Adaptive beast MRI scanning using AI Journal Article
| Authors: | Eskreis-Winkler, S.; Bhowmik, A.; Kelly, L. H.; Lo Gullo, R.; D'Alessio, D.; Belen, K.; Hogan, M. P.; Saphier, N. B.; Sevilimedu, V.; Sung, J. S.; Comstock, C. E.; Sutton, E. J.; Pinker, K. |
| Article Title: | Adaptive beast MRI scanning using AI |
| Abstract: | Background: MRI protocols typically involve many imaging sequences and often require too much time. Purpose: To simulate artificial intelligence (AI)–directed stratified scanning for screening breast MRI with various triage thresholds and evaluate its diagnostic performance against that of the full breast MRI protocol. Materials and Methods: This retrospective reader study included consecutive contrast-enhanced screening breast MRI examinations performed between January 2013 and January 2019 at three regional cancer sites. In this simulation study, an in-house AI tool generated a suspicion score for subtraction maximum intensity projection images during a given MRI examination, and the score was used to determine whether to proceed with the full MRI protocol or end the examination early (abbreviated breast MRI [AB-MRI] protocol). Examinations with suspicion scores under the 50th percentile were read using both the AB-MRI protocol (ie, dynamic contrast-enhanced MRI scans only) and the full MRI protocol. Diagnostic performance metrics for screening with various AI triage thresholds were compared with those for screening without AI triage. Results: Of 863 women (mean age, 52 years ± 10 [SD]; 1423 MRI examinations), 51 received a cancer diagnosis within 12 months of screening. The diagnostic performance metrics for AI-directed stratified scanning that triaged 50% of examinations to AB-MRI versus full MRI protocol scanning were as follows: sensitivity, 88.2% (45 of 51; 95% CI: 79.4, 97.1) versus 86.3% (44 of 51; 95% CI: 76.8, 95.7); specificity, 80.8% (1108 of 1372; 95% CI: 78.7, 82.8) versus 81.4% (1117 of 1372; 95% CI: 79.4, 83.5); positive predictive value 3 (ie, percent of biopsies yielding cancer), 23.6% (43 of 182; 95% CI: 17.5, 29.8) versus 24.7% (42 of 170; 95% CI: 18.2, 31.2); cancer detection rate (per 1000 examinations), 31.6 (95% CI: 22.5, 40.7) versus 30.9 (95% CI: 21.9, 39.9); and interval cancer rate (per 1000 examinations), 4.2 (95% CI: 0.9, 7.6) versus 4.9 (95% CI: 1.3, 8.6). Specificity decreased by no more than 2.7 percentage points with AI triage. There were no AI-triaged examinations for which conducting the full MRI protocol would have resulted in additional cancer detection. Conclusion: AI-directed stratified MRI decreased simulated scan times while maintaining diagnostic performance. © RSNA, 2025. |
| Keywords: | adult; controlled study; aged; middle aged; retrospective studies; major clinical study; nuclear magnetic resonance imaging; follow up; magnetic resonance imaging; cancer diagnosis; diagnostic accuracy; sensitivity and specificity; breast cancer; image interpretation, computer-assisted; breast; cancer screening; diagnostic imaging; breast neoplasms; retrospective study; simulation; computer assisted diagnosis; artificial intelligence; breast tumor; contrast medium; contrast media; menopause; dynamic contrast-enhanced magnetic resonance imaging; ethnicity; predictive value; race; protocol; breast magnetic resonance imaging; diagnostic test accuracy study; triage; procedures; breast imaging reporting and data system; humans; human; female; article; patient triage |
| Journal Title: | Radiology |
| Volume: | 315 |
| Issue: | 3 |
| ISSN: | 0033-8419 |
| Publisher: | Radiological Society of North America, Inc. |
| Date Published: | 2025-06-01 |
| Start Page: | e242924 |
| Language: | English |
| DOI: | 10.1148/radiol.242924 |
| PUBMED: | 40459419 |
| PROVIDER: | scopus |
| PMCID: | PMC12207643 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PDF -- MSK corresponding author is Sarah Eskreis-Winkler -- Source: Scopus |
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