手术后早期复发（ER）会导致肝内胆管癌（ICC）的预后不良。我们旨在开发机器学习（ML）影像组学模型，以预测治愈性切除术后ICC的ER。从三个机构进行了回顾性招募经治性手术的ICC患者，并分配到训练和外部验证队列。进行前期动脉和静脉期对比增强计算机断层扫描（CECT）图像并进行分割。通过其重要性提取并排名放射组学特征。使用单变量和多变量逻辑回归分析来识别临床特征。使用各种ML算法构建基于放射组学的模型，并通过接受者操作特征曲线、校准曲线和决策曲线分析评估预测性能。共纳入127名患者进行分析：90名患者在训练组，37名患者在验证组。92名患者（72.4%）经历了复发，包括71名患者出现ER。男性、微血管侵犯、TNM分期和血清CA19-9被确定为ER的独立危险因素，相应的临床模型预测性能不佳（AUC为0.685）。识别出57个差异放射组学特征，并选用10个最重要的特征进行建模。开发了7个ML放射组学模型，平均AUC为0.87±0.02，高于临床模型。此外，临床 - 放射组学模型表现出与放射组学模型相似的预测性能（AUC为0.87±0.03）。基于CECT的ML放射组学模型对于预测ICC中的ER具有价值。 ©2023.作者（s），在Springer-Verlag GmbH Germany的独家许可下，属于Springer Nature的一部分。

Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection.Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03).ML radiomics models based on CECT are valuable in predicting ER in ICC.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.