Academic radiology · 2025
Clinical Application of AI in Mammography: Insights from a Prospective Study.
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Yilmaz Ebru, Seker Mustafa Ege, Guldogan Nilgun, Turk Ebru Banu, Erdemli Servet, Koyluoglu Yilmaz Onat, Sancak Sehla Nurefsan, Aribal Erkin
Аннотация
RATIONALE AND OBJECTIVES: This prospective study evaluated the performance of AI in a diagnostic clinic setting, comparing its effectiveness with radiologists of varying experience.
MATERIALS AND METHODS: The study was conducted at a single center and included 1063 patients undergoing diagnostic or screening mammography. Five radiologists with different experience levels assessed the images using the fifth edition of the BI-RADS lexicon. Standalone AI software assigned risk scores (0-100), with scores above 30.44 considered positive. AI risk assessments were compared with radiologists' BI-RADS scores. Radiologists also re-evaluated AI-positive mammograms as a second look. Ground truth was established through histopathology and two years of follow-up.
RESULTS: Right and left breasts were analyzed separately, and 2126 mammography images were evaluated from 1063 women. A total of 29 cancers were diagnosed in 28 women. Among all examinations, 2.44% (52/2126) were positive, of which 46.15% (24/52) were true positive. Standalone AI detected 82.75% (24/29) of cancers, and the majority voting of radiologists scored positive (BI-RADS 0,4 and 5) in 8% (172/2126) where 89.65% (26/29) of cancers were detected. The AUC score of majority voting was 94.7% (95% CI: 91.1-98.3), and AI was 94.4% (95% CI: 88.5-100). AI was statistically not significantly different than (p=0.79) AUC of the majority voting. The re-evaluation assessment of AI-flagged images achieved an AUC of 94.8% (95% CI: 91.2-98.3), significantly different from the initial evaluation (p=0.015). However, it was not significantly different from AI (p=0.74).
CONCLUSION: AI algorithms in diagnostic settings can serve as effective CAD systems, aiding in breast cancer detection and reducing inter-reader variability.
DATA AVAILABILITY: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
MATERIALS AND METHODS: The study was conducted at a single center and included 1063 patients undergoing diagnostic or screening mammography. Five radiologists with different experience levels assessed the images using the fifth edition of the BI-RADS lexicon. Standalone AI software assigned risk scores (0-100), with scores above 30.44 considered positive. AI risk assessments were compared with radiologists' BI-RADS scores. Radiologists also re-evaluated AI-positive mammograms as a second look. Ground truth was established through histopathology and two years of follow-up.
RESULTS: Right and left breasts were analyzed separately, and 2126 mammography images were evaluated from 1063 women. A total of 29 cancers were diagnosed in 28 women. Among all examinations, 2.44% (52/2126) were positive, of which 46.15% (24/52) were true positive. Standalone AI detected 82.75% (24/29) of cancers, and the majority voting of radiologists scored positive (BI-RADS 0,4 and 5) in 8% (172/2126) where 89.65% (26/29) of cancers were detected. The AUC score of majority voting was 94.7% (95% CI: 91.1-98.3), and AI was 94.4% (95% CI: 88.5-100). AI was statistically not significantly different than (p=0.79) AUC of the majority voting. The re-evaluation assessment of AI-flagged images achieved an AUC of 94.8% (95% CI: 91.2-98.3), significantly different from the initial evaluation (p=0.015). However, it was not significantly different from AI (p=0.74).
CONCLUSION: AI algorithms in diagnostic settings can serve as effective CAD systems, aiding in breast cancer detection and reducing inter-reader variability.
DATA AVAILABILITY: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.