Miao Gengyun, Qian Xianling, Zhang Yunfei, Hou Kai, Wang Fang, Xuan Haoxiang, Wu Fei, Zheng Beixuan, Yang Chun, Zeng Mengsu
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China.
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China; Central Research Institute, United Imaging Healthcare, Shanghai, China.
Eur J Radiol. 2025 Feb;183:111896. doi: 10.1016/j.ejrad.2024.111896. Epub 2024 Dec 19.
Microvascular invasion (MVI) serves as a significant predictor of poor prognosis in intrahepatic cholangiocarcinoma (ICC). This study aims to establish a comprehensive model utilizing MR radiomics for preoperative MVI status stratification and outcome prediction in ICC patients.
A total of 249 ICC patients were randomly assigned to training and validation cohorts (174:75), along with a time-independent test cohort consisting of 47 ICC patients. Independent clinical and imaging predictors were identified by univariate and multivariate logistic regression analyses. The radiomic model was developed based on robust radiomic features extracted using a logistic regression classifier. The predictive efficacy of the models was evaluated by receiver operating characteristic curves, calibration curves and decision curves. Multivariate Cox analysis identified the independent risk factors for recurrence-free survival and overall survival, Kaplan-Meier curves were plotted, and a nomogram was used to visualize the predictive model.
The imaging model included tumor size and intrahepatic duct dilatation. The radiomics model comprised 25 stable radiomics features. The Imaging-Radiomics (IR) model, which integrates independent predictors and robust radiomics features, demonstrates desirable performance for MVI (AUC= 0.890, AUC= 0.885 and AUC= 0.815). The calibration curve and decision curve validate the clinical utility. Preoperative MVI prediction based on IR model demonstrated comparable accuracy in MVI stratification and outcome prediction when compared to histological MVI.
The IR model and the nomogram based on IR model-predicted MVI status may serve as potential tools for MVI status stratification and outcome prediction in ICC patients preoperatively.
微血管侵犯（MVI）是肝内胆管癌（ICC）预后不良的重要预测指标。本研究旨在建立一种综合模型，利用磁共振成像（MR）放射组学对ICC患者术前MVI状态进行分层并预测预后。
共249例ICC患者被随机分为训练组和验证组（174:75），另有一个由47例ICC患者组成的与时间无关的测试组。通过单因素和多因素逻辑回归分析确定独立的临床和影像预测因素。基于使用逻辑回归分类器提取的稳健放射组学特征建立放射组学模型。通过受试者工作特征曲线、校准曲线和决策曲线评估模型的预测效能。多因素Cox分析确定无复发生存和总生存的独立危险因素，绘制Kaplan-Meier曲线，并使用列线图直观显示预测模型。
影像模型包括肿瘤大小和肝内胆管扩张。放射组学模型包含25个稳定的放射组学特征。整合独立预测因素和稳健放射组学特征的影像-放射组学（IR）模型在MVI预测方面表现良好（AUC = 0.890、AUC = 0.885和AUC = 0.815）。校准曲线和决策曲线验证了其临床实用性。与组织学MVI相比，基于IR模型的术前MVI预测在MVI分层和预后预测方面具有相当的准确性。
IR模型以及基于IR模型预测的MVI状态的列线图可作为术前对ICC患者进行MVI状态分层和预后预测的潜在工具。
