To enable targeted personalized screening, we need to advance cancer risk models. Despite significant research on this topic, statistical models used today in clinical practice routinely undeperform. In this talk, I explore the potential of AI-based models for breast cancer prediction based on mammographic images. These models have already shown significantt performance gains over their counterparts. However, to bring deep learning models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. To this end, we propose Mirai, a new risk algorithm designed to predict risk at multiple time points, leverage potentially missing risk-factor information, and produce predictions that are consistent across mammography machines. The architectture of the new model will be covered in detail in the talk. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the US and was tested on held-out test sets from MGH, Karolinska in Sweden and Chang Gung Memorial Hospital in Taiwan, obtaining C-indices of 0.76 (95% CI 0.74, 0.80), 0.81 (0.79, 0.82), 0.79 (0.79, 0.83), respectively. Mirai obtained significantly higher five-year ROC AUCs than the Tyrer-Cuzick model (p<0.001) and prior deep learning models, Hybrid DL (p<0.001) and ImageOnly DL (p<0.001), trained on the same MGH dataset.

Citation Format: Regina Barzilay, Adam Yala. Towards robust image based models for cancer risk assessment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-24.