Wang Xinyi, Wei Mingxiang, Chen Ying, Jia Jianye, Zhang Yu, Dai Yao, Qin Cai, Bai Genji, Chen Shuangqing
Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
Insights Imaging. 2024 Nov 22;15(1):281. doi: 10.1186/s13244-024-01855-w.
To investigate the potential of intratumoral and peritumoral radiomics derived from T2-weighted MRI to preoperatively predict extrapelvic peritoneal metastasis (EPM) in patients with epithelial ovarian cancer (EOC).
In this retrospective study, 488 patients from four centers were enrolled and divided into training (n = 245), internal test (n = 105), and external test (n = 138) sets. Intratumoral and peritumoral models were constructed based on radiomics features extracted from the corresponding regions. A combined intratumoral and peritumoral model was developed via a feature-level fusion. An ensemble model was created by integrating this combined model with specific independent clinical predictors. The robustness and generalizability of these models were assessed using tenfold cross-validation and both internal and external testing. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanation method was employed for model interpretation.
The ensemble model showed superior performance across the tenfold cross-validation, with the highest mean AUC of 0.844 ± 0.063. On the internal test set, the peritumoral and ensemble models significantly outperformed the intratumoral model (AUC = 0.786 and 0.832 vs. 0.652, p = 0.007 and p < 0.001, respectively). On the external test set, the AUC of the ensemble model significantly exceeded those of the intratumoral and peritumoral models (0.843 vs. 0.750 and 0.789, p = 0.008 and 0.047, respectively).
Peritumoral radiomics provide more informative insights about EPM than intratumoral radiomics. The ensemble model based on MRI has the potential to preoperatively predict EPM in EOC patients.
Integrating both intratumoral and peritumoral radiomics information based on MRI with clinical characteristics is a promising noninvasive method to predict EPM to guide preoperative clinical decision-making for EOC patients.
Peritumoral radiomics can provide valuable information about extrapelvic peritoneal metastasis in epithelial ovarian cancer. The ensemble model demonstrated satisfactory performance in predicting extrapelvic peritoneal metastasis. Combining intratumoral and peritumoral MRI radiomics contributes to clinical decision-making in epithelial ovarian cancer.
探讨基于T2加权磁共振成像（MRI）的肿瘤内及瘤周放射组学特征术前预测上皮性卵巢癌（EOC）患者盆腔外腹膜转移（EPM）的潜力。
在这项回顾性研究中，纳入了来自四个中心的488例患者，并将其分为训练集（n = 245）、内部测试集（n = 105）和外部测试集（n = 138）。基于从相应区域提取的放射组学特征构建肿瘤内和瘤周模型。通过特征级融合开发了一种肿瘤内和瘤周联合模型。通过将该联合模型与特定的独立临床预测因子相结合创建了一个集成模型。使用十折交叉验证以及内部和外部测试评估这些模型的稳健性和泛化性。通过受试者操作特征曲线（AUC）下的面积评估模型性能。采用Shapley加性解释方法进行模型解释。
集成模型在十折交叉验证中表现出卓越性能，最高平均AUC为0.844±0.063。在内部测试集上，瘤周模型和集成模型显著优于肿瘤内模型（AUC分别为0.786和0.832，而肿瘤内模型为0.652，p分别为0.007和p < 0.001）。在外部测试集上，集成模型的AUC显著超过肿瘤内模型和瘤周模型（分别为0.843 vs. 0.750和0.789，p分别为0.008和0.047）。
瘤周放射组学比肿瘤内放射组学能提供更多关于EPM的信息。基于MRI的集成模型有潜力术前预测EOC患者的EPM。
基于MRI将肿瘤内和瘤周放射组学信息与临床特征相结合是一种有前景的非侵入性方法，可预测EPM以指导EOC患者的术前临床决策。
瘤周放射组学可为上皮性卵巢癌盆腔外腹膜转移提供有价值的信息。集成模型在预测盆腔外腹膜转移方面表现出令人满意的性能。结合肿瘤内和瘤周MRI放射组学有助于上皮性卵巢癌的临床决策。
