Figure 3 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)
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.