Figure 5 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group - David Milewski (15050643) (2025)
MYOD1 gene mutation prediction from H&E images. A, Workflow for deep learning of MYOD1 mutations from FN-RMS WSIs. B and C, Representative (B) H&E images and (C) class activation maps of a MYOD1 wild-type tumor and a tumor with a MYOD1 p.L122R mutation (VAF = 0.919). D, Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. E, Performance statistics for MYOD1 mutation prediction. F, Average ROC curve for MYOD1 mutation prediction using validation data. G, Plot of MYOD1 mutation positive ratio thresholds with test datasets (blue = known MYOD1 wild-type; red = known MYOD1 mutant) and independent dataset of known MYOD1-mutant tumors n = 10 (green) shown as geometric mean ± 1 geometric SD.