Tag wellness

What Can Be Done to Bolster Screening Mammography?

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Screening mammography was introduced slowly in the late 1960s, some momentum in the 1970s, and full steam ahead in the 1980s. Sad to say, along with its clinical introduction came a boatload of misconceptions about its efficacy. For the past 33 years, I’ve been trying to clarify the overstated benefit of screening mammography, but it has been a moving target with new bells and whistles added, such that the efficacy has nearly reached the original exaggerated numbers from decades past.

The original “Mammograms will detect 90% of breast cancers” was a deeply flawed misconception. To be as brief as possible, this 90% applied to cancers that could also be felt. That’s NOT screening. If you have a lump, then the mammograms are “diagnostic,” while screening is a term reserved for identifying breast cancer that is NOT palpable. The original prospective, randomized trials (most started in the 1960s through 1980s) were measuring “mortality reduction” in a screened population vs. no screening. So, for instance, a “20% mortality reduction” in the screened group would have fewer deaths than occurred in the non-screened group.

For further illustration, if the trial included 20,000 women over a 5-year period of mammographic screening wherein 400 women develop breast cancer — 200 in the screening group, 200 in the non-screened group — while deaths specifically from breast cancer are 100 in the non-screened group and 80 in the screened group, the “20% mortality reduction” is from 100 down to 80. (beg pardon on this oversimplification that ignores the 4 major epidemiologic biases). But 80 women still died of breast cancer even though they were in the screened group?! What gives? Mammograms appear to have saved lives, but not nearly as many as one would guess. This 20% mortality reduction is a conservative number in use today, while large observational studies indicate a 40%d mortality reduction, a better number, but not as easily accepted by critics.

It gets worse. Of the 7 prospective randomized trials (depends on how you count them), only one (Swedish 2-county) had a mortality reduction which reached statistical significance! Other trials trended toward significance, but the decision to proceed with major screening programs was a borderline recommendation from the beginning, largely made possible by combining these trials together (into meta-analyses) which then allowed us to reach statistical significance. The full story is complicated, but if you’re interested, a good resource (if I do say so myself) for a deep dive is the book, “Mammography and Early Breast Cancer Detection: How Screening Saves Lives” by Alan B. Hollingsworth (McFarland Publishers). Slightly outdated at the end by the decision to return to screening in women starting at age 40 (it was 50 when I wrote the book, appealing for age 40 in 2016). It’s a fascinating story, and I wrote it with the intent of providing insider info, personal anecdotes, historical trickery, uncontrolled human bias, and on and on, but the Editors moved all the “fun stuff” to the end, in a Chapter Notes section (rather than leave it in place next to the appropriate vignette). Still one reviewer said, “It’s more entertaining to buy the book and then just read Chapter Notes at the End.

Back to screening mammography. Why was the mortality reduction so weak in many of the trials? At the time, activist critics of screening mammography said it was because breast cancer outcome is decided very early in the course of disease and screeningsimply doesn’t find cancer early enough to make a big difference. In fact, this “biologic predestination”was the dominant belief by many academics, extending into the 2000s. But I believe there is a different explanation.

In determining whether or not screening saves lives, there are only TWO variables. The first is BIOLOGY — the cancer must lend itself to early detection. In the discussion above, critics laid the blame for weak results here. They said that early detection doesn’t matter in breast cancer because the tumor is biologically predestined as to outcome (in keeping with Fisher Theory that drove the development of lumpectomy). However, for a variety of reasons, I came to believe that this was not the case, but instead, it was variable Number TWO — SENSITIVITY — that determines outcome of screening. Sensitivity is, in lay terms, “the percentage of cancers identified.” So, the 90% sensitivity originally attributed to screening mammography was about as good as it gets, until people started to ask, “How do you know that you missed only 10%?” Therein lies the rub.

As it turned out, dense tissue mammograms are missing more than half of the detectable cancers. In the past, these women were told their mammograms were “all clear” then sent out the door with “see you next year” while their life-threatening cancer remained in place, well-hidden. Once things shifted from BIOLOGY (where there’s not much you can do) to SENSITIVITY, the race was on to improve Sensitivity. These cancers that are encased in dense tissue are best detected using MRI (or molecular imaging). But the problem arises as to how we select patients for MRI when there is no way to screen the entire population with this modality. My answer here has always been to develop a blood test that is reasonably sensitive for cancer detection, and if mammograms are negative, then a blood test positive, an MRI is indicated. Some blood tests are on the market already, but these multi-cancer options with circulating tumor DNA are not very sensitive for early-stage breast cancer, for some reason. We don’t know why. One study failed to detect a single breast cancer out of 14 detected by mammography. Needless to say, this is not good and runs counter to marketing efforts by these companies. Still, we are seeing results now in RNA-based testing that appear close to clinical use.

In the meantime, what bells and whistles have been added to mammography to rise above the rather pathetic sensitivity in women with dense breast tissue.

