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

作者：David Milewski, Hyun Jung, G. Thomas Brown, Yanling Liu, Ben Somerville, Curtis Lisle, Marc Ladanyi, Erin R. Rudzinski, Hyoyoung Choo‐Wosoba, Donald A. Barkauskas, Tammy Lo, David Hall, Corinne M. Linardic, Jun S. Wei, Hsien-Chao Chou, Stephen X. Skapek, Rajkumar Venkatramani, Peter K. Bode, Seth M. Steinberg, George Zaki, Igor B. Kuznetsov, Douglas S. Hawkins, Jack F. Shern, Jack Collins, Javed Khan · 年份：2025 · DOI：10.1158/1078-0432.30705735 · 研究领域：AI in cancer detection、Artificial Intelligence in Healthcare and Education、Explainable Artificial Intelligence (XAI)

<p><i>MYOD1</i> gene mutation prediction from H&E images. <b>A,</b> Workflow for deep learning of <i>MYOD1</i> mutations from FN-RMS WSIs. <b>B</b> and <b>C,</b> Representative (<b>B</b>) H&E images and (<b>C</b>) class activation maps of a MYOD1 wild-type tumor and a tumor with a MYOD1 p.L122R mutation (VAF = 0.919). <b>D,</b> Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. <b>E,</b> Performance statistics for <i>MYOD1</i> mutation prediction. <b>F,</b> Average ROC curve for <i>MYOD1</i> mutation prediction using validation data. <b>G,</b> Plot of <i>MYOD1</i> mutation positive ratio thresholds with test datasets (blue = known <i>MYOD1</i> wild-type; red = known <i>MYOD1</i> mutant) and independent dataset of known <i>MYOD1</i>-mutant tumors <i>n</i> = 10 (green) shown as geometric mean ± 1 geometric SD.</p>