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An Orthotopic Bladder Tumor Model and the Evaluation of Intravesical saRNA Treatment
Published on: July 28, 2012
Contribution of lesion shape features to the VI-RADS for predicting muscle invasion in bladder tumors
Mahmut Bilal Doğan1, Nesrin Gündüz1, Hüseyin Özgür Kazan2
1İstanbul Medeniyet University, Faculty of Medicine, Department of Radiology, İstanbul, Türkiye.
Objective:
Muscle invasion in bladder cancer is a crucial factor influencing treatment decisions and prognosis. We hypothesize that the integration of tumor shape characteristics with the VI-RADS scoring system improves the predictive accuracy for assessing muscle invasion in bladder cancer.
Methods:
This prospective observational study included patients scheduled for transurethral resection of bladder tumor (TURBT) and/or cystectomy due to suspected bladder cancer between March 2022 and November 2024. All patients underwent multiparametric bladder MRI following the VI-RADS protocol. Tumor segmentation was performed using 3D Slicer to assess shape features, including sphericity, surface area, surface-volume ratio, elongation, and flatness. Mann-Whitney U tests were conducted to evaluate the association between shape features and muscle invasion, while ROC (Receiver Operating Characteristic) analysis determined threshold values.
Results:
The study included 119 patients (98 males, 21 females; mean age 66.9 ± 9.9 years, range 33-89). Using VI-RADS ≥ 3 as the threshold, sensitivity was 100 %, with a specificity of 65.91 %. For VI-RADS ≥ 4, sensitivity was 80.65 %, and specificity was 95.45 %. A significant relation was found between tumor sphericity and muscle invasion. For VI-RADS ≥ 3B, sensitivity was 100 %, and specificity was 66.67 %.
Conclusion:
The VI-RADS algorithm demonstrated high predictive accuracy for muscle invasion, particularly for tumors scored VI-RADS ≥ 4. Additionally, while a relation was identified between tumor sphericity and muscle invasion, its incorporation into the VI-RADS scoring system did not enhance the overall predictive performance of the algorithm.
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