First came DIGITAL MAMMOGRAPHY, but this was a head fake. The study to confirm its utility was actually intended to prove itself EQUAL to original film screen mammography….not “better than.” All radiology departments were converting to digital at that time (circa 2000) so this was part of the switch. But in the process of comparing film screen to digital, we learned that the overall sensitivity in the general population was remarkably low for either technique — 70% sensitivity for digital and 66% for film screen. The definition of “missed cancer” in this DMIST study was any tumor arising after a negative mammogram with 12-month follow-up. Shockingly, if this 12-month follow-up was extended to 15 months, both approaches to mammography showed a dismal 41% sensitivity (all densities included). Only one subgroup showed a statistically improved cancer detection rate wherein digital outperformed film screen — (under 50, premenopausal, dense breasts) — and it was this group where media focused entirely on the exciting new development of digital mammography. Yet, for this subgroup, even the digital sensitivity was bad at 59%, until you look at the film sensitivity of 27%s. (imaging these women reading their mammography reports where the disclaimer noted, “5 to 10% of cancers are not visitble on mammography,” when in fact, their personalized miss rate is 73%. Of the 20 subgroup analyses, NONE had a sensitivity of 90%, and only 3 were above 80%. And this includes older women with low density mammograms.

Nexa camce CAD (computer assitsted diagnosis), a primitive artificial intelligence (AI), proving to be of little or no help for expert breast radiolostis. Studies indicated a very slight improvement in cancer detection rates (CDRs), but this was confined to radiologists who were not full-time breast specialists. The computer circles suspecious areas, while the big problem is false-positivies. In the end, it offered little.

Then came 3D TOMOSYNTHESIS, this time with a demonstrable improvement in CDRs. This development was modest, but seen consistently in all studies. Marketing approaches used the relative increase in Sensitivity because the numbers sound so much better. As we all know, absolute risks are not nearly as impressive as the same study expressed in relative terms. Example for 3D mammography — a 4% CDR with 2D is elevated to 6% CDR with 3D, resulting in a 50% relative increase in Sensitivity. Is that a 2% increase or a 50% increase? Both are true. Oh, well, every little bit helps. Importantly, the cancers found by 3D tend to be skewed toward invasive cancer, a fact that indicates a better chance at a saved life with 3D tomosynthesis, and this technology should be standard everywhere.

Finally, came a true competitor to MRI in the form of contrast-enhanced mammography (CEM). With an injection of radiology dye (iodine-based) prior to the smammogram, the Sensitivity was initially reported by several groups as comparable to MRI. That said, not all studies agree, and there are reports of Sensitivity as low as 64%. There may be a learning curve that alters final outcomes, or some technical factor, but this will eventually be sorted out, and I believe there is a place for CEM.

Until more data accumulates, I think the woman with dense breast tissue has multiple options to avoid her cancer being missed with routine mammography. These range from adding whole breast Ultrasound to 3D mammography, all the wayt to MRI and dropping mammograms entirely. And, into this mix, we will eventually figure out how a blood test streamlines screening and, importantly, cuts the costs that are inherent with any large scale screening program.

We’re come a long way since that first clinical trial in the 1960s, and it’s been an unusual trip. I was a surgery resident in training when I saw my first mammogram.We residents didn’t have a clue what we were looking at, or why we would ever need an X-ray of the breast. Same for the faculty. The story of how those original technologies were grossly inflated as to their efficacy is a fascinating story of human nature, and it’s why I wrote a book that attempted to reveal both nuance and full-scale battles. An academic breast radiologist in the U.K., newly retired, purchased a copy then contacted me with interesting praise that went something like this: “I’ve taught breast imaging and screening my whole career, but until now, reading your book, I finally understand what the fuss is all about.”

END

25 Years Earlier

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Have you noticed on TV the new way of handling flashbacks? No subtleties. No confusion. Rather, an announcement — 3 days earlier, 7 months earlier, 10 years earlier, 5 seconds earlier, and so on. Although too many flashbacks can be annoying, especially if it’s a flashback occurring within a flashback, I still think the definitive times plastered on the screen are helpful.

Recently, I had a “double flashback,” that is, 2 simultaneous events, unrelated except by the clock, each with its flashback best described as “25 Years Earlier.” Let me explain. During the first week of April 2026, I had two 25-year anniversaries.

The first flashback related to medical history made in April 2001 with the drawing of a blood sample on a patient at high-risk for breast cancer who was undergoing a breast MRI for screening purposes. Breast MRI was new at the time, used primarily for diagnostic problems, although asymptomatic screening was being tested (no official guidelines until 2007). But what we did at Breast MRI of Oklahoma, LLC, was unique. Under Institutional Review Board approval, my team started drawing blood on patients undergoing MRI, with a focus on the asymptomatic screener. I felt “risk assessment” to determine who gets an MRI was only half the solution to more efficient use of the expensive test. It took two decades before the radiologic community realized that breast density would be just as good a predictor of MRI benefit as risk levels. Indeed, we drew up a screening score based on both risk level and breast density wherein we found double the number of cancers than had we simply followed the new American Cancer Society guidelines. And, we varied the screening interval from 1 to 3 years, a move that would be validated in a large study in The Netherlands 10 years after our implementation. And, yes, we published this, yet our well-conceived point system fell by the wayside. Bottom line: even using risk and density together end up prompting many extra MRIs and false-positive biopsies.

So…from the beginning (before MRI was available), I promoted the idea of a blood test for screeners that would take us beyond risk, beyond density, and toward performing MRIs only if the mammograms were negative but blood test positive. I got the jump on this idea in 1993 when we invited Dr. Chaya Moroz to OKC to present her data supporting a screening blood test for breast cancer. With mammograms as the only available screening instrument at the time, the need for a blood test was not recognized as there was nothing else that could be done (ultrasound screening was still a few years away). But I recognized the potential immediately, having recently seen Dr. Steven Harms’ presentation in Dallas about the startling findings of what is REALLY in the breast with MRI. The future “father of Breast MRI” made it clear a revolution was on the way, but it would encounter major obstacles due to cost and false-positives.

