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AIMI Journal Club: Optimizing Risk-Based Breast Cancer Screening Policies with Reinforcement Learning - Adam Yala, PhD

Event Details:

Thursday, October 27, 2022
3:00pm - 4:00pm PDT

Abstract

Screening programs must balance the benefit of early detection with the cost of overscreening. Here, we introduce a novel reinforcement learning-based framework for personalized screening, Tempo, and demonstrate its efficacy in the context of breast cancer. We trained our risk-based screening policies on a large screening mammography dataset from Massachusetts General Hospital (MGH; USA) and validated this dataset in held-out patients from MGH and external datasets from Emory University (Emory; USA), Karolinska Institute (Karolinska; Sweden) and Chang Gung Memorial Hospital (CGMH; Taiwan). Across all test sets, we find that the Tempo policy combined with an image-based artificial intelligence (AI) risk model is significantly more efficient than current regimens used in clinical practice in terms of simulated early detection per screen frequency. Moreover, we show that the same Tempo policy can be easily adapted to a wide range of possible screening preferences, allowing clinicians to select their desired trade-off between early detection and screening costs without training new policies. Finally, we demonstrate that Tempo policies based on AI-based risk models outperform Tempo policies based on less accurate clinical risk models. Altogether, our results show that pairing AI-based risk models with agile AI-designed screening policies has the potential to improve screening programs by advancing early detection while reducing overscreening.

About

Adam Yala an Assistant Professor of Computational Precision Health and EECS at UC Berkeley and UCSF. His research focuses on developing machine learning methods for personalized medicine and translating them to clinical care.  His previous research has contributed to three areas: 1) predicting future cancer risk, 2) designing personalized screening policies and 3) private data sharing through neural obfuscation. Adam's tools underlie multiple prospective trials and his research has been featured in the Washington Post, New York Times, Boston Globe and Wired. Prof Yala obtained his PhD in Computer Science from MIT and he was a member of MIT Jameel Clinic and MIT CSAIL. 

 

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