Abstract
ObjectiveTo explore the performance of ultrasound image-based radiomics in predicting World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of clear-cell renal cell carcinoma (ccRCC). MethodsA retrospective study was conducted via histopathological examination on participants with ccRCC from January 2021 to August 2023. Participants were randomly allocated to a training set and a validation set in a 3:1 ratio. The maximum cross-sectional image of the lesion on the preoperative ultrasound image was obtained, with the region of interest (ROI) delineated manually. Radiomic features were computed from the ROIs and subsequently normalized using Z-scores. Wilcoxon test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature reduction and model development. The performance of the model was estimated by indicators including area under the curve (AUC), sensitivity and specificity. ResultsA total of 336 participants (median age, 57 y; 106 women) with ccRCC were finally included, of whom 243 had low-grade tumors (grade 1–2) and 93 had high-grade tumors (grade 3–4). A total of 1163 radiomic features were extracted from the ROIs for model construction and 117 informative radiomics features selected by Wilcoxon test were submitted to LASSO. Our ultrasound-based radiomics model included 51 features and achieved AUCs of 0.90 and 0.79 for the training and validation sets, respectively. Within the training set, the sensitivity and specificity measured 0.75 and 0.92, respectively, whereas in the validation set, the sensitivity and specificity measured 0.65 and 0.84, respectively. In the subgroup analysis, for the training and validation sets Philips AUCs were 0.91 and 0.75, Toshiba AUCs were 0.82 and 0.90, and General Electric AUCs were 0.95 and 0.82, respectively. ConclusionUltrasound-based radiomics can effectively predict the WHO/ISUP grading of ccRCC.
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