I saw the answer as being a blood test to accompany mammograms, and if the mammos were negative, but blood test positive, then perform MRI. That simple concept drives most everything I’ve done in my career. So, it was 1993 when I merged the concepts of a blood test and MRI, but actual research could not begin until we had MRI in place (2001). And as soon as our unit was installed, we began the blood drawing research project. I recall that first patient in 2001 (a personal friend) who underwent that first screening MRI and having her blood drawn on the same visit. My reasoning was simple — how can you develop a blood test that is sensitive enough to detect early breast cancer if your gold standard reference of “normal” is mammography? We are trying to IMPROVE mammography, so the blood samples ought to be based on MRI results, that is, what’s REALLY in the breast.

By gradually accumulating quiality samples and creating a comprehensive database, we ended up as the largest collection of MRI-based samples in the world. When word spread of my unique biobank in OKC, we had researchers from all over the US and internationally accessing our supply. Over 2,000 women participated, and with multiple samples generated out of single blood tubes, we were able to distribute well over 10,000 samples to researchers around the world. (Most of the time, the researchers would be attending the mammoth symposium in San Antonio each year, then they would travel to OKC on the way home to validate what we had in cold storage.) The largest shipment was over 3,000 samples to the University of Nottingham where the principal investigator there would ask me every year at the San Antonio meeting: “Have they made a movie of Flatbellies yet?”

When the dust settled, my team had shipped samples to 13 research entities, and had collaborated with shared knowledge in most of these, culminating in formal clinical trials in 3 of them. Relationships were short-lived when the trials failed, but the company I work with now (Syantra, Inc) uses a novel approach, focused on the immune cells of the body rather than the cancer cells. Those companies now claiming success with circulating tumor DNA are not transparent when it comes to early breast cancer where it just doesn’t seem to work. My last several posts have elaborated on why this is the case, but the main point is that the performance characteristics are represented in such a tricky fashion, few clinicians can sort it out. And, heaven help the lay person.

The most popular of the multi-cancer blood tests (up to 100 cancer types at once!) was recently put to the test in a cohort where all 14 early breast cancers found on mammography were missed by the blood test. That’s a zero detection rate that you won’t find in the company brochures. (Some cancer types, did in fact, have helpful results). Another company based on the same science, includes breast cancer in their mutlti-gene profile, but there is a small asterisk — and if you look at the bottom of the page, the asterisk is explained: “Not indicated to screen for breast or prostate cancer.” Wow! The nerve!

Originally, my role for Syantra was generating those blood samples tied to MRI results in patients being screened, that is, doing what I’d done for many years. This meant clinical input as well. As plans were made for extending the clinical trial, the suggestion on the table was that I would serve as principal investigator for the United States (it’s an international trial), but it was not to be. My sponsoring hospital administration was always critical of our work (in spite of $250K generated from the samples), and when COVID hit, they took the opportunity to close down all the non-critical programs, which includes medical research.

My risk assessment/genetics program that included a longitudinal high-risk clinic, was streamlined more than any similar center in the country as far as I knew, including academia where “streamline” rarely exists. I worked with a board-certified medical geneticist, boarded genetic counselor, nurse practitioner/navigator, administrative assistant, and a research breast imaging tech with a lifetime of experience. The entire group was given their tickets home, not allowed to contact our patients to explain what was happening (leaving hundreds of high-risk women out in the cold). And when COVID was essentially over, the phone didn’t ring to call us back, and my far-reaching program, what was once called the “star in the crown” at my hospital, was tossed away — all gone, with hundreds of blood samples left in the freezer.

My consulting work with Syantra, Inc. is, by far, the longest association I’ve had, among the 13 research entities I’ve worked with in the past. A large clinical trial supports the use of the blood test, including my long-term indication for women with dense breasts and negative mammograms. It’s been 25 years since that first patient held out her arm for venipuncture, writing herself into the history books, sort of a silent partner in the war against breast cancer.

Switching Gears – what about the OTHER 25-year flashback? The very same week that Patient #1 donated her blood in 2001, my first novel — Flatbellies — was released by a small sports publisher, with a rapid rise to the top on Amazon Sports, generating such widespread enthusiasm that the novel was picked up by WW Norton & Co (New York/London), and it ended up with a review in USA Today while also named as a Barnes & Noble Hot Book For the Summer. So, why this new 25th anniversay version of Flatbellies?

One of the many surprises that both publisher and author had was the wide demographic net that was cast by the book. Golfer or not. Male or Female. Young or old. It transcended all groupings, and was at one point, placed next to the cash register of every Barnes & Noble in the world. The book was optioned for film on several occasions, each time with total assurance that the story would make it to the silver screen. But the reality was something different. Unexpected issues arose at every turn, and all options were finally allowed to lapse about 10 years ago.

Book clubs, social clubs, student groups, even “industry” groups, all seemed to have the same questions about Flatbellies. And people wanted answers, as if this were a true story. Were these real characters? Did the grain elevator scene really happen? How about the fireworks scene? Is Neander T a real person? And so forth. Another development was the original hardcover publisher going out of business years later, as this put an end to any royalties. But the looming issue was always TV/film, and two screenwriters worked without pay for these same years, trying to make the film happen. But the 2-hour screenplay wasn’t enough to capture the book, as so many things had to be left out. The same two individuals have started working now on an 8-episode teleplay that we hope will help resurrect the plans for a movie.

