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Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Feng Zhang,
  • Yaoyao Wu,
  • Zhengyu Hu,
  • Piao Yang,
  • Wenting Lan,
  • Zhi Li,
  • Zhan Feng

摘要

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.