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Optimizing risk-based breast cancer screening policies with reinforcement learning


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.

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Fig. 1: Retrospective patient trajectory from the MGH test set compared with recommended trajectories by different guidelines.
Fig. 2: Overview of Tempo.
Fig. 3: Early detection versus the number of mammograms per year at MGH, Emory, Karolinska and CGMH.
Fig. 4: Histogram of screening frequency for each screening guideline on MGH, Emory, Karolinska and CGMH test sets.

Data availability

All datasets were used under license to the respective hospital system for the current study and are not publicly available. To access the MGH dataset, investigators should contact C.L. to apply for an IRB-approved research collaboration and obtain an appropriate data use agreement. To access the Karolinska dataset, investigators should contact F.S. to apply for an approved research collaboration and sign a data use agreement. To access the CGMH dataset, investigators should contact G.L. to apply for an IRB-approved research collaboration. To access the Emory dataset, investigators should contact H.T. to apply for an approved collaboration.

Code availability

All models and code used for training, evaluating and developing Tempo are publicly available at and (


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This work was supported by grants from Susan G. Komen, the Breast Cancer Research Foundation, Quanta Computer, Anonymous Foundation and the MIT Jameel Clinic. This work was also supported by the Chang Gung Medical Foundation (grant SMRPG3K0051) and Stockholm Läns Landsting HMT (grant 201708002). We are grateful to the Cancer Center of Linkou CGMH for assistance with data collection under IRB no. 201901491B0C601 and R. Yang, J. Song and their team (Quanta Computer) for providing technical and computing support for analyzing the CGMH dataset.

Author information

Authors and Affiliations



A.Y. and R.B. designed the research goals and aims. A.Y. and R.B. designed the model. A.Y. and R.B. designed the evaluation methodology. A.Y. wrote the software. C.L., G.L., F.S., Y.W., S.S., T.K., I.B., J.G. and H.T. curated the datasets. A.Y. and P.G.M. performed the analysis. P.G.M. created the visualizations. All authors contributed to manuscript writing. R.B. supervised the project.

Corresponding author

Correspondence to Adam Yala.

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The authors declare no competing interests.

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Nature Medicine thanks William Lotter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Estimated (circle) and observed (square) Mirai 5-year risk for two random patients in the MGH test set.

We estimated unobserved risk observations using an RNN, which was optimized to predict future risk assessments from past risk assessments on the MGH training set.

Extended Data Fig. 2 Histograms of early detection in months for Tempo-Mirai.

Histogram of early detection benefit in months relative to historical screening for patients who developed cancer in the MGH (top left), Emory (top right), Karolinska (bottom left), and CGMH (bottom right) test sets.

Extended Data Fig. 3 Histogram of screening recommendations for each screening policy.

MGH (top left), Emory (top right), Karolinska (bottom left), CGMH (bottom right).

Extended Data Fig. 4 Our early detection metric assumed that a cancer could be caught up to 18 months before diagnosis.

To test the robustness of our results to this assumption, we also evaluated our screening policies when changing this assumption to 6 months, 12 months and 24 months. For each policy, we report its screening efficiency, which is defined as its early detection benefit in months divided by the amount of mammograms it recommends per year. Asterisk denotes the policy with the highest screening efficiency.

Extended Data Fig. 5 Dataset construction flowcharts.

Dataset construction flow chart for the MGH dataset (top left), Emory (top right), Karolinska test set (bottom left), and CGMH test set (bottom right).

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Supplementary Tables 1–9.

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Yala, A., Mikhael, P.G., Lehman, C. et al. Optimizing risk-based breast cancer screening policies with reinforcement learning. Nat Med 28, 136–143 (2022).

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