With all things considered, I decided that a 25th anniversary version should be published, this time anticipating the questions that so many readers have in a 30-page addendum, “The Story of Flatbellies.” The novel remains the same, but inside scoops, backstory, and photographs are added in this high-quality collector’s volume, titled, “the 25th Anniversary Edition of Flatbellies.“

So, if you look at the two unrelated pathways above, I’m standing now near the end of both careers, but still working along both pathways as before, as long as health holds out. And if someone were to ask me, “What did you accomplish during your career?” I’d have to answer: “Which one?” because 25 Years Earlier, I didn’t take the path less traveled….I took both paths.

Blood Tests for Cancer — it’s harder than you think!

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In my July 2022 blogatorial, I covered the GRAIL blood test (for 50 types of cancer) that had been promoted locally by Mercy Hospital OKC as well as the entire Mercy system. The implication was strong — “We now offer….” which might have meant they had arranged a discount with bulk orders, but it was NOT a statement of exclusivity. Any physician could order the test. In fact, within a 20-mile radius of Mercy Hospital, there were 22 providers signed up to be GRAIL users.

The GRAIL test is a juggernaut backed by Bill Gates, Jeff Bezos, etc., to the tune of billions of dollars (read that July 2022 piece for details). Problem is, it is very difficult to prove a test like this when there are 5 performance characteristics to be applied to 50 cancers, giving 250 data points to evaluate. But when the Big Announcement came, the numbers floating about were not good — that is, the most important characteristic of Sensitivity (percentage of cancers detected) was mediocre at best, while for some cancers (like breast) the results were terrible (all while heavy promotion was underway).

What the circulating tumorDNA (ctDNA) could do quite well was Specificity (fewer false positives) but that’s not what saves lives. Specificity allows for test feasibility, and their number was near 100% for most of the cancer types. Great! Fewer false-positives, more efficient, patient-friendly. But the raw data presented way back in 2022 revealed a twisting of reality that made the test look better than it really was. First of all, much of the reporting was done with advanced stage disease (that’s NOT screening). Take away the Stage III and IV cases, and the Sensitivity for most cancer types plummets. So, rather than admit the poor Sensitivity, the marketing department loaded up using a performance characteristic of “Accuracy” which, in testing parlance does NOT mean Accuracy like you’d think. It’s a formula that blends Sensitivity and Specificity. So, if you have an excellent Specificity of 100% and a bad Sensitivity of 50%, you can blend the two parameters and come up with 75% Accuracy, which is OK, but not great. Much of the marketing material about GRAIL does just that, i.e., rely on Accuracy. But when it comes to measuring saved lives, Sensitivity dominates as the key parameter. If a cohort of patient has 100 cancers, and your test under study can detect 90 of them, then great! 90% Sensitivity! (though you still have to prove fewer breast cancer deaths). Bottom line: it was suspicious in 2022 that GRAIL was not getting spectacular results, and the question was “Does it have any role in patient care today? Specifically, will it save lives, or at least lower the number of advanced cancers?”

Sure enough, when put to the test by the NHS of the United Kingdom, the GRAIL test did not save lives overall, at least when judged by “fewer advanced cases of cancer.” This is a surrogate for saved lives. Now, after the company stock crashed, they went to work and they’ve identified trends of efficacy, noting a core group of 12 cancer types where there might be benefit (the official results, though, are a failure to meet the original primary endpoints). So, by focusing on great Specificity and hiding lackluster Sensitivity, the company shot itself in the foot (though I suspect the test will get better and better over time….especially in those cancers where there are no other proven screening methods).

But what about breast cancer? For some reason, circulating tDNA does not allow reliable early detection of breast cancer. This should be headline news in the breast cancer community, but it’s hardly mentioned. Some companies using this ctDNA approach have dropped breast cancer from their lists of detectable cancers. Oddly enough, Cologuard lists breast cancer in their overview of cancers detected, but with a subtle asterisk denoting “not indicated.” Go figure. Breast cancer and prostate cancer are the only cancer types that are market by the asterisk.

In the PATHFINDER study (35,878 women), one has to go to the online supplements and use a magnifying lens to find out what happened in the 14 cases of breast cancer that were all discovered by mammography. Using GRAIL, NOT A SINGLE BREAST CANCER WAS IDENTIFIED by the blood test. All 14 were found by mammography. Incidentally, please note that the blood test research where I’ve been involved since 1993 is aimed at finding breast cancer EARLIER than mammography, detectable by Ultrasound or more certainly, by MRI. The very purpose of blood test research, in my view, has always been to use blood test results in tandem with mammography, and if the mammograms are negative (esp. if dense tissue), then a positive blood test should prompt Ultrasound at a minimum, and preferably MRI. GRAIL (and other companies using the same ctDNA) doesn’t have a chance in breast cancer — it can’t even find the cancers found on mammography, while our goal is to improve detection beyond mammography.

I’ve spent a lot of time on this topic lately (July 2022 and January 2026), as clinicians are perplexed about this form of testing, and many are confused by the 5 performance characteristics I discussed in detail in the January 2026 blog. Bottom line — by focusing on a single cancer type (like PSA for prostate cancer), we generate tons of data that has to be analyzed closely. Over the years, I’ve submitted 10,000 samples to researchers who are focused on Sensitivity using completely different approaches than GRAIL et al. The company I’ve worked with since 2017 as a clinical consultant is Syantra, Inc. (Calgary, Alberta) where scientists are looking at changes in biomarkers that are NOT directly from cancer cells, but are the changes that occur in immune cells…in response to the breast cancer cells. It’s a different approach, but getting far better Sensitivity than GRAIL. If you are interested, here’s a PR piece that was written recently about the company:

Syantra’s breast cancer blood test isn’t looking for cancer at all

50+ Cancer Types from One Blood Sample (the Pathfinder Study)

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When I’m asked my opinion about tests like Galleri from the company Grail (a spin-off from Illumina), I look at my watch to determine if there’s enough time left in the day. These tests are generically referred to as MCED (multicancer early detection tests) or “liquid biopsy” or ctDNA (circulating tumor DNA). The science is sound. Understanding the test is another thing entirely.

