CT-based quantification of intratumoral heterogeneity for predicting distant metastasis in retroperitoneal sarcoma

作者：Jun Xu, Jianguo Miao, Chenxi Wang, Yupeng Zhu, Ke Liu, S. Qin, Haisong Chen, Ning Lang · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-01977-9 · 被引用次数：5 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Sarcoma Diagnosis and Treatment、Gastrointestinal Tumor Research and Treatment

OBJECTIVES: Retroperitoneal sarcoma (RPS) is highly heterogeneous, leading to different risks of distant metastasis (DM) among patients with the same clinical stage. This study aims to develop a quantitative method for assessing intratumoral heterogeneity (ITH) using preoperative contrast-enhanced CT (CECT) scans and evaluate its ability to predict DM risk. METHODS: We conducted a retrospective analysis of 274 PRS patients who underwent complete surgical resection and were monitored for ≥ 36 months at two centers. Conventional radiomics (C-radiomics), ITH radiomics, and deep-learning (DL) features were extracted from the preoperative CECT scans and developed single-modality models. Clinical indicators and high-throughput CECT features were integrated to develop a combined model for predicting DM. The performance of the models was evaluated by measuring the receiver operating characteristic curve and Harrell's concordance index (C-index). Distant metastasis-free survival (DMFS) was also predicted to further assess survival benefits. RESULTS: The ITH model demonstrated satisfactory predictive capability for DM in internal and external validation cohorts (AUC: 0.735, 0.765; C-index: 0.691, 0.729). The combined model that combined clinicoradiological variables, ITH-score, and DL-score achieved the best predictive performance in internal and external validation cohorts (AUC: 0.864, 0.801; C-index: 0.770, 0.752), successfully stratified patients into high- and low-risk groups for D...