ArticleAnnals of surgical oncology2025
Intra- and Peritumoral CT-Based Radiomics for Assessing Pathologic T-Staging in Clear Cell Renal Cell Carcinoma: A Multicenter Study.
Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- CT-Based Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction.Journal of clinical medicine · 2026Review
- Review
- Peritumoral Fat Radiomics for Dual Prediction of TNM Stage and Histological Grade in Clear Cell Renal Cell Carcinoma: Discovery of Target-Specific Optimal Imaging Distances.Diagnostics (Basel, Switzerland) · 2026Article
- Computed tomography radiomics-derived nomogram for prediciting early renal function decline after partial nephrectomy in renal cell carcinoma: a multicenter development/validation study.International journal of surgery (London, England) · 2026Article
- The value of dual-energy CT radiomics in evaluating vascular maturity and prognosis in clear cell renal cell carcinoma.Discover oncology · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundA radiomics model constructed from the intratumoral region of computed tomography (CT) can predict the pathologic T stage of clear cell renal cell carcinoma (ccRCC). However, the predictive capability of the radiomics model that incorporates both intra- and peritumoral regions of CT for the pathologic T stage in ccRCC patients has not been reported to date.
methodsThis study enrolled 250 patients with ccRCC who underwent laparoscopic surgery. Three radiomics models were developed based on the intra- and peritumoral regions. The sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves of each model were analyzed. Decision curve analysis (DCA) and calibration curves were used to assess the net benefit and calibration ability of the models. Additionally, the diagnostic performance of the different models were compared with that of radiologists.
resultsThe radiomics model based on the intra- and peritumoral regions at 5 mm exhibited the strongest performance, with area under curve values of 0.91 (95 % confidence interval [CI], 0.8551-0.9650), 0.85 (95 % CI, 0.7490-0.9517), and 0.873 (95 % CI, 0.7612-0.9839) in distinguishing high and low T stages of ccRCC across the training, validation, and test sets, respectively. The model's accuracy in the training, validation, and test sets was 0.798, 0.732, and 0.769, with corresponding sensitivity values of 0.921, 0.857, and 0.882, and specificity values of 0.747, 0.690, and 0.729. The calibration curve demonstrated a high level of agreement between the predicted and actual outcomes, whereas the DCA showed that the model provided a meaningful net benefit.
conclusionsThe radiomics model based on the intra- and peritumoral regions of CT has certain value in distinguishing between high and low T stages of ccRCC.
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Registered trials
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