On the surface, it looks great. A single blood sample can detect more than 50 types of cancer, hopefully at an early stage. Already, we’re in trouble. There’s a huge amount of data for each of the 50+ cancers. We are accustomed to using at least 7 “performance characteristics” as we discuss screening tests one cancer type at a time. But blood test performance is different for each type of cancer. Multiply 7 performance characteristics by 50 cancer types, and we have about 350 data points to start the review if we want to know how good the test is for each type of cancer. Calling it a “major challenge” doesn’t do it justice.

What do we know so far? At this point, a “large” prospective study, called PATHFINDER, enrolled a little over 6,500 patients, not nearly enough to get a handle on this new approach to screening. The REACH trial is underway, aiming for 50,000 patients, and a UK trial is aiming to enroll 100,000 patients. This should provide some clarity, but numerous controversies will be generated. Consider the patient with a cancer signal that indicates a positive test, but no cancer can be found after multiple radiologic procedures. That patient will be called a “false-positive.” But what if, 12 months later, that patient develops a cancer that fits the original blood test results. In reality, that patient is a “true positive,” we just didn’t have the means to confirm the diagnosis with the first blood sample. But where do you place that patient in your statistical analysis? She or he has should be registered as a “false negative” since the original results indicated “no further action needed.” (btw — this scenario is already occurring).

Consider the fact that mammographic screening is a single test, and the “performance characteristics” are still hotly debated. Now, we shoot for 50 cancer types, and the troubles have already come in the form of class action suits for — in brief summary — due to over-hype followed by a false-positive or false-negative.

The PATHFINDER STUDY was conducted at 7 major health care facilities using one of the MCED tests (Galleri) to screen prospectively for 50+ cancer types. The results? It would take a book filled with data to explain everything. In fact, the authors gave us Supplementary Data online (15 Tables and 5 Figures) because there wasn’t enough room in the main body of the article.

One thing we already know from past articles in the development of Galleri (and other similar tests) — it DOES NOT WORK WELL FOR BREAST CANCER. For some reason, breast cancer cells don’t shed detectable ctDNA as reliably as other types of cancer (more on this later). And, for full disclosure, I’ve been in the breast cancer blood test business for 30 years, so I’m looking at the ctDNA approach with a bias that indicates we should stick with one type of cancer unless future developments indicate otherwise.

Where the test has its most promising role is for those cancer types where screening is not routinely performed or recommended. Take pancreatic cancer, for instance. Finding pancreatic cancer earlier does not automatically save a life. The biology is aggressive and might not lend itself to early detection. Then again, it might prove to be the answer for pancreatic cancer as well as other cancer types not being screened. However, the data is being presented to clinicians in a confusing way that puts a spin on the test that is optimistic beyond reality. The marketing department is not fabricating the facts, rather they are simply molding the perspective (see Addendum at the END on “performance characteristics.”)

For instance, we are told that Specificity (one of the “performance characteristics”) is 99% for multiple types of cancer. That’s great, but what does that mean?….that the test will find 99% of cancers? No, not by a long shot. Specificity says, “Given no cancers in a population, what are the chances that the test will be positive (a.k.a. “false-positive”). Because only a small percentage of patients are carrying occult cancer, the large number of “negatives” in a study dilute the formula and make Specificity look really, really good at 99% (only 1% chance of a false-positive if you undergo the test). In the PATHFINDER study, there were over 6,500+ patients, and that large number is part of both the numerator and denominator of the Specificity formula, generating the tiny 1%.

But ask the question differently, and in a more clinically relevant fashion — “If I get a POSITIVE result on the Galleri test, what are the odds that I’ll actually have one of the 50 types of cancer?” This is a different performance characteristic called Positive Predictive Value (PPV), and it is this number that is critical for a good, informed consent when discussing the test with laymen and health care providers. Here, the large number of 6,500 patients is not part of the PPV formula, so one might get a PPV of 43%, leaving a 57% chance of the test being a false-positive. The false-positives are reasonable with Galleri, but their focus is on the 99% Specificity. Potential users must understand the 99% means that,prior to testing, there is only a 1% chance of a false-positive. Yet, given a positive test, there is a 43% chance of some type of cancer being present upon further investigation, usually radiologic studies, yet ending up with a 57% false-positive rate. At this point in performance, the overall false-positive rate is higher than the true positive rate.

Now, each cancer type has its own Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). (The NPV is hugely diluted by the 6,500 negatives, so it’s not very helpful here). Additional performance characteristics are mentioned below. Then, it gets more complicated as, depending on the cancer type, some cancers are not early at all. So, when divided into stages, one can get frustrated when told “we find 60% of a certain type of cancer,” only to learn that half of them are advanced stage where screening doesn’t help. Leaving the larger numbers for advanced stage in place, however, can lead to easier acceptance of the test.

