Fig. 3

Calibration and clinical utility of the gradient boosting machine (GBM) model. Calibration curves of predicted compared with observed CSS probability at 2 and 5 years in the training/validation A and the test B cohort. Decision curve analysis comparing the model with other strategies for predicting 2-and 5-year CSS in the training/validation C and the test D cohort. The y-axis measures the net benefit at a given threshold probability, which is estimated by summing the benefits (true-positive results) and subtracting the harms (false-positive results), weighting the latter by a factor related to the relative harm of an undetected disease compared with the harm of unnecessary treatment. The gray line represents the treat-all strategy (assuming all die of this disease), and the black line represents the treat-none strategy (assuming none die of this disease). GBM-based model provided greater net benefits compared with other strategies across the majority of threshold probabilities. CSS, cancer-specific survival