Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Abstract

Background New biomarkers of risk may improve breast cancer risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images (WSIs) into epithelium, fibrous stroma, and fat. We applied our method to the BBD breast cancer nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of breast cancer. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 WSIs from 293 cases who developed breast cancer and 1132 controls who did not. Percentages of each tissue region were calculated and 615 morphometric features were extracted. Elastic net regression was used to create a breast cancer morphometric signature. Associations between breast cancer risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and BMI evaluated the relationship between tissue composition and breast cancer risk. Results Among controls, BBD subtypes, parity, and number of births were differentially associated with all three tissue regions (p<0.05); select regions were associated with childhood body size, BMI, age of menarche, and menopausal status (p<0.05). Higher proportion of epithelial tissue was associated with increased breast cancer risk (OR=1.39, 95% CI 0.91-2.14 comparing highest and lowest quartiles; p-trend<0.05). No morphometric signature was associated with breast cancer. Conclusion The amount of epithelial tissue may be incorporated into risk assessment models to improve breast cancer risk prediction.

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals

“…We previously investigated the association of automated breast tissue composition and BC risk in the Nurses’ Health Studies (28). Women in the highest quartile for % epithelium had higher BC risk compared to women in the lowest quartile; there was no relationship between % fibrous stroma and BC risk (28). Taken together, this current study now provides quantitative histological evidence to support prior epidemiological reports that TT may reduce BC risk (3–5).…”

“…There was no relationship between TT and mammographic breast density. We previously investigated the association of automated breast tissue composition and BC risk in the Nurses' Health Studies (28). Women in the highest quartile for % epithelium had higher BC risk compared to women in the lowest quartile; there was no relationship between % fibrous stroma and BC risk (28).…”

“…High mammographic breast density is a strong BC risk factor for cisgender women (25). We previously developed a computational pathology algorithm to assess non-cancer breast tissue composition (26)(27)(28)(29). Using our algorithm, women with more breast epithelium (highest quartile) had higher subsequent BC risk compared with women in the lowest quartile (28).…”

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals

“…We previously developed a deep-learning algorithm to segment breast histological images into epithelium, fibrous stroma, and fat [ 28 ]. As this algorithm was not developed using images containing nipple-areolar complex or skin, it was likely to erroneously classify pixels containing nipple-areolar complex or skin as breast epithelium.…”

“…We previously developed a computational pathology algorithm to assess non-cancer breast tissue composition [ 26 – 29 ]. Using our algorithm, women with more breast epithelium (highest quartile) had higher subsequent BC risk compared with women in the lowest quartile [ 28 ]. Hormone-related BC risk factors such as reproductive and early body weight were also linked to changes in breast tissue composition [ 26 , 27 ].…”