Computed tomography-based unsupervised clustering identifies clusters associated with progression free survival in...

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.