Intratumoral habitat imaging for progression-free survival prediction of patients with high-risk, non-metastatic clear cell renal cell carcinoma

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Abstract
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.
Funding Information
  • General Project of Hangzhou Health Science and Technology Program (Grant No.B20253445)
  • Zhejiang Provincial Natural Science Foundation of China (Grant No. LTGY24H180003)

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