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
<p><i>TP53</i> gene mutation prediction from H&E images. <b>A,</b> Workflow for deep learning of <i>TP53</i> mutations from FN-RMS WSIs. <b>B</b> and <b>C,</b> Representative (<b>B</b>) H&E images and (<b>C&l...
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| Altres autors: | , , , , , , , , , , , , , , , , , , , , , , , |
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2025
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| Sumari: | <p><i>TP53</i> gene mutation prediction from H&E images. <b>A,</b> Workflow for deep learning of <i>TP53</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 TP53 wild-type tumor and a tumor with a TP53 p.P278T mutation (VAF = 0.474). <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>TP53</i> mutation prediction. <b>F,</b> Average ROC curve for <i>TP53</i> mutation prediction using holdout test data. <b>G,</b> Dot plot of <i>TP53</i> mutant samples (<i>n</i> = 15) showing relationship between <i>TP53</i> mutation VAF and A.I. positive prediction probability. Statistical analysis was performed using the Mann–Whitney <i>U</i> test.</p> |
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