Added value of diffusion-weighted imaging in detecting breast cancer missed by artificial intelligence-based...

To evaluate breast cancers missed by artificial intelligence-based computer-aided diagnosis (AI-CAD) in women newly diagnosed with breast cancer, identify factors associated with these missed cases, and assess the potential diagnostic value of standalone diffusion-weighted imaging (DWI) in detecting cancers overlooked by AI-CAD.

This retrospective study included 414 women (mean age, 55.3 years) with pathologically confirmed breast cancer who underwent preoperative mammography, MRI with DWI, and surgery. Cancers were classified as AI-detected if the lesion had an abnormality score greater than 10 and was correctly localized by AI-CAD; otherwise, they were categorized as AI-missed. Clinicopathologic and imaging features were compared between groups. Two radiologists independently reviewed DWI of AI-missed cancers and assigned malignancy confidence scores using a 6-point Likert-type scale (≥3 considered positive). Interobserver agreement and diagnostic performance were analyzed.

AI-CAD missed 127 of 414 breast cancers (30.7%). Multivariate regression analysis identified dense breasts (adjusted OR = 1.619; p = 0.049) and tumor size ≤ 2 cm (adjusted OR = 4.698; p < 0.001) as independent predictors of AI-missed cancer. Standalone DWI detected 83.5% and 79.5% of AI-missed cancers for Radiologists 1 and 2, respectively, with substantial agreement (κ = 0.61). DWI was effective in detecting mammographically occult or >1 cm tumors, but sensitivity declined for subcentimeter lesions.

Standalone DWI detects the majority of breast cancers missed by AI-CAD, supporting its potential role as a triage adjunct in AI-based screening, particularly for dense breasts and mammographically occult lesions. However, the retrospective, cancer-only design limits generalizability, highlighting the need for prospective multicenter screening trials for validation.