By Cancer Rose, March 9th
On the eve of March 8, dedicated to women’s rights, it is clear that a key right—the right to objective health information, as highlighted by the 2016 citizen consultation on mammography screening—remains unfulfilled.
https://www.cancer.fr/presse/depistage-des-cancers-du-sein-23-000-deces-evites-entre-2004-et-2018 – Evaluation of the impact of breast cancer screening, National Cancer Institute / Summary report, March 2026 –
We highlight this issue with the March summary report from the French National Cancer Institute (INCa), which uses a modeling study focused solely on demonstrating mammography screening’s benefits, without addressing its limitations. Find the translated main sections at the bottom of the article.
According to this report, screening would reduce breast-cancer deaths by about 20%, only 8% of detected cancers would be overdiagnoses (cancers that would never have caused problems), and the rate of severe cancers would be reduced by 26%.
Critical analysis of the INCa report on breast cancer screening
1. Methodological flaws
A – Use of a modelling approach
The report relies on a computer model (MISCAN-Fadia model) that simulates cancer progression and tumour growth toward metastatic stages. The results, therefore, depend on the assumptions chosen (tumour growth rate, screening effectiveness, etc.). These are not measurements taken directly from patient groups but projections based on assumptions.
According to this theoretical model, each woman with a tumour undergoes a linear tumour growth rate(simulated by modelling) with constant and inevitable cellular multiplication. However, the natural history of the disease—that is, the evolution of a tumour from its origin to its end if left untreated—does not correspond at all to such a simplistic scheme.
Read also: https://cancer-rose.fr/en/2021/10/23/how-does-a-cancer-develop/
Tumour growth is not linear, mechanistic, and inevitable as assumed in this model. Studies, observations, and research show that there are:
Very aggressive tumours from the outset, often missed by screening because they grow rapidly and have intrinsically poor biological characteristics
· Very slow-growing tumours
· Tumours that remain almost dormant for decades, where people die with their cancer but not because of it
Modelling tends to under-represent these indolent tumours.
As a consequence, if one assumes that all tumours are destined to grow, progress, and metastasize, then overdiagnosis will inevitably be underestimated or even denied. This is what happens here, where overdiagnosis is estimated at its lowest range, 8%, a figure that no serious study supports today and that is contradicted by autopsy studies (which show about 40% overdiagnosis for invasive cancers).
We return later to the issue of the underestimation of overdiagnosis.
The report also assumes that some in situ cancers (stage 0 cancer, not counted in incidence figures) would necessarily become serious cancers if untreated. In reality, it is now well known that a very large number of these lesions may remain silent for a long time and may even never cause problems. If the model overestimates their progression toward aggressive cancer, it creates the impression that screening saves more lives than it actually does.
B – Calibration using observed mortality data
The model is calibrated to reproduce incidence, mortality, and screening performance observed in France.
The methodological problem is that breast cancer mortality has declined sharply since 1990, but it is well known that this decline cannot be attributed to screening. Treatments have improved dramatically since 1990.
According to impact studies, mortality from breast cancer in all countries with screening programs had already begun to decline before screening campaigns were introduced, did not accelerate after screening began (which should have been observed), and also affects women who were not screened.
Thus, when the model reproduces the observed mortality decline, it must divide that decline between the effect of screening and the major therapeutic advances of the 1990s. Depending on the model’s assumptions, attributing most of the mortality reduction to screening inevitably overestimates lives saved and minimizes the real impact of modern treatments.
Since the 1990s, breast-cancer mortality has fallen by about 40%, and various studies suggest that the decline is mainly due to:
· hormone therapy
· the introduction of trastuzumab
· modern chemotherapy
· de-escalation of highly toxic chemotherapy used before the 1990s
· improvements in patient management
Read:
https://cancer-rose.fr/en/2023/06/14/the-risk-of-death-from-breast-cancer-is-declining-with-screening-or-not/
https://link.springer.com/article/10.1007/s10549-022-06773-3 (A population health assessment of screening mammography on breast cancer mortality in North Carolina)
https://jamanetwork.com/journals/jama/fullarticle/2668347 (Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012)
Several studies estimate that:
· 50–80% of the mortality decline is attributable to treatments
· only 20–50% to screening
C – Limitations inherent to this type of model
Such models are primarily designed to ealuate screening policies, not to test their relevance or effectiveness. Depending on the data entered, they can produce the results expected in advance.
The model assumes that screening works, although this is far from established, and participation rates are declining. This optimizes parameters and creates a structural bias in favor of screening.
These are well-known limitations of micro-simulation models applied to breast-cancer screening.
2. Presentation of the data
The figures can be misleading for the general public because the report highlights relative risks.
For example, it states that a woman who regularly undergoes mammography reduces her risk of death by 40%. In reality, absolute benefits are much smaller.
According to the first Cochrane meta-analysis, in real life, regarding only breast cancer mortality, one life is saved only by screening very large cohorts of women over a long period (around 10 years).
If among 2,000 screened women 4 die of breast cancer, and among 2,000 unscreened women 5 die, the reduction from 5 to 4 represents a 20% mortality reduction, but in absolute terms, it is only one additional life saved.
But in the most recent Cochrane review from 2013, the authors say:
Three trials with adequate randomisation did not show a statistically significant reduction in breast cancer mortality at 13 years (relative risk (RR) 0.90, 95% confidence interval (CI) 0.79 to 1.02); four trials with suboptimal randomisation showed a significant reduction in breast cancer mortality with an RR of 0.75 (95% CI 0.67 to 0.83). The RR for all seven trials combined was 0.81 (95% CI 0.74 to 0.87).
