High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI Journal Article
| Authors: | Hirsch, L.; Sutton, E. J.; Huang, Y.; Kayis, B.; Hughes, M.; Martinez, D.; Makse, H. A.; Parra, L. C. |
| Article Title: | High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI |
| Abstract: | Purpose: To develop and evaluate an open-source deep learning model for detection and localization of breast cancer on MRI scans. Materials and Methods: In this retrospective study, a deep learning model for breast cancer detection and localization was trained on the largest breast MRI dataset to date. Data included all breast MRI examinations conducted at a tertiary cancer center in the United States between 2002 and 2019. The model was validated on sagittal MRI scans from the primary site (n = 6615 breasts). Generalizability was assessed by evaluating model performance on axial data from the primary site (n = 7058 breasts) and a second clinical site (n = 1840 breasts). Results: The primary site dataset included 30 672 sagittal MRI examinations (52 598 breasts) from 9986 female patients (mean age, 52.1 years +/- 11.2 [SD]). The model achieved an area under the receiver operating characteristic curve of 0.95 for detecting cancer in the primary site. At 90% specificity (5717 of 6353), model sensitivity was 83% (217 of 262), which was comparable to historical performance data for radiologists. The model generalized well to axial examinations, achieving an area under the receiver operating characteristic curve of 0.92 on data from the same clinical site and 0.92 on data from a secondary site. The model accurately located the tumor in 88.5% (232 of 262) of sagittal images, 92.8% (272 of 293) of axial images from the primary site, and 87.7% (807 of 920) of secondary site axial images. Conclusion: The model demonstrated state-of-the-art performance on breast cancer detection. Code and weights are openly available to stimulate further development and validation. |
| Keywords: | chemotherapy; breast; mri; tissue; follow-up; computer-aided diagnosis (cad); neural networks; deep; ensemble |
| Journal Title: | Radiology: Artificial Intelligence |
| Volume: | 7 |
| Issue: | 5 |
| ISSN: | 2638-6100 |
| Publisher: | Radiological Society of North America, Inc. |
| Date Published: | 2025-09-01 |
| Language: | English |
| ACCESSION: | WOS:001551689000001 |
| DOI: | 10.1148/ryai.240550 |
| PROVIDER: | wos |
| PUBMED: | 40560044 |
| Notes: | Article -- e240550 -- Source: Wos |
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