为了开发和验证基于T2加权磁共振成像（MRI）的深度学习放射组学评分模型（DLRN），以区分I型和II型上皮性卵巢癌（EOC）。这项多中心研究纳入了来自五个中心的437名患者，分为训练组（n = 271）、内部验证组（n = 68）和外部验证组（n = 98）。深度学习（DL）模型使用肿瘤区域最大的正交切片构建。提取的放射组学特征用于构建放射组学模型。临床模型基于临床特征开发。DLRN通过整合DL签名，放射组学签名和独立临床预测因子来建立。通过接受者操作特征（ROC）分析，布雷尔分数，校准曲线和决策曲线分析（DCA）评估模型性能。使用DeLong检验比较ROC曲线下面积（AUC）。双尾P < 0.05被视为显著不同。DLRN在内部和外部验证集中展示了对I型和II型EOC的满意鉴别能力，AUC分别为0.888（95%置信区间[CI] 0.810, 0.966）和0.866（95% CI 0.786, 0.946）。这些AUC显著超过了临床模型（在内部和外部验证集中，P = 0.013和0.043）。根据布雷尔分数，校准曲线和DCA，DLRN展示了最佳的分类准确性和临床应用价值。基于T2加权MRI的DLRN在区分I型和II型EOC方面显示出了很大的潜力，可以为临床决策提供帮助。版权所有©2023大学放射学会。由Elsevier Inc.出版，保留所有权利。

To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC).This multicenter study incorporated 437 patients from five centers, divided into training (n = 271), internal validation (n = 68), and external validation (n = 98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P < 0.05 was considered significantly different.The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P = 0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA.A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.