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SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors

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机构： [1]The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China. [2]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China. [3]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.

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关键词： intrahepatic cholangiocarcinoma histological grade multiple instance learning crossattention mechanism CT-based diagnostics

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After hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer. Timely and accurate identification of ICC histological grade is critical for guiding clinical diagnosis and treatment planning.We proposed a dual-branch deep neural network (SiameseNet) based on multiple-instance learning and cross-attention mechanisms to address tumor heterogeneity in ICC histological grade prediction. The study included 424 ICC patients (381 in training, 43 in testing). The model integrated imaging data from two modalities through cross-attention, optimizing feature representation for grade classification.In the testing cohort, the model achieved an accuracy of 86.0%, AUC of 86.2%, sensitivity of 84.6%, and specificity of 86.7%, demonstrating robust predictive performance.The proposed framework effectively mitigates performance degradation caused by tumor heterogeneity. Its high accuracy and generalizability suggest potential clinical utility in assisting histopathological assessment and personalized treatment planning for ICC patients.Copyright © 2025 Fu, Feng, He, Li, Li, Ziluo, Huang and Ye.

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出版当年[2025]版：

大类 | 3 区医学

小类 | 4 区肿瘤学

最新[2025]版：

大类 | 3 区医学

小类 | 4 区肿瘤学

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第一作者机构： [1]The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.

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