Ma Xiao-Hui, Shu Liqi, Jia Xuan, Zhou Hai-Chun, Liu Ting-Ting, Liang Jia-Wei, Ding Yu-Shuang, He Min, Shu Qiang
The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Department of Neurology, Warren Alpert Medical School of Brown University, Providence, RI, United States.
Front Pediatr. 2022 May 23;10:873035. doi: 10.3389/fped.2022.873035. eCollection 2022.
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.
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 ( = 94) and test cohorts ( = 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 -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.
The SVM model was fitted with 15 radiomic features obtained by -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.
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.
开发并验证一种基于机器学习的CT影像组学方法,用于术前预测小儿患者肾母细胞瘤(WT)的分期(I期和非I期)。
回顾性研究了2014年至2021年期间在本中心接受增强计算机断层扫描(CT)的118例WT患者,并将其分为两组:I期疾病组和非I期疾病组。患者被随机分为训练队列(n = 94)和测试队列(n = 24)。从腹部CT术前门静脉期图像中提取了来自七个特征类别的总共1781个影像组学特征。采用合成少数过采样技术(SMOTE)处理不平衡数据集,随后进行t检验和最小绝对收缩和选择算子(LASSO)正则化进行特征选择。使用选定的信息性特征部署支持向量机(SVM)来开发预测模型。根据模型的准确性、敏感性和特异性评估模型性能。还绘制了受试者工作特征曲线(ROC)和ROC曲线下面积(AUC)来评估模型性能。
SVM模型拟合了训练数据集中通过t检验和LASSO获得的15个与WT分期相关的影像组学特征,并在测试数据集中表现出良好性能。训练数据集上的交叉验证AUC为0.79,95%置信区间(CI)为0.773 - 0.815,变异系数为3.76%,而测试数据集上的AUC为0.81,准确性、敏感性和特异性分别为0.79、0.87和0.69。
基于CT图像提取的影像组学特征的SVM机器学习模型能够准确术前预测小儿患者WT的I期和非I期疾病,为WT分期研究提供了一种快速、无创的方法。