Fig. 3: Performance of all the possible multimodal combinations, with a late fusion strategy and tree ensemble methods. | Nature Communications

Fig. 3: Performance of all the possible multimodal combinations, with a late fusion strategy and tree ensemble methods.

From: Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer

Fig. 3

The bar height corresponds to the performance metric (either ROC AUC or C-index) averaged across the 100 cross-validation schemes, and the error bar corresponds to ± 1 standard deviation, estimated across the 100 cross-validation schemes. A ROC AUCs associated with the prediction of 1-year death with XGBoost algorithms (top) and estimated with n = 77 patients. C-indexes associated with the prediction of OS with Random Survival Forest algorithms (bottom) and estimated with n = 79 patients. B ROC AUCs associated with the prediction of 6-month progression with XGBoost algorithms (top) and estimated with n = 75 patients. C-indexes associated with the prediction of PFS with Random Survival Forest algorithms (bottom) and estimated with n = 80 patients. * C: clinical, R: radiomic, P: pathomic, RNA: transcriptomic. Source data are provided as a Source Data file.

Back to article page