The New England Journal of Medicine
Donald R. Lannin, M.D., and Shiyi Wang, M.D., Ph.D.
Summary by Cécile Bour, MD
Presentation, objectives and conclusions of the study
Under this rather provocative title, the authors of this study, Donald Lannin and Shiyi Wang of the Yale Cancer Center (New Haven, Connecticut) use a database from the U.S. Surveillance, Epidemiology, and End Results (SEER) to evaluate the screening.
The study is original in that for the first time, biological data are being used for an epidemiological evaluation, particularly for over-diagnosis.
The study links the biological factors of tumors, their size, and both life expectancy and latency of cancers in order to evaluate overdiagnosis, and to support Welch's thesis that the smallest breast cancers, as often detected by screening, have disproportionately favorable biological characteristics, a very long lead time, and would not compromised women's health or lives if undetected.
Welch quantified over-diagnosis at around 22%.
The authors reached this conclusion:
Many of the small tumors that are excessively detected by screening have a very good prognosis due to their intrinsically slow growth, which means that they are unlikely to become large tumors and are inherently favorable. They are the ones that constitute the over-diagnosis as a direct result of the screening activity. They will not grow enough to be dangerous.
Conversely, large tumors, which are responsible for death and most often have an unfavorable prognosis, are dangerous from the outset. Unfortunately, they escape mammographic detection because their growth kinetics are too rapid.
This theory, that originated from epidemiology a long time ago and which explains why screening does not improve the prognosis of women with breast cancer, is substantiated here.
Procedures, methods, data to be understood
The study concerns only invasive cancers. Lesions are divided into three prognosis groups based on the following biological factors: their grade, the presence of estrogen receptors and progesterone receptors (knowing that tumors with these hormone receptors have a better prognosis).
Twelve combinations are possible based on these three variables, each of these 12 groups having a distinct prognosis.
Four groups of cancers with poor prognosis :
- grade 2, negative receptors
- grade 3, negative receptors
- grade 3, estrogen receptor positive and progesterone receptor negative
- grade 3, estrogen receptor negative and progesterone receptor positive
Three groups of cancers with a good prognosis:
- grade 1, positive receptors
- grade 1, estrogen receptors positive and progesterone receptors negative
- grade 1, estrogen receptors negative and progesterone receptors positive
The other five groups are intermediate prognosis groups.
The authors investigate the correlation of tumor size and biological features of tumors in relation to cancer-specific survival rates.
A "favorable" tumor is one where the biological features presume a good prognosis and an "unfavorable" tumor if otherwise.
The "mean lead time " that enables the quantification of over-diagnosis is the length of time between when a cancer can be detected by mammography and when it would have become clinically apparent without early screening by mammography (adjustment to the age being made).
The fraction of women with a life expectancy less than the lead time represented the percent of overdiagnosis.
(This means that these women will die from causes other than their breast cancer, which would have been unnecessarily detected because it did not endanger them).
I-Biological characteristics of tumors as a function of their size:
The percentages of favorable, intermediate, or unfavorable tumors for each tumor size are examined for women over 40 years of age and for women under 40 years of age.
For women aged 40 and over :
For tumors 1cm and less: 38.2% are "favorable" tumors.
Among the lesions of 1cm and less, only 14.1% are "unfavorable" tumors. On the other hand, among tumors larger than 5 cm, 35.8% are "unfavorable" tumors.
Results for women under 40 years of age:
The favorable tumors were only about half as common and the unfavorable tumors were much more common, for each tumor size examined.
II-Study of specific survival as a function of both biological characteristics and tumor size.
Classification into small tumors for those between 0.1 and 2 cm = T1; and into large tumors for those between 2.1 and 5 cm = T2.
The diagram below shows that both tumor size and biological factors influence the prognosis, but that large tumors with a favorable biological features had a better prognosis than small tumors with an unfavorable biological features.
This means that the difference in survival is less dependent on size than on biological factors, while larger size will be more critical when the biological factors are already unfavorable.
III-Evaluation of over-diagnosis according to the lead time
The approach finds a close link between the over-diagnosis rate and the lead time, by identifying the average lead time most in line with a given frequency of over-diagnosis.
It is important to remember that the lead time is the period of time between the time when cancer could be detected if a mammogram was performed and the time when clinical signs appear if a mammogram is not performed.
When life expectancy is known (estimated according to several factors including age), and when one of the two data, overdiagnosis rate or lead time, is known, it is possible to estimate the one of these two data that is unknown, and this for each of the 12 biological groups listed above.
The lead time, according to all models, is longer for tumors with favorable than unfavorable factors.
Thus, the percentage of overdiagnosis could be estimated at 53% for favorable tumors, 44% for intermediate tumors, 3% for unfavorable ones; in fact there is no evidence that unfavorable tumors do not progress, the small overdiagnosis observed in the unfavorable group is due to deaths occurring in these patients from unrelated causes before the lead time, which is short for these forms.
