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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.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 20, 2025
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Summary

This study introduces a new radiomics system to subtype clear cell renal cell carcinoma (ccRCC). It identifies two subtypes with different recurrence risks, improving ccRCC risk stratification and guiding precision oncology.

Keywords:
clear cell renal cell carcinomaimmune microenvironmentprognosistumor recurrenceunsupervised clustering

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Area of Science:

  • Oncology
  • Radiology
  • Genomics

Background:

  • Clear cell renal cell carcinoma (ccRCC) has variable clinical behavior.
  • Traditional staging methods have limited prognostic accuracy for ccRCC.
  • There is a need for improved risk stratification and understanding of ccRCC biology.

Purpose of the Study:

  • To develop an unsupervised radiomics-based subtyping system for ccRCC.
  • To integrate multi-omics data for decoding tumor biology and improving risk stratification.
  • To assess the prognostic and predictive value of identified subtypes.

Main Methods:

  • Analysis of five ccRCC cohorts (n=1700) including surgical and advanced treated cases.
  • Extraction of 1834 CT radiomic features and application of consensus clustering.
  • Validation of identified subtypes across multiple external cohorts and treatment groups.

Main Results:

  • Two distinct ccRCC subtypes (Cluster 1 and Cluster 2) with significantly different recurrence risks were identified.
  • Cluster 2 demonstrated higher recurrence risk, VHL/KDM5C mutations, a more immunosuppressive microenvironment, and lower PD-L1 expression.
  • In advanced ccRCC treated with tyrosine kinase inhibitor and immunotherapy, Cluster 2 patients showed shorter overall survival.

Conclusions:

  • The developed unsupervised radiomic system effectively stratifies ccRCC patients by recurrence risk.
  • This system provides insights into molecular drivers and treatment efficacy for ccRCC subtypes.
  • It offers a novel framework for precision oncology in ccRCC management.