Therefore breast cancer mortality was an unreliable outcome that was biased in favour of screening, mainly because of differential misclassification of cause of death. The trials with adequate randomisation did not find an effect of screening on total cancer mortality, including breast cancer, over 10 years (RR 1.02, 95% CI 0.95 to 1.10) or on all-cause mortality over 13 years (RR 0.99, 95% CI 0.95 to 1.03).
Screening does not save any lives.
This is why results should always be presented in absolute numbers, not percentages that make the situation appear more favorable.
The 2016 citizen consultation on mammography screening had already denounced this practice and asked for correction, yet the INCa persists in what can only be described as an intellectually dishonest presentation.
3. Deliberate underestimation of overdiagnosis
The report indicates 8% overdiagnosis.
However, other studies—particularly by researchers such as H. Gilbert Welch or the Nordic Cochrane Centre—often estimate this figure at 20–30%.
Breast-Cancer Tumor Size, Overdiagnosis, and Mammography Screening Effectiveness.”
The New England Journal of Medicine (NEJM).
2016; 375(15):1438-1447.
DOI: 10.1056/NEJMoa1600249.
Many other modern and independant studies estimate the overdiagnosis rate much higher, for example: https://cancer-rose.fr/en/2019/09/06/mammography-screening-a-major-issue-in-medicine/
The difference largely stems from how it is calculated, especially the choice of denominator.
Definition: Overdiagnosis is a cancer detected by screening that would never have been diagnosed during a person’s lifetime without screening and would never have caused harm.
Some estimates calculate overdiagnosis relative to all cancers observed in the population, which dilutes the phenomenon. Others calculate it relative only to cancers detected through screening, where it appears much higher.
Here an example:
· 30 overdiagnosed cancers out of 100 detected by screening → 30%
· Institutional calculation: 30 overdiagnosed cancers out of 300 expected cancers in the population → 10%
The biological reality is the same, but the result appears three times smaller.
Estimated overdiagnosis according to different methods:
| INCa Report 2026 | ≈ 8% |
| MISCAN-type modelling studies | 5–10% |
| European meta-analyses | 10–20% |
| Population observational studies | 20–30% |
| Nordic Cochrane Centre | ≈ 30% |
4. The incidence–mortality paradox
If screening worked as expected, certain epidemiological patterns should appear (what G. Welch calls epidemiological signatures):
1️⃣ Increase in early cancers
2️⃣ Parallel decrease in advanced cancers (metastatic)
3️⃣ Significant decline in mortality
In reality, we observe:
· a large increase in early cancers
· very slight decrease in advanced cancers
The report’s claim of a reduction in advanced cancers is false.
The well-known study from the U.S. National Cancer Institute by H. Gilbert Welch shows a massive increase in early cancers with only a small decrease in advanced cancers. Similar findings were reported in the Harding 2015 study and many others.
In many countries, after mammography screening was introduced, early-stage cancer incidence increased sharply without a proportional decline in advanced cancers. This pattern strongly suggests overdiagnosis.
Read: https://cancer-rose.fr/en/2020/06/28/digital-mammography-not-more-effective-in-reducing-the-most-serious-cancers-according-to-australian-meta-analysis/
5. Underrepresentation of screening harms
In the INCa leaflet “Women’s Health / Breast-Cancer Screening: What Benefits?” (second leaflet which summarizes the main results of the study, identical link)
· False positives: These are alarming results on the mammography that ultimately prove negative after additional imaging or biopsies. Over 10 screening rounds, 20–30% of women experience a false positive. Consequences include anxiety, unnecessary biopsies, and medicalization. This is barely quantified in the report.
Read: https://cancer-rose.fr/en/2025/05/17/false-positive-and-cancer-dangerous-links/
· Overtreatment: a consequence of overdiagnosis; indolent lesions may be treated unnecessarily, exposing women to treatment side effects.
Read: https://cancer-rose.fr/en/2019/08/08/excess-mortality-due-to-treatment-outweighs-the-benefit-of-breast-cancer-screening-synthesis-of-several-studies/
· Radiation-induced cancers: probably underestimated. Repeated mammography and unnecessary radiotherapy increase the risk of secondary cancers, particularly in radiosensitive individuals.
Conclusion
The major limitations of the INCa report, based on modelling, are:
1. Strong dependence on model assumptions
2. Significant underestimation of overdiagnosis
3. Emphasis on relative reductions instead of absolute ones
4. Poor quantification of negative effects
5. Uncertain attribution of mortality reduction to screening
The initial assumptions are designed to produce a simulation favorable to screening, rather than a direct measurement of its real effectiveness.
Breast-cancer screening has become a major public-health program with significant political and institutional investment. An entire economy revolves around it: imaging equipment manufacturers, sponsors, and “philanthropists” who strongly promote it during the Pink October campaigns, often with limited transparency regarding financial flows.
Acknowledging a high level of overdiagnosis would create several problems: admitting to women the overtreatment they undergo, and recognizing the cost of a system that diverts resources from other underfunded areas of public health.
Changing communication with patients is difficult. It is easier—and more profitable—to maintain the “conspiracy of hope” surrounding supposedly lifesaving screening than to acknowledge uncertainty.
Yet the issue is no longer to defend mammography screening at all costs, but to recognize the scientific uncertainty surrounding its benefit-risk balance and ensure transparent information so that women can make truly informed decisions.
Every woman should receive clear and balanced information to decide knowingly. That is the honest and independent information we aim to provide here.
With this “information” leaflet based on a highly questionable methodological report, the INCa manages—on the eve of International Women’s Day—to undermine women’s right to information and equality in health.
And it is not their first attempt.
The INCa-leaflet
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