ArticleJournal of imaging informatics in medicine2026
Factors Associated with False-Positive Marks and False-Negative Cancers on Automated Breast Ultrasound Using an Artificial Intelligence-Based Computer-Aided Detection System.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Artificial intelligence-based computer-aided detection (AI-CAD) systems have been developed to assist radiologists in interpreting automated breast ultrasound (ABUS), but factors associated with false-negative (FN) cancers and false-positive (FP) AI-CAD findings remain unclear. This single-center retrospective study evaluated stand-alone AI-CAD performance in ABUS and identified factors associated with FN cancers and FP findings. The study included 338 women with final negative screening ABUS results and 97 women with screening-detected breast cancer. AI-CAD sensitivity and specificity were evaluated. Characteristics of cancers with and without AI-CAD markers were compared, and patient-level logistic regression and lesion-level generalized estimating equations were used to identify factors associated with FP results and pseudolesions. AI-CAD detected 72 of 97 cancers, yielding a sensitivity of 74.2% and specificity of 57.7%. FN cancers were more often nonmass lesions (20.0% vs. 4.2%, P = 0.025), isoechoic masses (55.0% vs. 10.1%, P < 0.001), and masses without an echogenic rind (60.0% vs. 34.8%, P = 0.043) compared with AI-CAD-detected cancers. Age (odds ratio [OR], 0.94; 95% confidence interval [CI], 0.91-0.97; P < 0.001) and heterogeneous background echotexture (OR, 1.75; 95% CI, 1.09-2.82; P = 0.022) were associated with patient-level FP results. At the lesion level, dense breasts (OR, 2.02; 95% CI, 1.14-3.59; P = 0.017), heterogeneous background echotexture (OR, 2.30; 95% CI, 1.50-3.53; P < 0.001), posterior location (OR, 2.07; 95% CI, 1.15-3.70; P = 0.015), detection on the opposite side of the scanned view (OR, 2.47; 95% CI, 1.51-4.03; P <0.001), and number of detected views (OR, 0.06; 95% CI, 0.03-0.12; P < 0.001) were associated with pseudolesions. Specific lesion and imaging features are associated with FN cancers and FP AI-CAD findings on ABUS, suggesting that AI-CAD should be used as an adjunctive tool rather than as an independent screening reader.
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