该研究旨在探讨放射组学诊断模型的价值，以识别铂抗性并预测晚期高级别浆液性卵巢癌（HGSOC）患者的无进展生存期（PFS）。这项多中心回顾性研究对301名患有高级别HGSOC的患者进行放射组学特征提取，从增强T1WI和T2WI获得了整个原发肿瘤的放射组学特征。使用基于支持向量机的递归特征消除方法选择放射组学特征，然后生成放射组学标志。进一步通过多元逻辑回归将放射组学标志和临床特征结合起来建立了放射组学诊断模型。采用接受者操作特征曲线分析来评估其预测性能。使用净再分类指数（NRI）、综合判定提升（IDI）和决策曲线分析（DCA）来比较不同模型的临床效用和益处。选择5个特征与铂抗性显著相关来构建放射组学模型。结合FIGO分期、CA-125和残留肿瘤等三个临床特征，生成了放射组学诊断模型，其面积ROC曲线下面积（AUC）较单独的临床模型高（AUC：0.799 vs 0.747），且NRI和IDI为阳性。放射组学诊断模型的净效益通常高于单独的临床模型和放射组学模型。卡帕兰梅尔生存分析表明，在晚期HGSOC患者中，放射组学诊断模型定义的高风险组的PFS比低风险组短。放射组学诊断模型可以识别铂抗性并预测PFS，有助于进行个体化治疗管理。•基于放射组学的方法有潜力识别铂抗性，并有助于进行高级别HGSOC的个体化治疗管理。•放射组学-临床诊断模型在预测铂抗性HGSOC方面表现出更好的性能。•提出的诊断模型在预测训练和测试集中低风险和高风险HGSOC患者的PFS时间方面表现良好。©2023年。作者，独家许可欧洲放射学会（ESR）。

This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC).In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models.Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC.The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC.• The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.© 2023. The Author(s), under exclusive licence to European Society of Radiology.