Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma

Figure 2 Schematic of the nested cross-validation framework for model development and evaluation. After feature selection and standardization, a nested cross-validation framework was applied. The outer loop evaluated the model’s generalization performance, while the inner loop used AutoGluon, an automated machine learning framework, for feature selection and hyperparameter tuning. CV: Cross-validation.