Fig. 6: Overview of supervised machine learning model development and validation for predicting immune checkpoint inhibitor responses based on HistoTME predictions. | npj Precision Oncology

Fig. 6: Overview of supervised machine learning model development and validation for predicting immune checkpoint inhibitor responses based on HistoTME predictions.

From: Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images

Fig. 6

A Model development for response prediction: (1) HistoTME predictions are engineered into new features by taking pairwise sums, differences, products, and quotients. (2) random forest feature selection. (3) XGBoost trained for response prediction. B Feature network pairwise interactions of 18 selected features. Arrow endpoints denote the signature subtracted or divided from the signature at the start point. C Test set receiver operating characteristic (ROC) curve of the model trained on engineered features or TME signatures alone. Optimal cut point shown based on the Youden index. Kaplan Meier plot depicting overall survival of the test set stratified by AI response prediction for D all patients that received anti-PD1/PD-L1 treatment and E first-line immunotherapy (IO)-treated patients. F Shapley additive explanation (SHAP) summary plot ordered by SHAP importance.

Back to article page