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DOI 10.1016/j.ejrad.2025.112104

Медицинская библиотека · European journal of radiology · 2025

Contribution of lesion shape features to the VI-RADS for predicting muscle invasion in bladder tumors.

Doğan Mahmut Bilal, Gündüz Nesrin, Kazan Hüseyin Özgür, Çakıcı Mehmet Çağlar, Yıldırım Asıf, Erdem Gülnur

СтатьяНаблюдательное исследованиеПеревод в очереди

Аннотация

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.

Для цитирования: Doğan Mahmut Bilal, Gündüz Nesrin, Kazan Hüseyin Özgür, Çakıcı Mehmet Çağlar, Yıldırım Asıf, Erdem Gülnur. Contribution of lesion shape features to the VI-RADS for predicting muscle invasion in bladder tumors.. European journal of radiology. 2025. DOI: 10.1016/j.ejrad.2025.112104.

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