ArticleAbdominal radiology (New York)2026
Biphasic CT clustering-based habitat radiomics predicts WHO/ISUP nuclear grade in clear cell renal cell carcinoma.
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. Not yet cited in PubMed.
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Abstract
purposeTo evaluate a biphasic CT clustering-based habitat radiomics approach for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) nuclear grade in clear cell renal cell carcinoma (ccRCC) and assess the discriminatory value of different tumor subregions.
methodsThis retrospective study collected 473 ccRCC patients (training: n = 337; test: n = 136). Thin-section corticomedullary phase (CMP) and excretory phase (EP) CT images were preprocessed and registered. NnU-Net was used for automated segmentation of tumor. Tumor habitats were generated via k-means clustering of three parameters: CMP attenuation values (CMP_HU), EP attenuation values (EP_HU), and their interphase difference (ΔHU). Radiomics features were extracted from whole-tumor volumes, individual subregions, and subregional combinations, followed by feature selection and model development.
resultsFive distinct subregions were identified. Subregion 4 ( representing the tumor viable-necrosis transition zone ) -based Random Forest model achieved diagnostic performance statistically comparable to whole-tumor-based model in both CMP ( AUC: 0.773 vs. matched whole-tumor AUC 0.819, DeLong' p = 0.152 ) and EP ( AUC: 0.810 vs. matched whole-tumor AUC 0.827, DeLong' p = 0.593 ). Subregion 2 ( necrotic core ) -based model showed similar efficacy to whole-tumor model in EP ( AUC: 0.771 vs. matched whole-tumor AUC 0.802, DeLong' p = 0.219 ). Multi-subregion models containing Subregion 4 maintained statistically comparable performance with whole-tumor models, with Subregion 1+4 demonstrating optimal performance.
conclusionBiphasic CT clustering-based habitat analysis reveals that specific tumor subregions-particularly the tumor viable-necrosis transitional zone-can approximate whole-tumor diagnostic performance for ccRCC nuclear grading. This method provides spatial mapping of tumor heterogeneity through visualizable CT subregions, offering a potential complementary tool for non-invasive ccRCC characterization. Further prospective multicenter validation with histopathological correlation is needed before clinical implementation.
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