Publication

Abstract : Objective: To explore the utility of first-order MRI-texture analysis (TA) parameters in predicting histologic grade and muscle invasion in urinary bladder cancer (UBC). Methods: After ethical clearance, 40 patients with UBC, who were imaged on a 3.0-Tesla scanner, were retrospectively included. Using the TexRADTM platform, two readers placed freehand ROI on the sections demonstrating the largest dimension of the tumor, evaluating only one tumor per patient. Interobserver reproducibility was assessed using the intraclass correlation coefficient (ICC). Mann–Whitney U test and ROC curve analysis were used to identify statistical significance and select parameters with high class separation capacity (AUC >0.8), respectively. Pearson’s test was used to identify redundancy in the results. Results: All texture parameters showed excellent ICC. The best parameters in differentiating high and low-grade tumors were mean/ mean of positive pixels (MPP) at SSF 0 (AUC: 0.897) and kurtosis at SSF 5 (AUC: 0.828) on the ADC images. In differentiating muscle invasive from non-muscle invasive tumors, mean/ MPP at SSF 0 on the ADC images showed AUC >0.8; however, this finding resulted from the confounding effect of high-grade histology on the ADC values of muscle invasive tumors. Conclusion: MRI-TA generated few parameters which were reproducible and useful in predicting histologic grade. No independent parameters predicted muscle invasion. Advances in knowledge: There is lacuna in the literature concerning the role of MRI-TA in the prediction of histologic grade and muscle invasion in UBC. Our study generated a few first-order parameters which were useful in predicting high-grade histology.