为了对输尿管肝内外分流术（PCCA）术前淋巴结（LN）转移进行预测，我们开发了一个基于CT图像学的放射组学模型。本研究纳入了三个独立的中国医疗中心中接受PCCA手术的连续病例。应用Boruta算法建立了主要肿瘤和LN的放射组学特征标志。采用k-means算法根据放射组学特征LN对选定的LN进行聚类。支持向量机被用来构建预测模型。通过受试者工作特征曲线下面积（AUC）来衡量诊断效能。最优模型的标定、临床实用性和预后价值进行了评估。研究共纳入了214例患者（平均年龄：61.6岁±9.4；男性130名）。选定的LN被分为两类簇，所有队列中的LN转移与其显著相关（p＜0.001）。该模型结合了临床危险因素、放射组学特征主要肿瘤和LN簇，预测效果最佳，分别在训练、内部验证和外部验证队列中的AUC值为0.981（95% CI: 0.962-1），0.896（95% CI: 0.810-0.982）和0.865（95% CI: 0.768-0.961）。最优模型预测的高危患者其总生存期较低危患者明显缩短（中位数分别为13.7与27.3个月，p＜0.001）。研究提出了一种预测PCCA LN转移的放射组学模型，并证明其良好性能。作为一种非侵入性术前预测工具，该模型可以帮助患者进行风险分层和个体化治疗。CT基于放射组学模型可以准确预测PCCA患者的淋巴结转移。这种非侵入性术前工具可以帮助患者进行风险分层和个体化治疗，有可能改善患者预后。•基于增强CT的放射组学模型是预测PCCA淋巴结转移的有用工具。•从淋巴结中提取的放射组学特征在预测淋巴结转移方面具有巨大潜力。•本研究首次根据放射组学鉴定了具有高概率转移的淋巴结表型。©2023. 作者（们），独家授权给欧洲放射学学会。

To develop a CT-based radiomics model for preoperative prediction of lymph node (LN) metastasis in perihilar cholangiocarcinoma (pCCA).The study enrolled consecutive pCCA patients from three independent Chinese medical centers. The Boruta algorithm was applied to build the radiomics signature for the primary tumor and LN. The k-means algorithm was employed to cluster the selected LNs based on the radiomics signature LN. Support vector machines were used to construct the prediction models. The diagnostic efficiency was measured by the area under the receiver operating characteristic curve (AUC). The optimal model was evaluated in terms of calibration, clinical usefulness, and prognostic value.A total of 214 patients were included in the study (mean age: 61.6 years ± 9.4; 130 male). The selected LNs were classified into two clusters, which were significantly correlated with LN metastasis in all cohorts (p < 0.001). The model incorporated the clinical risk factors, radiomics signature primary tumor, and the LN cluster obtained the best discrimination, with AUC values of 0.981 (95% CI: 0.962-1), 0.896 (95% CI: 0.810-0.982), and 0.865 (95% CI: 0.768-0.961) in the training, internal validation, and external validation cohorts, respectively. High-risk patients predicted by the optimal model had shorter overall survival than low-risk patients (median, 13.7 vs. 27.3 months, p < 0.001).The study proposed a radiomics model with good performance to predict LN metastasis in pCCA. As a noninvasive preoperative prediction tool, this model may help in patient risk stratification and personalized treatment.A CT-based radiomics model accurately predicts lymph node metastasis in perihilar cholangiocarcinoma patients. This noninvasive preoperative tool can aid in patient risk stratification and personalized treatment, potentially improving patient outcomes.• The radiomics model based on contrast-enhanced CT is a useful tool for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma. • Radiomics features extracted from lymph nodes show great potential for predicting lymph node metastasis. • The study is the first to identify a lymph node phenotype with a high probability of metastasis based on radiomics.© 2023. The Author(s), under exclusive licence to European Society of Radiology.