Even when a test is finding early (smaller) cancers reliably, eventually it will be necessary to document a mortality reduction or one of the surrogates for mortality reduction (fewer interval cancers, stage shift, fewer advanced cancers). We might think we’ve solved the problem of aggressive cancers by finding them smaller, but they could prove to be just as deadly. That’s why a mortality reduction, or surrogate, must eventually be confirmed.

Hang on…here’s the punchline. I’ve not mentioned the most important performance characteristic of all — SENSITIVITY. This is the percentage of cancers that will be detected by a screening test. Stated alternatively, given a certain number of cancers in a cohort, it’s the percentage that will be found by the screening test. Sensitivity is how we generate our mortality reductions. This is where lives are saved, finding more cancers earlier. It is the first characteristic on everyone’s mind when a new screening test is introduced. Specificity is very important when it comes to the feasibility factors, but it doesn’t save lives. To save lives, one has to detect as many cancers as possible, as small as possible, and this is entirely through Sensitivity. The other way to state Sensitivity is through a “false-negative rate.” If mammography is detecting 70% of breast cancers, then its Sensitivity is 70% and its false-negative rate is 30% (false-negative = mammo negative, but cancer present).

For those of us working on a blood test that “specializes” in one cancer type — breast cancer — Sensitivity vastly exceeds what is being seen in the MCED tests. Scientists are approaching 80% to 90% Sensitivity with breast-only blood testing using different methodologies. And this is where it turns bizarre when it comes to breast cancer, which is only one test of 50+ in the Galleri test. In nearly all the MCED studies, the Sensitivity for breast cancer early detection is low. Instead of acknowledging an issue here, amazingly, SENSITIVITY is barely mentioned. I can calculate overall sensitivity for all cancer types from an algorithm in Figure S3 — 21%. FYI…21% is terrible (only finding 21% of cancers), and that’s all types of cancer, and all stages. Limited to smaller Stage I & 2, the number would be even lower. This explains why the company focus is on cancer types for which there is no routine screening.

When it comes to breast cancer and MCED testing, the shocking point is not simply that Sensitivity is left out of the Discussion, but that the breast cancer data is only published deep in the Supplementary materials. There, one is surprised to find14 breast cancers identified through routine screening (exam and mammography) and ZERO were identified with the MCED blood test. Repeat: 14 breast cancers were identified per the usual screening methodology, and not a single one of the 14 had a positive Galleri blood test. There were 5 breast cancer recurrences detected, but that’s not what a screening blood test is for. The medical oncologists might be able to use the information gleaned from recurrences, but I’m totally focused on asymptomatic screening. We’ve lost our way when marketing departments tell us that MCED testing has 99% Specificity (technically true), yet the practical ability of the test to find early, occult cancer was ZERO in the PATHFINDER STUDY.

I’ve barely scratched the surface here, but try to imagine explaining all of the above to a patient or clinician. I didn’t even get into the other performance characteristics (added as Addendum) — 1) Accuracy (a combo of specificity and sensitivity), 2) Cancer Detection Rate (CDRs), or 3) Number Needed to Screen to Save One Life (NNS). Multiply the 7 items by 50 to get 350 data points from which to draw an informed consent. If you’re trying to tackle undiagnosed cancer, the Galleri test (and other MCEDs) are going to help more in those cancers that are not routinely screened. As for breast cancer, it’s not going to help at all unless major improvements are made.

“One Ring to Rule Them All” is a famous phrase from JRR Tolkein and his fantasty world. But when it comes to breast cancer, that’s what we need — a single test that generates Sensitivity of 80-90%, with Specificity of 90%, and good results in the remaining performance characteristics.

So, here’s a breast cancer screening approach for the future:

3D mammography, and if 3D is negative, proceed to a blood test designed for breast cancer. If the blood test is positive, then proceed to contrast-enhance mammography, breast MRI, or molecular imaging. A positive blood test turns screening into a diagnostic work up. The most logical place to begin blood test screening is with women who have dense breast tissue since the miss rate is much higher for them. And, so far, the blood tests being developed are not affected by density levels.

When used primarily for women with dense tissue on negative mammography, then we are talking about a cohort of women in whom, by definition, have already demonstrated ZERO Sensitivity (mammos negative), yet we know that if we proceed with MRI, we will find 10 to 20 occult cancers in the general population. With this knowledge, one could move directly to MRI or the other contrast-enhanced technologies. But this is where the blood test comes in — sparing many from having an MRI et al while still finding most of the hidden cancers.

You can see now why I look at my watch whenever I’m asked what I think about MCED blood testing. The Powerpoint version lasts an hour.

END (almost)

A streamlined view of performance characteristics, as pertains to cancer screening:

The BUILDING BLOCKS for performance are true-positives (TP), false-positives (FP), true-negatives (TN), and false-negatives (FN). These 4 outcomes (expressed in numbers) are then inserted to 4 basic formulas that each have a specific message for the clinician. In lay terms for blood testing (rather than confusing formulas), these 4 primary performance characteristics are:

SENSITIVITY — Given a population with cancers, sensitivity is the percentage of those cancers that will be identified by the screening test in question.

SPECIFICITY — Given a population that includes patients without cancer, what is the percentage that will be correctly identified as negative.

NEGATIVE PREDICTIVE VALUE (NPV) — Given a negative result on the test in question, what is the probability that cancer is absent in such a patient.

POSITIVE PREDICTIVE VALUE (PPV) — Given a positive result on the test in question, what is the probability that cancer will be identified.

