Piao Yang

Purpose Accurate risk stratification for patients with high-risk, non-metastatic clear cell renal cell carcinoma (ccRCC) remains a clinical challenge. Our aim is to develop and validate an imaging model based on intratumoral habitat imaging for non-invasive assessment of progression-free survival (PFS) in this high-interest population. Methods A retrospective analysis was performed on a cohort of 220 patients with ccRCC from three hospitals. Preoperative CT images and clinical data were comprehensively analyzed. K-mean clustering method was used to generate habitat subregions using contrast-enhanced CT images. Radiomic features were extracted from habitat subregions and the entire tumor, Random Survival Forest for PFS prediction were constructed using conventional radiomics features, habitat subregions-derived radiomics. Model evaluation included calibration curve, receiver operating characteristic curve (ROC) and Kaplan-Meier survival analysis. Results The Habitat 3 model demonstrated favorable prognostic performance. In the training cohort, 1-, 3-, and 5-year AUCs were 0.92, 0.92, and 0.78, respectively. In the external test set, the model maintained robustness with 1-, 3-, and 5-year AUCs of 0.87, 0.74, and 0.94, outperforming traditional radiomics and other Habitat models. Furthermore, patients stratified by Habitat 3 showed significant survival differences between high-risk and low-risk groups in both cohorts. Conclusion We proposed a habitat-based imaging technique for the preliminary analysis of high-risk ccRCC. This retrospective study suggests that habitat signatures are associated with PFS, serving as a potential non-invasive complement to provide insights into tumor heterogeneity.

Purpose: To investigate the association between CT radiomics-based tumor heterogeneity and recurrence-free survival (RFS) in high-risk clear cell renal cell carcinoma (ccRCC) after nephrectomy, and to determine whether integrating CT radiomics with clinicopathological model enhances recurrence risk prediction for adjuvant treatment decisions. Methods: This retrospective study included 194 patients with high-risk ccRCC undergoing nephrectomy. A radiomics model based on random survival forest was developed in the training set, using radiomics features extracted from pre-operative corticomedullary phase images. The performance of radiomics, Leibovich score, and the combined model were evaluated using Kaplan-Meier survival analysis, time-dependent receiver operating characteristic curves (time-AUC), time-dependent Brier scores, and decision curve analysis in external test set. Results: During follow-up, 62 patients experienced recurrence. The radiomics model demonstrated superior predictive performance compared to the Leibovich score, with higher time-dependent AUCs (1-year: 0.882 vs. 0.781; 2-year: 0.865 vs. 0.762; 3-year: 0.793 vs. 0.797; all p < 0.05) and better calibration (lower Brier scores) in the test set. Decision curve analysis demonstrated that the combined model provided the highest net benefit, particularly for 2- to 3-year recurrence risk predictions. Conclusions: For high-risk ccRCC, CT radiomics provides incremental prognostic value beyond conventional clinicopathological models, enabling more precise recurrence risk stratification. This approach bridges imaging and precision oncology, with potential to optimize surveillance protocols and adjuvant therapy trial design.