Gregory Thomas Brown

Supplemental Figure S1. Performance of A.I. model in classifying multiple samples from the same patient.

Deep learning algorithms can predict event-free and overall survival from H&E histology alone. A, Schematic for training a convolutional neural network for survival. Patients with FN-RMS (n = 264) were partitioned into three groups based on EFS length to ensure even sampling and fed into a deep learning architecture with a Cox proportional hazard layer to assess risk. B, Kaplan–Meier curves for EFS based on COG clinical risk group assessment. C, Kaplan–Meier curves for event-free survival based on A.I. hazard prediction. D, Kaplan–Meier curves for OS based on COG clinical risk group assessment. E, Kaplan–Meier curves for OS based on the same A.I.-predicted risk grouping as in C.

Prediction of RAS pathway mutations using a trained CNN. A, Workflow for deep learning of RAS pathway mutations from FN-RMS WSIs. B and C, Representative (B) H&E images and (C) class activation maps of a RAS pathway wild-type tumor and a tumor with a KRAS p.G12C mutation (VAF = 0.659). D, Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. E, Statistics for confusion matrix. F, Average ROC curve from holdout test data.

Supplemental Figure S4. Sample partitioning for training a MYOD1 mutation predictive model using K-fold cross-validation.

Fusion status prediction from H&E images. A, Workflow for deep learning of normal tissue, FN-RMS, and FP-RMS. B and C, Representative (B) H&E images and (C) class activation maps as predicted by the trained model. D, Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. E, Performance statistics for FP-RMS and FN-RMS prediction. Note: Normal tissues (n = 15) were classified with 100% sensitivity and 100% specificity and are included in performance metric calculations. F, Average ROC curve for FP-RMS vs. FN-RMS vs. normal predictions using holdout test data. G, Workflow for testing the trained algorithm against an independent RMS TMA dataset. H, Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. I, Performance statistics for FP-RMS and FN-RMS predictions on an independent dataset. J, Average ROC curve for FP-RMS vs. FN-RMS using an independent RMS tissue dataset.

Supplemental Description of the Rhabdomyosarcoma Web-based Application

Supplemental Figure S2. Sample partitioning for training and testing a TP53 mutation predictive model using K-fold cross-validation.

TP53 gene mutation prediction from H&E images. A, Workflow for deep learning of TP53 mutations from FN-RMS WSIs. B and C, Representative (B) H&E images and (C) class activation maps of a TP53 wild-type tumor and a tumor with a TP53 p.P278T mutation (VAF = 0.474). D, Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. E, Performance statistics for TP53 mutation prediction. F, Average ROC curve for TP53 mutation prediction using holdout test data. G, Dot plot of TP53 mutant samples (n = 15) showing relationship between TP53 mutation VAF and A.I. positive prediction probability. Statistical analysis was performed using the Mann–Whitney U test.