Note: in the top 2 characteristics, it is the patients, some of whom harbor cancer, who make up the premise. And, in the bottom 2 characteristics (NPV and PPV), it is the test result that forms the premise. Without going into too much detail, there are some relationships among the Big 4. For instance, a very high Sensitivity will render a test to have a very good NPV, helping to rule out cancer. And, high Specificity will render a test with a good PPV helping to “rule in” cancer.

Ideally, we want all 4 characteristics to be good, or even excellent. That said, there is frequently a teeter-totter relationship between Sensitivity and Specificity. As one tweaks the experimental method to elevate Sensitivity, ground will be lost by worsening Specificity. Or, vice versa. So, in the article reviewed above, you can understand my concern that Sensitivity seems to be neglected except for the online Tables and Figures where one can do their own math.

We’re not done yet. There are also “DERIVATIVE performance characteristics” that are unique expressions dependent upon the Big 4 above. Far and away, the most confusing is “ACCURACY.” You’d think Accuracy meant Accuracy, but it doesn’t. This favorite number is much beloved by the marketing departments where great weaknesses can be hidden. Accuracy, to the research clinician, is a formula that gives equal weight to both Sensitivity and Specificity, combining them into one number. Just for kicks, here’s the formula: TP + TN, divided by TP + TN + FP + FN. When expressed as a graph instead of a number, it is called the Area Under the ROC Curve, often expressed as AUC.

The MCED tests are perfect to demonstrate how bad results can be hidden. If Specificity is 99%, and Sensitivity is an unacceptable 50%, then Accuracy is 75%. Specificity is dragged down, Sensitivity is inflated, but hey, 75% looks fairly good in detecting cancer. But this example doesn’t detect cancer at a 75% rate…it detects it at a 50% rate. Accuracy is not the same as the ability to detect cancer (Sensitivity is the key for detecting cancer). But those who read an article with Accuracy as an endpoint might fail to realize that Sensitivity could be good, bad, or ugly, you don’t know unless Sensitivity has been calculated separately. Sensitivity plus Specificity equals Accuracy. While Accuracy might be helpful in comparing two or more different tests, it has huge abuse potential when it comes to the promotional literature for the MCED tests. In other words, the 99% Specificity is so powerful it easily covers low Sensitivity levels that are quite concerning. This confusion about “performance characteristics” is one of the many reasons that class action suits have arisen with the MCED tests as being “over-hyped” and “over-promoted.”

Another DERIVATIVE performance characteristic based entirely on Sensitivity is the CDR — the CANCER DETECTION RATE. By convention, this is expressed in terms of cancers identified per 1,000 screened patients. There are many variables here, from risk status, to age, to the screening interval, to the modality used for screening. But it is very helpful in comparing different cohorts of patients or comparing different technologies.

Although there are many DERIVATIVE characteristics used to judge the effect of screening, the last one I’ll mention here is “NUMBER NEEDED TO SCREEN to save one life” (NNS). For example, NNS might be 1,500 for screening mammograms in the general population, though NNS will be paradoxically lower for high-risk patients (Example: NNS = “only” 900 mammograms rather than 1,500), and higher for low-risk women (NNS = 2,000 mammograms, rather than 1,500). This derivative is subject to manipulation of the data, as it is not always clear as to when a life has been saved through early detection.

The Big 4 plus derivatives are an integral part of medical practice, from screening to diagnosis to treatment. Yet, without a working knowledge, “industry” can pull the wool over the clinician’s eyes. Even after all these years, in the collaborative group where I work, we sometimes obsess over the various published numbers in a conference setting trying to make sure things are in order. It’s not for the faint of heart.

In conclusion, would I ever order a MCED test like Galleri (there are several), right now as the technology stands? Yes. There are certain germline mutations (born with DNA variants that are present in every cell in the body) that elevate cancer risk throughout the body, many cancer types. The most powerful of the multi-cancer genes is TP53, known as Li Fraumeni Syndrome. The cancers in these families can occur anywhere, in any organ, and the current guidelines include “total body MRI.” But not all patients can have an MRI, or they might have trouble getting insurance to pay for frequent MRIs, or might want assurance more frequently than current guidelines. The same goes for families who test negative on their germline mutations, but the families are loaded with all types of cancer. In other words, they are undiagnosed even though we suspect germline mutations that might only pertain to the one family. For these patients, I think serial MCED testing will prove helpful.

We’re going to be hearing a lot about MCED testing or “liquid biopsy” or ctDNA over the next decade, and additional confusion will be encountered as the tests are potentially useful for reasons other than screening (identifying recurrent disease, guiding therapy, long-term follow-up, etc.) Medical oncologists will be following the events from their standpoint, but I wonder which specialty will take charge of these many options when used for screening?

END

How Did the Month of Halloween Become Breast Cancer Awareness Month?

abhollingsworth Leave a comment

(Originally posted October 31, 2019, then yearly in October)

Date for this post: Oct 31, 2025

Mammography schedules are jammed in October. It’s hard to work in the callbacks and biopsies. Everyone is pressured to get it done before the looming Holiday Season. And if we find cancer, it spoils that festive stretch from Thanksgiving to Christmas, Hanukkah, or whatever, through New Years…not to mention the fact that deductibles were met at the time of the mammographic screening, but then in January, it starts over again as cancer care continues. Ugh! How did we ever get here?

Prior to 1980, there were no pink ribbons, no races, no walks. There was very little awareness and breast cancer was not discussed in polite society. The fact that Shirley Temple Black (breast cancer in 1972), Betty Ford (1974) and Happy Rockefeller (diagnosed 2 weeks after Ford in 1974) made their diagnoses public was shocking (and trailblazing). And when the Susan Komen Foundation was established in 1982, newspapers resisted using the words “breast cancer.” As late as 1993 when we opened our doors at the University of Oklahoma Institute for Breast Health, there were complaints about our signage with its “irreverent” word BREAST plastered right there in public.

