Abstract SP080: Hidden clues in the mammogram: How AI can improve early breast cancer detection

Abstract Innovative methods of risk assessment that leverage the strength of Artificial Intelligence (AI) are essential to propel the goals of precision prevention forward. Since the creation of the Gail model in 1989, risk models have supported risk-adjusted screening and prevention, and their continued evolution has been a central pillar of breast cancer research. Prior research has explored multiple risk factors related to hormonal and genetic information. One factor that has received substantial attention is mammographic breast density. Incorporating mammographic breast density into clinically used models such as the Gail and Tyrer-Cuzick risk models significantly improves prediction and discrimination. However, current risk models are limited in that they incorporate only a small fraction of data available on any given patient. Using breast density as a proxy for the detailed information embedded in the mammogram is extremely limited, as breast density assessment is subjective, varies widely across radiologists, and restricts the rich information contained in the digital images to a single crude value. Patients of the same age assigned the same density score can have mammogram images that appear drastically different and can have very different future risk profiles. While previous studies have explored automated methods to assess breast density, these efforts reduce the complex data contained in the mammogram into a few statistics, which are not sufficiently rich to distinguish patients who will and will not develop breast cancer. Deep learning models can operate over full resolution mammogram images to assess a patient’s future breast cancer risk. Rather than manually identifying discriminative image patterns, machine learning models can discover these patterns directly from the data. Specifically, models are trained with full resolution mammograms and the outcome of interest, namely whether the patient developed breast cancer within five years from the date of the examination. Our recent work demonstrates that application of novel artificial intelligence applications to imaging data can significantly improve breast cancer risk prediction. In addition, unlike traditional models, our DL model performs equally well across varied races, ages, and family histories and we have built a clinical platform which is currently in use to support implementation of our risk model into clinical care. The COVID-19 pandemic has revealed severe inequities in healthcare while providing opportunities for essential reform. In breast cancer care, preliminary, conservative estimates predict the disruption of breast cancer screening due to the COVID-19 pandemic will result in a significant upward stage shift of cancers diagnosed and more than 5,000 breast cancer deaths in the U.S. alone. Due to severely limited healthcare resources during pandemics, and to protect patients and healthcare workers, state governments urge providers to focus cancer screening efforts on those patients at higher risk. These mandates are necessary responses to support fair allocation of scarce resources to maximize benefits for all patients across the full spectrum of healthcare needs. AI-based breast cancer risk models have the potential to support more effective and more equitable mammographic screening for breast cancer during these times of severely restricted access to screening. ROC Area Under the Curve Analyses of Traditional vs AI Risk Models Risk ModelTyrer-Cuzick version 8 AUCAI Image Only AUCRaceAfrican American0.58 (0.39, 0.79)0.74 (0.60, 0.90)Asian0.53 (0.35, 0.74)0.79 (0.68, 0.94)White0.64 (0.60, 0.68)0.77 (0.73, 0.80)Age&amp;lt;500.65 (0.57, 0.72)0.75 (0.68, 0.82)50-700.64 (0.60, 0.69)0.76 (0.72, 0.79)&amp;gt;700.52 (0.43, 0.60)0.77 (0.70, 0.84)DensityNon-Dense0.63 (0.58, 0.68)0.77 (0.73, 0.81)Dense0.63 (0.58, 0.69)0.77 (0.73, 0.81) Citation Format: C Lehman, A Yala, L Lamb, R Barzilay. Hidden clues in the mammogram: How AI can improve early breast cancer detection [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr SP080.