Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images

In breast cancer management, predicting axillary lymph node (ALN) metastasis using whole-slide images (WSIs) of primary tumor biopsies is a challenging and underexplored task for pathologists. We developed METACANS, an multimodal artificial intelligence (AI) model that integrates WSIs with clinicopathological features to predict ALN metastasis. METACANS was trained on 1991 cases and externally validated across five cohorts with a total of 2166 cases. Across all validation cohorts, METACANS achieved an area under the curve (AUC) of 0.733 (95% CI, 0.711-0.755), with an overall negative predictive value of 0.846, sensitivity of 0.820, specificity of 0.504, and balanced accuracy of 0.662. Without additional annotations, METACANS identified pathological imaging patterns linked to metastatic status, such as micropapillary growth, infiltrative patterns, and necrosis. While its predictive performance may not yet support immediate clinical application, METACANS addresses the task of predicting ALN metastasis using WSIs and clinicopathological features, and demonstrates the feasibility of multimodal AI approaches for preoperative axillary staging in breast cancer.

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Publication Details

Journal
npj Precision Oncology
Published
2025-05-06
DOI
https://doi.org/10.1038/s41698-025-00914-9
Citations
2
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
4.96

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article

Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images

Yangkyu Lee, Sanghui Park, Yoon Jin, Yong‐Moon Lee et al.
2 citations
npj Precision Oncology
AI in cancer detection
4.96
article

Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images

Yangkyu Lee, Sanghui Park, Yoon Jin, Yong‐Moon Lee, Doohyun Park, Taejoon Eo, Sun Young Kwon, Dosik Hwang, Su‐Jin Shin, Eunhyang Park, Hee Jung An, Hyunjin Park, Hee Jung Park, Haeyoun Kang, Nam Hoon Cho, David C. H. Kwon, Hye-Ra Jung, Sung-Eun Choi, Ji Min Kim
article en
2 citations

Abstract

In breast cancer management, predicting axillary lymph node (ALN) metastasis using whole-slide images (WSIs) of primary tumor biopsies is a challenging and underexplored task for pathologists. We developed METACANS, an multimodal artificial intelligence (AI) model that integrates WSIs with clinicopathological features to predict ALN metastasis. METACANS was trained on 1991 cases and externally validated across five cohorts with a total of 2166 cases. Across all validation cohorts, METACANS achieved an area under the curve (AUC) of 0.733 (95% CI, 0.711-0.755), with an overall negative predictive value of 0.846, sensitivity of 0.820, specificity of 0.504, and balanced accuracy of 0.662. Without additional annotations, METACANS identified pathological imaging patterns linked to metastatic status, such as micropapillary growth, infiltrative patterns, and necrosis. While its predictive performance may not yet support immediate clinical application, METACANS addresses the task of predicting ALN metastasis using WSIs and clinicopathological features, and demonstrates the feasibility of multimodal AI approaches for preoperative axillary staging in breast cancer.

npj Precision OncologyVol. 9(1)
Ewha Womans University (KR), Yonsei University (KR), Severance Hospital (KR), Seoul Medical Center (KR), CHA University Bundang Medical Center (KR), Gangnam Severance Hospital (KR), CHA University (KR), Yonsei University Health System (KR), Keimyung University (KR), Korea Institute of Science and Technology (KR), Dankook University (KR)
Ministry of Health and Welfare, National Research Foundation, Yonsei University, Ministry of Health and Welfare, Korea Institute of Science and Technology, Korea Health Industry Development Institute, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 5%
AI in cancer detection
4.96
DOIOpen Access PDFOpenAlex
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