Machine Learning-Based CT Radiomics Method for Identifying the Stage of Wilms Tumor in Children

Xiao-Hui Ma; Liqi Shu; Xuan Jia; Hai-Chun Zhou; Ting-Ting Liu; Jia-Wei Liang; Yu-Shuang Ding; Min He; Qiang Shu

doi:10.3389/fped.2022.873035

Machine Learning-Based CT Radiomics Method for Identifying the Stage of Wilms Tumor in Children

Front Pediatr. 2022 May 23:10:873035. doi: 10.3389/fped.2022.873035. eCollection 2022.

Authors

Xiao-Hui Ma¹, Liqi Shu², Xuan Jia¹, Hai-Chun Zhou¹, Ting-Ting Liu³, Jia-Wei Liang¹, Yu-Shuang Ding¹, Min He¹, Qiang Shu¹

Affiliations

¹ The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
² Department of Neurology, Warren Alpert Medical School of Brown University, Providence, RI, United States.
³ Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.

Abstract

Purpose: To develop and validate a machine learning-based CT radiomics method for preoperatively predicting the stages (stage I and non-stage I) of Wilms tumor (WT) in pediatric patients.

Methods: A total of 118 patients with WT, who underwent contrast-enhanced computed tomography (CT) scans in our center between 2014 and 2021, were studied retrospectively and divided into two groups: stage I and non-stage I disease. Patients were randomly divided into training cohorts (n = 94) and test cohorts (n = 24). A total of 1,781 radiomic features from seven feature classes were extracted from preoperative portal venous-phase images of abdominal CT. Synthetic Minority Over-Sampling Technique (SMOTE) was used to handle imbalanced datasets, followed by a t-test and Least Absolute Shrinkage and Selection Operator (LASSO) regularization for feature selection. Support Vector Machine (SVM) was deployed using the selected informative features to develop the predicting model. The performance of the model was evaluated according to its accuracy, sensitivity, and specificity. The receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC) was also arranged to assess the model performance.

Results: The SVM model was fitted with 15 radiomic features obtained by t-test and LASSO concerning WT staging in the training dataset and demonstrated favorable performance in the testing dataset. Cross-validated AUC on the training dataset was 0.79 with a 95 percent confidence interval (CI) of 0.773-0.815 and a coefficient of variation of 3.76%, while AUC on the test dataset was 0.81, and accuracy, sensitivity, and specificity were 0.79, 0.87, and 0.69, respectively.

Conclusions: The machine learning model of SVM based on radiomic features extracted from CT images accurately predicted WT stage I and non-stage I disease in pediatric patients preoperatively, which provided a rapid and non-invasive way for investigation of WT stages.

Keywords: CT; Wilms tumor; clinical staging; machine learning; radiomics.