Park Doohyun, Lee Yong-Moon, Eo Taejoon, An Hee Jung, Kang Haeyoun, Park Eunhyang, Cha Yoon Jin, Park Heejung, Kwon Dohee, Kwon Sun Young, Jung Hye-Ra, Shin Su-Jin, Park Hyunjin, Lee Yangkyu, Park Sanghui, Kim Ji Min, Choi Sung-Eun, Cho Nam Hoon, Hwang Dosik
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
Department of Pathology, Dankook University College of Medicine, Cheonan, Republic of Korea.
NPJ Precis Oncol. 2025 May 6;9(1):131. doi: 10.1038/s41698-025-00914-9.
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.
在乳腺癌治疗中，利用原发肿瘤活检的全切片图像（WSIs）预测腋窝淋巴结（ALN）转移，对病理学家来说是一项具有挑战性且尚未充分探索的任务。我们开发了METACANS，这是一种多模态人工智能（AI）模型，它将WSIs与临床病理特征相结合以预测ALN转移。METACANS在1991例病例上进行了训练，并在总共2166例病例的五个队列中进行了外部验证。在所有验证队列中，METACANS的曲线下面积（AUC）为0.733（95%CI，0.711 - 0.755），总体阴性预测值为0.846，敏感性为0.820，特异性为0.504，平衡准确率为0.662。在没有额外注释的情况下，METACANS识别出了与转移状态相关的病理影像模式，如微乳头生长、浸润模式和坏死。虽然其预测性能可能尚不支持立即临床应用，但METACANS解决了利用WSIs和临床病理特征预测ALN转移的任务，并证明了多模态AI方法用于乳腺癌术前腋窝分期的可行性。
