ArticleWorld journal of urology2024
Tumor margin irregularity degree is an important preoperative predictor of adverse pathology for clinical T1/2 renal cell carcinoma and the construction of predictive model.
Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Development and validation of a CT algorithm based on intratumoral necrosis and tumor morphology to predict the nuclear grade of small (2-4 cm) solid clear cell renal cell carcinoma.BMC medical imaging · 2025Article
- Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology.World journal of surgical oncology · 2024Article
- Letter to the editor for the article"Tumor margin irregularity degree is an important preoperative predictor of adverse pathology for clinical T1/2 renal cell carcinoma and the construction of predictive model".World journal of urology · 2024Article
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6 authors.
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Abstract
purposeTo explore the critical role of the tumor margin irregularity degree (TMID) of renal tumors in predicting adverse pathology of patients with clinical T1/2 (cT1/2) renal cell carcinoma (RCC).
methodsA total of 821 patients with cT1/2 RCC undergoing nephrectomy in the Second Hospital of Tianjin Medical University between January 2017 and December 2020 were reviewed. The tumor margin irregularity (TMI) was classified into renal mass with locally raised protrusion and smooth margin called 'lobular', sharply and unsmooth nodular margin called 'spiculation', blurred margins between tumor and renal parenchyma or a completely irregular and non-elliptical shape. The ratio between the number of irregular cross-sections (X) and the number of total cross-sections from top to bottom occupied (Y) was defined as TMID (X/Y). The logistic regression was performed to determine the independent predictors of adverse pathology, and the Kaplan-Meier curve and log-rank test were used to analyze the survival outcomes.
resultsAmong 821 cT1/2 RCC patients, 245 (29.8%) had adverse pathology. The results of the univariate and multivariate logistic regressions showed that the age, tumor size, hemoglobin, and TMID were the independent predictors of adverse pathology. Incorporation of TMID could increase the discrimination of the predictive model with the area under curve (AUC) of ROC curves increasing from 0.725 to 0.808. Patients with adverse pathology or higher TMID both had significantly shorter recurrence-free survival (RFS).
conclusionThe nomogram model incorporated with TMID for predicting adverse pathology could increase its discrimination, calibration, and clinical application values, compared with the models without TMID.
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