A South Korean retrospective study evaluated Lunit INSIGHT MMG (Lunit), an AI tool for breast cancer detection on mammograms, using mammograms of 5707 asymptomatic women aged ≥40 with dense breasts (including 33 cancer cases). The study compared the cancer detection rate (CDR), sensitivity, specificity, and abnormal interpretation rate (AIR) of mammographs interpreted by 5 radiologists with and without AI assistance, and mammography combined with supplemental ultrasound (US).
The assistance of the AI tool led to an improved specificity of 95.3% and a reduced abnormal interpretation rate (AIR) of 5.0%, compared to 94.3% specificity and 6.0% AIR without AI. The cancer detection rate (CDR) was slightly higher with AI (3.5 per 1000 examinations) than without AI (3.3 per 1000 examinations). However, adding supplemental US further increased the CDR to 5.6 per 1000 examinations and sensitivity to 97.0%, despite a lower specificity (77.6%) and a higher AIR (22.9%). Supplemental US detected 12 additional cancers that were missed by both radiologists and AI. While AI assistance improves certain aspects of mammography interpretation, it does not outperform mammography combined with supplemental ultrasound in detecting breast cancer in women with dense breasts.
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Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US
Radiology, 2024
Abstract
Background
Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography remains unclear.
Purpose
To compare the performance of mammography alone, mammography with AI, and mammography plus supplemental US for screening women with dense breasts, and to investigate the characteristics of the detected cancers.
Materials and Methods
A retrospective database search identified consecutive asymptomatic women (≥40 years of age) with dense breasts who underwent mammography plus supplemental whole-breast handheld US from January 2017 to December 2018 at a primary health care center. Sequential reading for mammography alone and mammography with the aid of an AI system was conducted by five breast radiologists, and their recall decisions were recorded. Results of the combined mammography and US examinations were collected from the database. A dedicated breast radiologist reviewed marks for mammography alone or with AI to confirm lesion identification. The reference standard was histologic examination and 1-year follow-up data. The cancer detection rate (CDR) per 1000 screening examinations, sensitivity, specificity, and abnormal interpretation rate (AIR) of mammography alone, mammography with AI, and mammography plus US were compared.
Results
Among 5707 asymptomatic women (mean age, 52.4 years ± 7.9 [SD]), 33 (0.6%) had cancer (median lesion size, 0.7 cm). Mammography with AI had a higher specificity (95.3% [95% CI: 94.7, 95.8], P = .003) and lower AIR (5.0% [95% CI: 4.5, 5.6], P = .004) than mammography alone (94.3% [95% CI: 93.6, 94.8] and 6.0% [95% CI: 5.4, 6.7], respectively). Mammography plus US had a higher CDR (5.6 vs 3.5 per 1000 examinations, P = .002) and sensitivity (97.0% vs 60.6%, P = .002) but lower specificity (77.6% vs 95.3%, P < .001) and higher AIR (22.9% vs 5.0%, P < .001) than mammography with AI. Supplemental US alone helped detect 12 cancers, mostly stage 0 and I (92%, 11 of 12).
Conclusion
Although AI improved the specificity of mammography interpretation, mammography plus supplemental US helped detect more node-negative early breast cancers that were undetected using mammography with AI.