在这个多中心研究中，我们通过基于人工智能（AI）的CT放射组学方法，旨在开发和验证术前模型，以预测肝内胆管癌（ICC）根治性切除术后早期复发（ER）风险。我们回顾性收集了来自8个医疗中心共311名接受了根治性切除的患者（推导组：160；内部和两个外部验证组：36、74和61）。在推导队列中，通过LightGBM（一种机器学习算法）构建了放射组学和临床放射组学模型，用于ER预测。还开发了一个临床模型进行比较。通过ROC在内部和两个外部队列中验证了模型性能。此外，我们还研究了LightGBM模型的可解释性。 这个结合了15个放射组学特征和3个临床特征（CA19-9 > 1000 U/ml、血管侵犯和肿瘤边缘）的临床放射组学模型，在推导队列中得出了曲线下面积（AUCs）为0.974（95％CI为0.946-1.000），在内部和外部验证队列中分别为0.871-0.882（95％CI为0.672-0.962），高于AJCC第8版TNM分期系统（AUCs：0.686-0.717，p均<0.05）。特别地，该机器学习模型的平均敏感性可达94.6％，适用于所有队列。 这个由人工智能驱动的结合放射组学模型可能作为一个有用的工具，预测ER并改善ICC患者的治疗管理。©2023年。亚太肝病研究协会。

In this multicenter study, we sought to develop and validate a preoperative model for predicting early recurrence (ER) risk after curative resection of intrahepatic cholangiocarcinoma (ICC) through artificial intelligence (AI)-based CT radiomics approach.A total of 311 patients (Derivation: 160; Internal and two external validations: 36, 74 and 61) from 8 medical centers who underwent curative resection were collected retrospectively. In derivation cohort, radiomics and clinical-radiomics models for ER prediction were constructed by LightGBM (a machine learning algorithm). A clinical model was also developed for comparison. Model performance was validated in internal and two external cohorts by ROC. In addition, we investigated the interpretability of the LightGBM model.The combined clinical-radiomics model that included 15 radiomic features and 3 clinical features (CA19-9 > 1000 U/ml, vascular invasion and tumor margin), resulting in the area under the curves (AUCs) of 0.974 (95% CI 0.946-1.000) in the derivation cohort, and 0.871-0.882 (95% CI 0.672-0.962) in the internal and external validation cohorts, respectively, which are higher than the AJCC 8th TNM staging system (AUCs: 0.686-0.717, p all < 0.05). Especially, the sensitivity of this machine learning model could reach 94.6% on average for all the cohorts.This AI-driven combined radiomics model may provide as a useful tool to preoperatively predict ER and improve therapeutic management of ICC patients.© 2023. Asian Pacific Association for the Study of the Liver.