An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data

作者：Huiyu Zhao, Chuang Miao, Yidi Zhu, Yijun Shu, Xiangsong Wu, Ziming Yin, Xiao Deng, Wei Gong, Ziyi Yang, Weiwen Zou · 发表于：BMC Cancer · 年份：2025 · DOI：10.1186/s12885-025-14462-9 · 被引用次数：8 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、Gallbladder and Bile Duct Disorders、Hepatocellular Carcinoma Treatment and Prognosis

BACKGROUND: The accurate early-stage diagnosis of gallbladder cancer (GBC) is regarded as one of the major challenges in the field of oncology. However, few studies have focused on the comprehensive classification of GBC based on multiple modalities. This study aims to develop a comprehensive diagnostic framework for GBC based on both imaging and non-imaging medical data. METHODS: This retrospective study reviewed 298 clinical patients with gallbladder disease or volunteers from two devices. A novel end-to-end interpretable diagnostic framework for GBC is proposed to handle multiple medical modalities, including CT imaging, demographics, tumor markers, coagulation function tests, and routine blood tests. To achieve better feature extraction and fusion of the imaging modality, a novel global-hybrid-local network, namely GHL-Net, has also been developed. The ensemble learning strategy is employed to fuse multi-modality data and obtain the final classification result. In addition, two interpretable methods are applied to help clinicians understand the model-based decisions. Model performance was evaluated through accuracy, precision, specificity, sensitivity, F1-score, area under the curve (AUC), and matthews correlation coefficient (MCC). RESULTS: In both binary and multi-class classification scenarios, the proposed method showed better performance compared to other comparison methods in both datasets. Especially in the binary classification scenario, the proposed method achiev...