Bi Qiu, Miao Kun, Liu Yang, Yang Jing, Zhou Ao, Shi Wenwei, Lei Ying, Wu Yunzhu, Song Yang, Ai Conghui, Li Haiming, Qiang Jingwei
Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Abdom Radiol (NY). 2025 May 29. doi: 10.1007/s00261-025-05004-9.
To evaluate the value of multiparametric MRI (mpMRI)-based habitat analysis for predicting prognoses in patients with high-grade serous ovarian cancer (HGSOC), and to develop combined models by integrating habitat analysis with clinical predictors.
This retrospective study included 503 HGSOC patients from four centers. A K-means algorithm was used to identify voxel clusters and generate habitats on mpMRI. Radiomics features were extracted from each habitat sub-region. After feature selection, habitat models were developed to predict overall survival (OS) and progression-free survival (PFS). Cox regression analyses were performed to identify clinical predictors and construct clinical models. Combined models were developed by integrating habitat signatures with clinical predictors. Model performance was evaluated using C-index and time-dependent receiver operating characteristic area under the curves (AUCs).
Compared with the clinical models (OS: 0.713 and 0.695; PFS: 0.727 and 0.700) and habitat models (OS: 0.707 and 0.672; PFS: 0.627 and 0.641), the combined models integrating habitat features and clinical independent predictors such as neoadjuvant chemotherapy (OS: 0.752 and 0.745; PFS: 0.784 and 0.754) achieved the highest C-indices for predicting OS and PFS in the internal validation cohort and external test cohort. The combined models also achieved the highest AUCs in all cohorts.
The habitat models based on mpMRI demonstrated potential value in predicting the prognoses of HGSOC patients, but no significant advantages over the clinical models. The combined models were expected to improve the prognoses from the level of individual clinical characteristics and habitat features reflecting intratumoral heterogeneity.
评估基于多参数磁共振成像（mpMRI）的栖息地分析在预测高级别浆液性卵巢癌（HGSOC）患者预后中的价值，并通过将栖息地分析与临床预测因素相结合来开发联合模型。
这项回顾性研究纳入了来自四个中心的503例HGSOC患者。采用K均值算法识别体素簇并在mpMRI上生成栖息地。从每个栖息地子区域提取影像组学特征。经过特征选择后，开发栖息地模型以预测总生存期（OS）和无进展生存期（PFS）。进行Cox回归分析以识别临床预测因素并构建临床模型。通过将栖息地特征与临床预测因素相结合来开发联合模型。使用C指数和曲线下时间依赖性受试者操作特征面积（AUC）评估模型性能。
与临床模型（OS：0.713和0.695；PFS：0.727和0.700）和栖息地模型（OS：0.707和0.672；PFS：0.627和0.641）相比，整合栖息地特征和新辅助化疗等临床独立预测因素的联合模型（OS：0.752和0.745；PFS：0.784和0.754）在内部验证队列和外部测试队列中预测OS和PFS时获得了最高的C指数。联合模型在所有队列中也获得了最高的AUC。
基于mpMRI的栖息地模型在预测HGSOC患者预后方面显示出潜在价值，但与临床模型相比没有显著优势。联合模型有望从反映肿瘤内异质性的个体临床特征和栖息地特征水平改善预后。
