A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study

作者：Zongjie Wei, Xuesong Bai, Yingjie Xv, Shao‐Hao Chen, Siwen Yin, Yang Li, Fajin Lv, Mingzhao Xiao, Yongpeng Xie · 发表于：Insights into Imaging · 年份：2024 · DOI：10.1186/s13244-024-01840-3 · 被引用次数：25 · 研究领域：Bladder and Urothelial Cancer Treatments、Radiomics and Machine Learning in Medical Imaging、Ferroptosis and cancer prognosis

OBJECTIVE: To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation. METHODS: In this retrospective study, 207 patients with pathologically confirmed BCa were enrolled and divided into the training set (n = 154) and test set (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in the training set. Five radiomics-based ML models, namely logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost) and random forest (RF), were developed. The predictive performance of established ML models was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley additive explanation (SHAP) was used to analyze the interpretability of ML models. RESULTS: A total of 1218 radiomics features were extracted from the nephrographic phase CT images, and 11 features were filtered for constructing ML models. In the test set, the AUCs of LR, SVM, KNN, XGBoost, and RF were 0.803, 0.709, 0.679, 0.794, and 0.815, with corresponding accuracies of 71.7%, 69.8%, 60.4%, 75.5%, and 75.5%, respectively. RF was identified as the optimal classifier. SHAP analysis showed that texture features (gray level size zone matrix and gray level co-occurrence matrix) were significant predic...