We find the figure proposed by Welch of 22% over-diagnosis overall, again concerning only invasive forms (not the cancers in situ).
IV-Tumor size may be an indirect indicator of good or poor biological features
If all tumors were progressing we would expect a steady state to be reached in which there would be a similar distribution of tumor biological features across size categories.
There would be favorable and unfavorable forms in similar rates in each size category.
Instead, these data provide fairly direct evidence that many small tumors with favorable biological features do not progress to large tumors over the lifetime of the patient.
Furthermore, the data imply that large tumors do not arise equally from all small tumors but preferentially develop from a distinct subpopulation of small tumors with unfavorable biological features.
The figure below shows the distribution of tumors of the three different prognostic values according to tumor size; on the x-axis the number of patients, on the y-axis the tumor size. The colors correspond to a prognostic value.
The upper part corresponds to women under 40 years of age and the lower part for women over 40 years of age.
There are generally more tumors with good prognosis in the small tumor size category and vice-versa.
Click to enlarge
Reminder of the conclusions, in detail
- Both data: tumor size and biological features impact the prognosis, but a large tumor with favorable biological features is likely to have a better prognosis than a small lesion with unfavorable features. Furthermore, tumor size is more decisive for the prognosis in the category of tumors with unfavorable biological features.
- Many small tumors with favorable biological factors do not progress to a large size during the patient's lifetime. Large tumors do not arise from the small ones, but from a subpopulation of small lesions with pejorative biological factors from the outset. The higher incidence of favorable tumors in women aged 40 and over suggests that these tumors are precisely and preferentially detected by systematic mammography, and that this explains the obviously very favorable survival rates for this detected age group.
- The authors' estimate of over-diagnosis by all biological groups combined is similar to Welch's estimate of 22%, and indicates that the lead time for the most favorable cancers is 19 years (between 10 and 20 years), compared to 2 years or less for unfavorable cancers.
- Lead time: due to a very long lead time in the favorable tumor group, mammography is necessarily very effective for these tumors with a very good prognosis, long time latent, which find themselves over-represented among small tumors. A considerable number of these tumors would never manifest themselves during a woman's lifetime. And those of these less aggressive tumors that are likely to grow, keep this excellent prognosis. Thus their detection by mammography is of little interest but leads to a perception of efficiency and exaggerated survival.
In the most favorable group, which certainly contains the largest proportion of over-diagnosed cancers (grade 1, positive receptors and size < 2cm), the 10-year survival rate is 97%. The proportion of over-diagnosis can be estimated to be at least 50%. (In fact, the smaller the tumor in this group, the greater the likelihood of over-diagnosis. It is therefore not surprising to see these very good survival figures highlighted in communication on screening, Editor’s note).
- Still in this very favorable group, over-diagnosis predominates in older women compared to younger women. Indeed, because of this very long lead time of 15 to 20 years, many of these cancers could have been diagnosed only around the age of 70, but are now detected around 50 to 60 years because of screening. And a large proportion of these non-aggressive and indolent lesions detected by screening in these 70-year-olds may never have been detected during a person's lifetime in the absence of mammography.
- For tumors with unfavorable biological features, the prognosis is considerably better if the detection is early (less than 2cm), unfortunately this is very rarely the case due to their short lead time, so they are often diagnosed late and few of these unfavorable tumors are found in the small tumor group because of their rapid growth.
Early detection is not a universal benefit
====>>>> Low-grade and high-grade malignancies tumors result from different molecular mechanisms; a low-grade tumor almost never dédifferentiates into a high-grade tumor. It seems to be arguable that the biological features of a tumor represent a constant natural factor.
=====>>>> The biggest problem resulting from the over-diagnosis of small, favorable lesions, over-represented in tumors detected by screening, is the over-treatment and anxiety that these unnecessary diagnoses cause.
There is a need to educate physicians, patients, and the general public that some cancers are indolent.
Editor's note: The partial indicators that are the five-year survival and the increase in the proportion of cancers detected at an "early" stage are not relevant criteria for judging the effectiveness of existing diagnostic and therapeutic practices. Once again, only mortality is a relevant indicator.
Sparano, J.A, Gray R et al. Prospective Validation of a 21-Gene Expression Assay in Breast Cancer. The New England Journal of Medicine. 2015;373(21):2005-14. https://www.nejm.org/doi/full/10.1056/NEJMoa1510764
The majority of cancers are diagnosed at an early stage.
However, diagnosing increasingly smaller cancers was not accompanied by any decrease in breast cancer mortality until the 1990s. The prognosis of a cancer varies more according to its molecular characteristics than to its size, whether it is larger or smaller than 2 cm.
Extract from the study :
"In multivariate analysis including age ...., tumor size (2.1 to 5.0 cm vs. ≤ 2 cm in the largest dimension), histological grade (high vs. intermediate vs. low) and type of operation ... only histological grade showed a significant correlation with rate of recurrence."