Computed tomography-based unsupervised clustering identifies clusters associated with progression free survival in clear cell renal cell carcinoma

Abstract

Background This study aimed to develop and validate a radiologic clustering model using CT imaging features to stratify clear cell renal cell carcinoma (ccRCC) patients by prognosis and identify key imaging predictors of 5-year progression free survival (PFS). Methods This retrospective study included 164 ccRCC patients with multiphase kidney CT and next-generation sequencing (NGS) between September 2003 and October 2024. Qualitative imaging features were extracted, and unsupervised consensus clustering was performed to classify tumors based on radiologic characteristics. A nomogram-based C1 score was derived from features predictive of the high-risk cluster. Model performance was evaluated using C-index and 5-year area under the receiver operating curve (AUC). Genetic alterations and copy number variations (CNVs) were also analyzed for associations with imaging features and survival. Results Clustering revealed two distinct radiologic subtypes. Cluster C1 characterized by aggressive behavior such as tumor heterogeneity (p = 0.011), exophytic growth pattern (p = 0.002), non-smooth margin (p = 0.019), and renal sinus extension (p = 0.016), and was independently associated with poorer 5-year PFS (p = 0.018). The C1 score demonstrated an AUC of 0.992 for predicting cluster C1 in the test-set. Using a cutoff of 0.75, the model achieved 96.3% sensitivity and 96.4% specificity. For predicting 5-year PFS, the C1 score showed moderate performance (AUC 0.65; C-index 0.65), which improved when combined with nodal/distant metastasis and BAP1 mutation status (AUC 0.71; C-index 0.67). Conclusions Radiologic clustering using CT features enables non-invasive prognostic stratification of ccRCC. The C1 score derived from this approach may serve as a practical tool to guide surveillance and treatment decisions. Trial registration Retrospectively registered.