The application of machine learning based on computed tomography images in the identification of renal tumors in children.

Abstract

The clinical manifestations of Wilms tumor and non-Wilms tumor in children are similar, and the only way to confirm the diagnosis is by postoperative pathology. Computed tomography (CT) is one of the main methods for preoperative diagnosis of the two, but it is also difficult to distinguish because it is easily affected by the subjective influence and the experience of the radiologists. The CT images of 82 children with renal tumors admitted to the Department of Pediatric Urology, Shandong Provincial Hospital from January 2011 to March 2022 were retrospectively analyzed. First, we drew the two-dimensional (2D) region of interest (ROI) of the largest cross-section on the corticomedullary phase (CMP) and nephrogenic phase (NP) images, and extracted seven types of 107 features in the ROI. Then, the texture features with similarity greater than 95% and repetition less than 90% were screened out, and the remaining texture features were further screened by analysis of variance (ANOVA) and recursive feature elimination (RFE). Finally, 15 texture feature were used to build the machine learning (ML) models. We used the synthetic minority oversampling technique (SMOTE) and 10-fold cross-validation to build ML models and verified them in the training, testing, and internal validation sets. The area under the receiver-operating characteristic curve (AUC) and calibration curve were used to evaluate the diagnostic performance. We collected 77 CMP and 81 NP images, which were randomly divided into the training set and the testing set according to the ratio of 7:3. In the internal validation of CMP, the Mean-PCC-ANOVA-5-AE pipeline model achieved the highest AUC 0.792 [95% confidence interval (CI): 0.653-0.930], and its accuracy (ACC), sensitivity (SEN), and specificity (SPE) were 0.833, 0.539 and 0.927, respectively. Correspondingly, in NP, the Mean-PCC-ANOVA-2-LR pipeline model achieved the highest AUC 0.655 (95% CI: 0.485-0.82) in the internal validation. The ACC, SEN, and SPE were 0.696, 0.539, and 0.744, respectively. The ML models based on CT images have good diagnostic efficiency in differentiating Wilms tumors from non-Wilms tumors in children.