CT-based radiomics model for noninvasive prediction of progression-free survival in high-grade serous ovarian carcinoma: a multicenter study incorporating preoperative and postoperative clinical factors

作者：Xinping Yu, Zidong Zhang, Yuwei Zou, Chang Wang, Jinwen Jiao, Chengjian Wang, Haiyang Yu, Shuai Zhang · 发表于：BMC Medical Imaging · 年份：2025 · DOI：10.1186/s12880-025-01865-0 · 被引用次数：3 · 研究领域：Ovarian cancer diagnosis and treatment、Intraperitoneal and Appendiceal Malignancies、Radiomics and Machine Learning in Medical Imaging

OBJECTIVE: To investigate the potential of combining radiomics with clinicoradiological features in predicting progression-free survival (PFS) after the surgery of high-grade serous ovarian carcinoma (HGSOC). METHODS: In this retrospective multicenter study, a total of 195 patients with pathologically confirmed HGSOC who underwent cytoreductive surgery followed by platinum-based chemotherapy were included from two institutions (train cohort, n = 134; test cohort, n = 61). From the train cohort, univariate and multivariate Cox proportional hazards regression analyses systematically evaluated associations between clinicoradiological features and PFS, culminating in a clinical prediction model for stratifying progression risk. Radiomics features were extracted and utilized to build the radiomics model through univariate Cox regression and least absolute shrinkage and selection operator Cox regression. A combined model integrating both clinicoradiological and radiomics features was subsequently developed. The concordance index (C-index) was used to assess the predictive performance of different models in 1-, 3-, and 5-year PFS evens among HGSOC patients. Model performance was assessed using time-dependent receiver operating characteristic curves, with area under the curve (AUC) values calculated at various time points. as well as calibration curves and Brier scores to evaluate prediction accuracy and model reliability. Kaplan-Meier analysis was employed to evaluate the clinical u...