# Toward robust mammography-based models for breast cancer risk

Source: https://onco.cc/key-papers/paper-yala-sci-transl-med/  
OnCo record `paper-yala-sci-transl-med` (Key paper). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Paper cited by one technology page, indexed on Europe PMC as PubMed record 33504648 and published in Science Translational Medicine; the citing page links this DOI, which is how the record was matched.

## Summary

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model ( P < 0.001) and prior deep learning models Hybrid DL ( P < 0.001) and Image-Only DL ( P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL ( P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model ( P < 0.001).

Indexed on Europe PMC as PubMed record 33504648 (DOI 10.1126/scitranslmed.aba4373). Matched by DOI alone: one technology page cites this DOI among its external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor.

## Fields

- Kind: Key paper
- Last checked: 2026-09-22
- Tags: europepmc-ingest
- Journal: Science Translational Medicine
- Year: 2021
- DOI: 10.1126/scitranslmed.aba4373
- Authors: Yala A, Mikhael PG, Strand F, et al.
- What it means: One technology page on OnCo cites this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing page listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
- Caveats: Matched to the citing OnCo records by DOI alone; the summary reproduces the Europe PMC abstract and no figure has been verified against the full paper.

## Sources

- Sci Transl Med 2021: https://doi.org/10.1126/scitranslmed.aba4373
- PubMed: https://pubmed.ncbi.nlm.nih.gov/33504648/
- Europe PMC: https://europepmc.org/article/MED/33504648

## Connected records

- technologies: [Mirai (MIT breast cancer risk from mammograms)](https://onco.cc/technologies/mirai/)
- journals: [Science Translational Medicine](https://onco.cc/journals/science-translational-medicine/)

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