ArticleAbdominal radiology (New York)2026
Radiomics-based tumor heterogeneity augments clinicopathological models for predicting recurrence in high-risk clear cell renal cell carcinoma after nephrectomy.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Review
- The Central Role of Imaging in Renal Cell Carcinoma: A Comprehensive Review of Tumor Aggressiveness, Histology, and Radiomics.Cancers · 2026Review
- Multimodal deep learning for prediction of postoperative recurrence in clear cell renal cell carcinoma: a clinical-radiologic-pathologic approach.Frontiers in oncology · 2026Article
- Radiomics in Action: Multimodal Synergies for Imaging Biomarkers.Bioengineering (Basel, Switzerland) · 2025Review
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6 authors.
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Abstract
purposeTo 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.
methodsThis 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.
resultsDuring 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.
conclusionsFor 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.
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