Added value of diffusion-weighted imaging in detecting breast cancer missed by artificial intelligence-based mammography.
[OBJECTIVE] 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 mis
- p-value p = 0.049
- p-value p < 0.001
- OR 1.619
APA
Kim JY, Kim JJ, et al. (2026). Added value of diffusion-weighted imaging in detecting breast cancer missed by artificial intelligence-based mammography.. La Radiologia medica, 131(4), 607-616. https://doi.org/10.1007/s11547-025-02161-1
MLA
Kim JY, et al.. "Added value of diffusion-weighted imaging in detecting breast cancer missed by artificial intelligence-based mammography.." La Radiologia medica, vol. 131, no. 4, 2026, pp. 607-616.
PMID
41324774
Abstract
[OBJECTIVE] 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.
[MATERIALS AND METHODS] 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.
[RESULTS] 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.
[CONCLUSION] 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.
[MATERIALS AND METHODS] 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.
[RESULTS] 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.
[CONCLUSION] 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.
MeSH Terms
Humans; Female; Breast Neoplasms; Middle Aged; Diffusion Magnetic Resonance Imaging; Retrospective Studies; Artificial Intelligence; Mammography; Aged; Adult; Missed Diagnosis; Diagnosis, Computer-Assisted
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