Development of a prediction model for chemotherapy and immunotherapy response in esophageal squamous cell carcinoma patients using machine learning algorithms

Objective‍ ‍To develop models for predicting response to chemotherapy combined with immunotherapy in patients with esophageal squamous carcinoma with various machine learning algorithms, and then select the optimal model. Methods‍ ‍A retrospective study was performed for 174 patients with esophageal squamous cell carcinoma undergoing chemotherapy combined with immunotherapy admitted in Department of Thoracic Surgery of the First Affiliated Hospital of Army Medical University from January 2022 to December 2023. The CT scans and clinical information were collected before treatment. They were randomly divided into a training set (n=122) and a testing set (n=52) in a ratio of 7∶3. CT radiomic features were extracted and selected, and then 5 machine-learning algorithms were employed to establish the prediction models, including radiomics model and clinical-radiomics model. Five-fold cross-validation was conducted on the training set, and the performance of the prediction models was evaluated on the testing set using receiver operating characteristic (ROC) curve and the F1 score. The best-performing model was further explained using local interpretable model-agnostic explanations (LIME) algorithm. Results‍ ‍Among the 174 patients, 115 (66.1%) achieved clinical remission. From the clinical information and CT images, 1 clinical features and 10 radiomic features were identified. The area under of ROC curve (AUC) for the radiomics and clinical-radiomics models was 0.750 (95%CI: 0.616~0.883), and 0.766 (95%CI: 0.637~0.895), respectively. The F1 score of the optimal clinical-radiomics model was 0.829. LIME algorithm indicated that this best model demonstrated reliability in predicting individual samples. Conclusion‍ ‍The clinical-radiomics prediction model based on machine learning algorithm performs well, and can provide a reference for doctors' clinical decision-making by predicting the response to chemotherapy combined with immunotherapy in patients with esophageal squamous cell carcinoma.