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Objective <p>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.</p> Materials and methods <p>This retrospective study included 414 women (mean age, 55.3&#xa0;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.</p> Results <p>AI-CAD missed 127 of 414 breast cancers (30.7%). Multivariate regression analysis identified dense breasts (adjusted OR = 1.619; <i>p</i> = 0.049) and tumor size ≤ 2&#xa0;cm (adjusted OR = 4.698; <i>p</i> &lt; 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 &gt;1&#xa0;cm tumors, but sensitivity declined for subcentimeter lesions.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Jin You Kim,
  • Jin Joo Kim,
  • Ho Jun Lee,
  • Lee Hwangbo,
  • You Seon Song,
  • Ji Won Lee,
  • Nam Kyung Lee,
  • Seung Baek Hong,
  • Suk Kim

摘要

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.