Early detection of breast cancer in MRI using AI Journal Article
| Authors: | Hirsch, L.; Huang, Y.; Makse, H. A.; Martinez, D. F.; Hughes, M.; Eskreis-Winkler, S.; Pinker, K.; Morris, E. A.; Parra, L. C.; Sutton, E. J. |
| Article Title: | Early detection of breast cancer in MRI using AI |
| Abstract: | Rationale and Objectives: To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women. Materials and Methods: A convolutional neural network (CNN) AI model, pre-trained on breast MRI data, was fine-tuned using a retrospective dataset of 3029 MRI scans from 910 patients. These contained 115 cancers that were diagnosed within one year of a negative MRI. The model aimed to identify these cancers, with the goal of predicting cancer development up to one year in advance. The network was fine-tuned and tested with 10-fold cross-validation. Mean age of patients was 52 years (range, 18–88 years), with average follow-up of 4.3 years (range 1–12 years). Results: The AI detected cancers one year earlier with an area under the ROC curve of 0.72 (0.67–0.76). Retrospective analysis by a radiologist of the top 10% highest risk MRIs as ranked by the AI could have increased early detection by up to 30%. (35/115, CI:22.2–39.7%, 30% sensitivity). A radiologist identified a visual correlate to biopsy-proven cancers in 83 of prior-year MRIs (83/115, CI: 62.1–79.4%). The AI algorithm identified the anatomic region where cancer would be detected in 66 cases (66/115, CI:47.8–66.5%); with both agreeing in 54 cases (54/115, CI:%37.5–56.4%). Conclusion: This novel AI-aided re-evaluation of “benign” breasts shows promise for improving early breast cancer detection with MRI. As datasets grow and image quality improves, this approach is expected to become even more impactful. © 2024 The Association of University Radiologists |
| Keywords: | adolescent; adult; aged; aged, 80 and over; middle aged; retrospective studies; young adult; major clinical study; cancer localization; nuclear magnetic resonance imaging; follow up; magnetic resonance imaging; sensitivity and specificity; breast cancer; image interpretation, computer-assisted; diagnostic imaging; breast neoplasms; retrospective study; algorithms; radiologist; algorithm; computer assisted diagnosis; breast tumor; high risk population; early detection of cancer; early detection; cost benefit analysis; breast biopsy; artificial neural network; breast magnetic resonance imaging; procedures; very elderly; humans; human; female; article; deep learning; convolutional neural network; early cancer diagnosis; cross validation; neural networks, computer |
| Journal Title: | Academic Radiology |
| Volume: | 32 |
| Issue: | 3 |
| ISSN: | 1076-6332 |
| Publisher: | Elsevier Science, Inc. |
| Date Published: | 2025-03-01 |
| Start Page: | 1218 |
| End Page: | 1225 |
| Language: | English |
| DOI: | 10.1016/j.acra.2024.10.014 |
| PUBMED: | 39482209 |
| PROVIDER: | scopus |
| PMCID: | PMC11875922 |
| DOI/URL: | |
| Notes: | Source: Scopus |
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69Sutton -
42Martinez -
213Pinker-Domenig
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