Predictive nomogram integrating radiomics and multi‐omics for improved prognosis‐model in cholangiocarcinoma

作者：Yunlu Jia, Mingyu Wan, Yifei Shen, Junli Wang, Xiao Luo, Mengye He, Ruiliang Bai, Wenbo Xiao, Xiaochen Zhang, Jian Ruan · 发表于：Clinical and Translational Medicine · 年份：2025 · DOI：10.1002/ctm2.70171 · 被引用次数：3 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、Radiomics and Machine Learning in Medical Imaging、Cancer Diagnosis and Treatment

Dear Editor, Intrahepatic cholangiocarcinoma (ICC) is a malignant tumour originating from the epithelial cells of the intrahepatic bile ducts. In recent years, its incidence has shown an upward trend globally. Notably, hepatitis B virus (HBV) infection is one of the significant risk factors for ICC.1 Despite significant advancements in medical imaging and molecular biology technologies, predicting the prognosis of HBV-associated ICC patients remains challenging. One major reason for this challenge is the complex interactions between HBV infection, genetic mutations and tumour behaviour, which increase the uncertainty of prognosis predictions. As a result, traditional single indicators are insufficient for comprehensively assessing patient outcomes. Radiomics is a technology that extracts a large number of quantitative features from medical images, capturing the spatial structure and morphological changes of tumours.2 Genomics, on the other hand, focuses on deciphering DNA sequence information, revealing the contributions of genetic variations to disease development. This study aims to develop and validate a predictive model that integrates radiomic features with genomic information. By doing so, it seeks to overcome the limitations of existing biomarkers, better meet the needs for personalised treatment of HBV-associated ICC patients and provide valuable references for future research and clinical practice. A total of 389 intrahepatic cholangiocarcinoma (ICC) patients were re...