Automated Quantitative Measures of Terminal Duct Lobular Unit Involution and Breast Cancer Risk
Year: 2020
Type: article
Abstract: 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: > 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.
Authors: Kevin H. Kensler, Emily Z.F. Liu, Suzanne C. Wetstein, Allison M. Onken, Christina I. Luffman, Gabrielle M. Baker, Laura C. Collins, Stuart J. Schnitt, Vanessa C. Bret-Mounet, Mitko Veta, Josien P.W. Pluim, Ying Liu, Graham A. Colditz, A. Heather Eliassen, Susan E. Hankinson, Rulla M. Tamimi, Yujing J. Heng
Institutions: Dana-Farber Cancer Institute, Beth Israel Deaconess Medical Center, Harvard University, Eindhoven University of Technology, Brigham and Women's Hospital, Dana-Farber Brigham Cancer Center, Washington University in St. Louis, Alvin J. Siteman Cancer Center, University of Massachusetts Amherst, Weill Cornell Medicine
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1195446685, G2214254730, G294025628, Contributions of tumor molecular subtypes to prostate cancer racial disparities, G4417598090, Computational pathology to predict breast cancer risk in benign breast disease, G6069753585, Risk Factors for Breast Cancer in Younger Nurses, G9000099730, G9000246629, G978275231
OpenAlex ID: W3086116609