would expect, the higher rates were seen when more slides were examined, the mean number of slides per breast in the studies ranging from 9 to 275.

Seemingly forgotten today, this comprehensive autopsy review secondarily included coverage of invasive disease as well. Given that disease prevalence in the U.S. for invasive breast cancer among the living is around 1% in an unscreened population, one would expect the autopsy data to generate a number well in excess of 1% if widespread tumor quiescence were at work. In fact, the discovery of occult invasive breast cancer in these seven autopsy studies ranged from 0 to 1.8%, with a median of 1.3%, a strong indictment against the alleged phenomenon of tumor quiescence. Of special interest, the lead author of this 1997 autopsy review was H. Gilbert Welch, cutting his teeth on DCIS, who many years later would move on to invasive disease. In 2012, Bleyer and Welch would claim that, over the past 30 years, 1.3 million women in the U.S. have been overtreated for pseudocancers of the breast, and that we continue at the rate of 70,000 per year[5].

If neither tumor regression nor tumor quiescence is at work, then most likely, the magnified illusion of overdiagnosis is occurring due to old-fashioned length bias with very long natural histories for many breast cancers. By using indirect methodology, these tumors appear as “excess cancers” when adjustments are not made accordingly.

Overdiagnosis is intertwined with length bias – the former being tumors that never progress, the latter relating to tumors that slowly progress. Yet, the two cousin concepts can be indistinguishable in large-scale population studies, depending on how the data is handled. And given the choice of picking overdiagnosis as one’s operative word versus length bias, which is the more powerful iconoclastic bombshell? “Overdiagnosis” sends shivers up the public spine, whereas “Length Bias” only draws a collective yawn.

 

 

[1] Welch HG, Prorok PC, O’Malley AJ, Kramer BS. Breast-cancer tumor size, overdiagnosis,, and mammography screening effectiveness. N Engl J Med 2016; 375:1438-1447.

 

[2] Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 2015; 313:1122-1132.

 

[3] Zahl PH, Maehlen J, Welch HG. The natural history of invasive breast cancers detected by screening mammography. Arch Intern Med 2008; 168:2311-2316.

 

[4] Welch HG, Black WC. Using autopsy series to estimate the disease “reservoir” for ductal carcinoma in situ of the breast: how much more breast cancer can we find? Ann Intern Med 1997; 127:1023-1028.

 

[5] Bleyer A, Welch HG. Effect of three decades of screening mammography on breast-cancer incidence. N Engl J Med 2012; 367:1998-2005