Optimizing risk-based breast cancer screening policies with reinforcement learning
Name
Optimizing_risk-based_breast_cancer_screening_poli-1.pdf
Description
Accepted version
Size
3.49 MB
Format
Adobe PDF
Checksum (MD5)
dc7c07d10a67e76b55320efa9fea5e9f
Author(s)
Yala, Adam
Mikhael, Peter G
Lehman, Constance
Lin, Gigin
Strand, Fredrik
Wan, Yung-Liang
Hughes, Kevin
Satuluru, Siddharth
Kim, Thomas
Banerjee, Imon
Date Issued
January 2022
Journal
Nature Medicine
Publisher
Springer Science and Business Media LLC
Citation
Yala, Adam, Mikhael, Peter G, Lehman, Constance, Lin, Gigin, Strand, Fredrik et al. 2022. "Optimizing risk-based breast cancer screening policies with reinforcement learning." Nature Medicine, 28 (1).
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41591-021-01599-w
