Figure 2 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)
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.