Abstract LB161: Whole mammogram image improves breast cancer prediction

Abstract

Abstract To efficiently capture data from mammographic breast images and classify long term risk of breast cancer, we developed methods that use the extensive existing data that are currently ignored in the context of breast cancer risk stratification. More than 20 studies support texture features add value to risk prediction beyond breast density. However, the entire mammogram imaging data has a high dimension of pixels (~13 million per image), greatly exceeding the number of women in a cohort. We apply functional principal component analysis methods to predict 5-years breast cancer incidence using baseline mammograms. We applied these methods onto women participating in the Joanne Knight Breast Health Cohort which is comprised of over 10,000 women undergoing repeated mammography screening at Siteman Cancer Center and followed since 2010. All women had baseline mammogram at entry, provided a blood sample and completed a risk factor questionnaire. Mammograms are all using the same technology (Hologic). During follow-up through October 2020, we identified 246 incident breast cancer cases (pathology confirmed) and matched them to controls from the perspective cohort based on month of mammogram and age at entry. In a baseline model we controlled for age, menopause, BMI, and mammographic breast density (BIRADs). We then added the full image (characterized by the FPC) to the base model and further compared the AUC of the new model vs the base model using the likelihood ratio test. AUC is validated with internal 10-fold cross validation. The AUC for 5-year breast cancer risk classification increased significantly from a median of 0.61 (sd 0.09 for estimated AUCs across 10-fold internal validation) for the baseline model to 0.70 (0.10) when the full image is added, p < 0.001. We conclude that using full mammogram images for breast cancer risk prediction captures additional information on breast tissue characteristics that relate to cancer risk, and improves prediction classification. This prediction algorithm can run efficiently in real time (in seconds) with processing of digital mammograms. Thus, this model can be easily implemented in mammography screening services and other clinical settings to guide real-time risk stratification to improve precision prevention of the leading cancer in women world-wide. Further analysis will quantify the value of adding other breast cancer risk factors, including polygenic risk scores. Addition of repeated mammogram images over time should further increase classification performance. This approach has the potential to improve risk classification by using data already available for the vast majority of women already having repeated screening mammograms. Citation Format: Shu Jiang, Graham A. Colditz. Whole mammogram image improves breast cancer prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB161.