ArticleBMC medical imaging2025
An interpretable contrast-enhanced CT radiomics-based pipeline incorporating automatic segmentation for predicting ISUP grade group in ccRCC.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesNon-invasive assessment of clear cell renal cell carcinoma (ccRCC) International Society of Urological Pathology grade group (ISUP GG) remains a clinical challenge. This study aims to develop an interpretable radiomics-based pipeline incorporating automatic segmentation for predicting the ISUP GG in ccRCC.
methodsThis retrospective study included 276 pathologically confirmed ccRCC patients who underwent preoperative contrast-enhanced CT (193 train, 83 test), and were stratified by ISUP GG (1-2 vs. 3-4). Radiomics features were extracted from the auto‑segmented tumor regions on corticomedullary-phase images. Feature selection was performed using Recursive Feature Elimination Cross-Validation across 5 classifiers. Along with clinical information, we ultimately constructed radiomics, clinical, and combined models. Models comprehensively evaluated via multiple metrics, with partial results visualized through Receiver Operating Characteristic and calibration curves, and decision curve analysis, and further interpretability analysis conducted.
resultsThe combined model significantly outperformed the clinical and radiomics models, achieving an AUC of 0.907 (95% CI 0.868-0.947) vs. 0.802 (95% CI 0.735-0.869) and 0.769 (95% CI 0.702-0.836), both p < 0.001. In the test, the combined model significantly outperformed the clinical model, achieving an AUC of 0.834 (95% CI 0.737-0.931) vs. 0.698 (95% CI 0.575-0.821), p = 0.001, but showed no significant difference compared with the radiomics model (AUC 0.834 [95% CI 0.737-0.931] vs. 0.796 [95% CI 0.695-0.897], p = 0.328). Calibration curves and DCA indicated that the combined model demonstrated good calibration and better clinical net benefit.
conclusionThe interpretable pipeline may have potential as a non-invasive tool for predicting ISUP GG in ccRCC.
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