Let’s go back to the 1940s when Susan and Nancy Goodman were sisters growing up in a well-to-do Jewish family in Peoria, Illinois. Who could have conceived of the notion that both girls carried a mutation in BRCA-1 that would lead to breast cancer in the both of them? After all, it would be more than 50 years before the launch of commercial testing of BRCA-1 and BRCA-2.

susan komen and nancy brinker

(Susan Komen on the left; sister Nancy Brinker on the right)

Susan grew up to be the “darling of Peoria,” a beauty queen and local model. She would enter a disastrous first marriage (groom collapsed at the wedding for starters), followed by a second marriage to Stan Komen who would run a liquor store – Stan’s Wine and Spirits – in Peoria until his retirement in 2014.

At age 33, Susan felt a breast lump. Not a good thing in 1977, on the eve of a revolution about to occur in the management of breast cancer. 1977 was, however, the peak time of enthusiasm for “subcutaneous mastectomies” with the newfangled breast implants for reconstruction…sometimes patient-driven, sometimes surgeon-driven and sometimes both. Pain, cysts, “fibrocystic disease,” you name it, then cough up the money, and surgeons would perform the “scoop out” procedure with implant reconstruction. Some women were very happy with their result. Many were not. And for some, considerable amounts of breast tissue remained in place. Nevertheless, no one at the time conceived of using the procedure for treating cancer — except for Susan’s surgeon who had been recommended by her family physician in Peoria.

After performing subcutaneous mastectomy for Susan’s cancer, the surgeon pronounced her cancer-free, a tad premature since she developed positive nodes shortly thereafter and systemic metastases as well. After treatment at both the Mayo Clinic and M. D. Anderson, she was still left with chest wall recurrences and died 3 years later in 1980 at the age of 36. Given the aggressiveness of this tumor, it is hard to lay blame on the unusual subcutaneous approach used for local control, but it is noteworthy nonetheless.

Meanwhile, younger sister Nancy had moved to Dallas where she became a buyer for Neiman Marcus, and then, in 1981, she became the bride of Norman Brinker, the restaurant magnate who founded Steak and Ale, et al, (now Brinker International) and is credited with the development of that omnipresent dining staple – the salad bar. With her new financial stature and a deathbed promise to her sister Susan, Nancy Brinker founded the Susan G. Komen Breast Cancer Foundation in 1982, a mere 2 years after Susan’s death. The foundation became intimately linked to Dallas as a result of Nancy’s new home, not Susan’s home in Peoria (although Peoria did become a site for a “Komen Breast Center,” a nationwide network concept of screening centers that never happened.)

The first Race for the Cure took place in Dallas, Texas with 800 participants in 1983. The rest is history. I attended the first race in Oklahoma City (1989, as I recall) where participants raced around the track at Remington Park. Pink ribbons were not introduced until 1991, and though several groups lay claim to the pink revolution, I’m pretty sure it was a Komen innovation.

One year after that first Race for the Cure, Nancy Brinker was diagnosed with breast cancer. She underwent unilateral mastectomy and later contralateral prevention, even though her BRCA-1 mutation was not confirmed until 2006.

Initially, the sole agenda for Komen was to flood the country with high quality screening mammography. This drew some objections, including one prominent breast surgeon, the late Susan Love, who broke ranks with the movement by clarifying that mammography is not a “cure,” and that we needed to be thinking more about a true cure and a “post-mammographic era.” Eventually, Komen expanded its scope to all types of breast cancer research.

Nevertheless, the growth of the Race for the Cure was phenomenal, taking place in a parallel fashion to the breast cancer diagnosis and treatment revolution that was well underway. It is estimated that, today, over 1.5 million participants raise money through one of several outreach programs sponsored by Susan G. Komen (several name changes of the foundation have occurred over the years, but Susan’s name is always there). Politics, of course, wormed its way into the activities of Susan G. Komen and eventually Nancy Brinker resigned as CEO.

Back to Breast Cancer Awareness in the spooky month of October. Around 1985 or 1986, the American Cancer Society teamed up with a pharmaceutical company that later became Zeneca, then AstraZeneca, announcing October as National Breast Cancer Awareness month. Zeneca was criticized for self-serving interest since they manufactured pharmaceuticals used for breast cancer. But they had, in fact, done their own internal audit on employees, showing that it was cheaper to screen with mammograms for an early diagnosis than to do nothing and pay for treatment of more advanced disease.

At this same time, the famous (or infamous) Breast Cancer Detection Demonstration Project was reporting results indicating that massive screening of the general population in the U.S. was both feasible and effective. The BCDDP was sponsored by the American Cancer Society and the National Cancer Institute, both organizations riding the waves of the War on Cancer legislation signed by President Nixon in 1971. So, by the mid-80s, the policy makers were wildly enthusiastic about general population screening with mammography, and it was a case of “full steam ahead.”

Still…why October?

I’ve not been able to nail down the exact details surrounding October as Breast Cancer Awareness Month, but I have a theory — a link between the rapid rise of the Susan G. Komen Foundation and the proclamation about October made by the American Cancer Society. I think the answer might be found by looking at Susan Goodman Komen’s birthday – she was born on October 31, 1943. She would have been 82 this Halloween.