Radiomics-Based Unsupervised Clustering Identifies Subtypes Associated With Prognosis and Immune Microenvironment in
Yusheng Guo1,2,3, Bingxin Gong1,2,3, Yi Li1,2,3
1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Abstract:
Clear cell renal cell carcinoma (ccRCC) exhibits marked clinical heterogeneity, limiting the prognostic accuracy of traditional staging. We developed an unsupervised radiomics-based subtyping system integrating multi-omics data to decode tumor biology and improve risk stratification. Analyzing five cohorts (n = 1700, including surgical cohorts and an advanced ccRCC cohort receiving combined tyrosine kinase inhibitor and immunotherapy [T-I] treatment), we extracted 1834 CT radiomic features, applying consensus clustering to a discovery cohort (n = 748) and validating across centers. Two subtypes emerged with distinct recurrence risks: Cluster 1 and Cluster 2 (adjusted HR = 2.75 for recurrence, 95% CI 1.42-5.33, P = 0.003). Cluster 2's high recurrence risk was validated in three external cohorts (adjusted HRs: 1.76, 4.33, and 3.09; all P < 0.05). Radiogenomic analysis revealed Cluster 2 showed a higher frequency of VHL mutations and KDM5C mutations compared to Cluster 1, a more immunosuppressive microenvironment (reduced CD8+ T cell infiltration, P < 0.01; suppressed interferon signaling pathways, Gene Set Enrichment Analysis P < 0.05), and lower PD-L1 expression. In the T-I treated advanced ccRCC cohort, Cluster 2 patients had shorter overall survival. This first unsupervised radiomic system stratifies ccRCC by recurrence risk, molecular drivers, and treatment efficacy, offering a framework for precision oncology.
