Fig. 2: Benchmarking models on internal and external validation.

A Classification performance (area under the receiver operating curve, AUROC) for prediction of tumor on internal validation cohort, i.e., 20% of Duke cohort. The three shades of blue represent different parts of a single cohort, Duke, with the centralized model in dark blue comprising 80% of Duke. Error bars represent the standard deviation of AUROC values for each model across five repetitions of the experiment. Individual data points outside the whiskers indicate outliers from the five repetitions. B Classification performance (area under the receiver operating curve, AUROC) for prediction of tumor on external validation cohort, i.e., UKA. The number of patients used for prediction per cohort is 122 for Duke and 422 for UKA. Error bars represent the standard deviation of AUROC values for each model across five repetitions of the experiment. Individual data points outside the whiskers indicate outliers from the five repetitions. C Classification performance for prediction of the tumor using 3D-Resnet101 model trained using real-world swarm learning across three cohorts: Duke, USZ, and CAM. Its classification performance was evaluated on an external validation cohort, UKA, for tumor prediction. Local model performance was assessed using AUROC and DeLong’s test to compare it with swarm models. Error bars represent the standard deviation of AUROC values for each model across five repetitions of the experiment. Individual data points outside the whiskers indicate outliers from the five repetitions. The significance level was set at p < 0.05 (*P < 0.05, **P < 0.001), and median patient scores from five repetitions determined superior performance. D Classification performance for the prediction of tumors using the 3D-Resnet101 model was trained using real-world swarm learning across three cohorts: Duke, USZ, and CAM. Its classification performance was evaluated on an external validation cohort, MHA, for tumor prediction. Local model performance was assessed using AUROC and DeLong’s test to compare it with swarm models. Error bars represent the standard deviation of AUROC values for each model across five repetitions of the experiment. Individual data points outside the whiskers indicate outliers from the five repetitions. The significance level was set at p < 0.05 (*P < 0.05, **P < 0.001), and median patient scores from five repetitions determined superior performance. The training cohort from Duke is consistently represented by the dark blue color throughout the figure.