Fig. 1: Features selection, SHAP analysis and models assessment. | npj Precision Oncology

Fig. 1: Features selection, SHAP analysis and models assessment.

From: Sub-regional radiomics combining multichannel 2-dimensional or 3-dimensional deep learning for predicting neoadjuvant chemo-immunotherapy response in esophageal squamous cell carcinoma: a multicenter study

Fig. 1

A Features selection with LASSO regression in sub-region1 radiomics model (left panel), multichannel 2D DL model (middle panel), and 3D DL model (right panel). B, C Feature importance based on XGBoost algorithm with SHAP analysis in sub-region1 model (left panel), multichannel 2D DL model (middle panel), and 3D DL model (right panel). D The ROC curves of DLRad1, DLRad2, 2D DL, 3D DL, and sub-region1 radiomics model in training set (left panel), internal validation set (middle panel), and external validation set (right panel). Note: 2D two-dimensional, 3D three-dimensional, DL deep learning, LASSO least absolute shrinkage and selection operator, ROC receiver operating characteristic, SHAP Shapley Additive Explanations, XGBoost eXtreme Gradient Boosting.

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