Objective To discriminate the pathological grade of bladder urothelial carcinoma through grayscale ultrasound image-based radiomics analysis.Methods A retrospective analysis was conducted on 153 patients with bladder urothelial carcinoma confirmed by pathology in our hospital from April 2016 to May 2023.The grayscale ultrasound images were manually delineated to outline the tumor region of interest and extract radiomics features.LASSO feature selection was utilized for dimensionality reduction,followed by modeling with three machine learning methods to identify the best model.The performance of the models was evaluated using ROC curves,and the goodness-of-fit was assessed using the Hosmer-Lemeshow test and calibration curve.Furthermore,decision curve analysis was conducted to explore the clinical utility of the model.Results Among the three machine learning models,the Support vector machine model exhibited the best performance,with an AUC of 0.858(95%CI:0.787-0.928)on the training set and 0.832(95%CI:0.708-0.936)on the test set.The calibration curve demonstrated good consistency.The decision curve analysis also showed a high net benefit.Conclusion Grayscale ultrasound image-based radiomics has preoperative diagnostic value in distinguishing the pathological grading of bladder urothelial carcinoma,which contributes to precise clinical diagnosis and treatment.
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