BACKGROUND: Manual qualitative and quantitative measures of terminal duct lobular unit (TDLU) involution were previously reported to be inversely associated with breast cancer risk. We developed and applied a deep learning method to yield quantitative measures of TDLU involution in normal breast tissue. We assessed the associations of these automated measures with breast cancer risk factors and risk.
METHODS: We obtained eight quantitative measures from whole slide images from a benign breast disease (BBD) nested case-control study within the Nurses' Health Studies (287 breast cancer cases and 1,083 controls). Qualitative assessments of TDLU involution were available for 177 cases and 857 controls. The associations between risk factors and quantitative measures among controls were assessed using analysis of covariance adjusting for age. The relationship between each measure and risk was evaluated using unconditional logistic regression, adjusting for the matching factors, BBD subtypes, parity, and menopausal status. Qualitative measures and breast cancer risk were evaluated accounting for matching factors and BBD subtypes.
RESULTS: Menopausal status and parity were significantly associated with all eight measures; select TDLU measures were associated with BBD histologic subtype, body mass index, and birth index (P < 0.05). No measure was correlated with body size at ages 5-10 years, age at menarche, age at first birth, or breastfeeding history (P > 0.05). Neither quantitative nor qualitative measures were associated with breast cancer risk.
CONCLUSIONS: Among Nurses' Health Studies women diagnosed with BBD, TDLU involution is not a biomarker of subsequent breast cancer.
IMPACT: TDLU involution may not impact breast cancer risk as previously thought.
Bibliographical note©2020 American Association for Cancer Research.
M. Veta reports grants from Netherlands Organisation for Scientific Research and Philips Research during the conduct of the study. A.H. Eliassen reports grants from NIH during the conduct of the study. R.M. Tamimi reports grants from NIH/NCI during the conduct of the study. No potential conflicts of interest were disclosed by the other authors. This work was supported by the NIH/NCI R21 CA187642 (to R.M. Tamimi), R01 CA175080 (to R.M. Tamimi), UM1 CA186107 (to A.H. Eliassen), and U01 176726 (to A.H. Eliassen), Susan G. Komen for the Cure IIR13264020 (to R.M. Tamimi), the Klarman Family Foundation (to Y.J. Heng), BIDMC High School Summer Research Program (to E.Z.F. Liu), and the Deep Learning for Medical Image Analysis research program by Netherlands Organization for Scientific Research P15-26 (to S.-C. Wetstein, M. Veta, and J.P.W. Pluim) and Philips Research (to S.C. Wetstein, M. Veta, and J.P.W. Pluim).
|BIDMC High School Summer Research Program
|Netherlands Organisation for Scientific Research and Philips Research
|National Institutes of Health
|National Cancer Institute
|U01CA176726, R01 CA175080, U01 176726, R01CA050385, K99CA245900, R21 CA187642, UM1 CA186107
|Klarman Family Foundation
|Nederlandse Organisatie voor Wetenschappelijk Onderzoek