ArticleAnnals of surgical oncology2026
Intratumoral and Peritumoral Fat CT‑Based Radiomics for Predicting Recurrence Risk in Non-Muscle-Invasive Bladder Cancer: A Two-Center Study.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 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
- MRI-based deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
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7 authors.
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Abstract
backgroundNon-muscle-invasive bladder cancer (NMIBC) has a high risk of recurrence, and multiple surgeries increase the disease burden on patients. Using computed tomography (CT)-based machine learning, this study established a pre-treatment recurrence prediction model incorporating tumor and peritumoral fat characteristics. This approach may guide early clinical interventions.
methodsIn this retrospective study, 208 NMIBC patients who underwent enhanced CT before transurethral resection of bladder tumor (TURBT) with intravesical chemotherapy were collected from two hospitals. The radiomics features were extracted from the intratumoral region and peritumoral fat region (5 mm), followed by least absolute shrinkage and selection operator (LASSO) selection. Three radiomics models were developed: intratumoral, peritumoral-fat, and combined intratumoral-peritumoral model. Kaplan-Meier analysis assessed the association between radiomics features and recurrence-free survival. Cox analyses identified clinical risk factors integrated with radiomics score into a clinical-radiomics nomogram. Time-dependent ROC assessed the nomogram's predictive performance, and decision curve analysis evaluated its clinical utility.
resultsThe combined model based on logistic regression demonstrated superior discrimination, with area under the curve (AUC) values of 0.88 in the test set and 0.82 in the external validation set. The clinical-radiomics nomogram exhibited optimal performance in predicting early recurrence for NMIBC, with time-AUC values of 0.86 and 0.84 in the test and external validation sets, respectively. The nomogram showed better calibration and reclassification than the clinical model (net reclassification improvement, 0.736; p < 0.05).
conclusionsThe integrated radiomics-clinical model enhances the predictive capability compared with individual models, demonstrating significant value in predicting early recurrence of NMIBC. This approach offers a novel predictive strategy for assessing NMIBC recurrence risk.
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