Figure 6 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>Deep learning algorithms can predict event-free and overall survival from H&E histology alone. <b>A,</b> Schematic for training a convolutional neural network for survival. Patients with FN-RMS (<i>n</i> = 264) were partitioned into three groups based on EFS leng...
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| Rannpháirtithe: | , , , , , , , , , , , , , , , , , , , , , , , |
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2025
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Cuir clib leis
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| Achoimre: | <p>Deep learning algorithms can predict event-free and overall survival from H&E histology alone. <b>A,</b> Schematic for training a convolutional neural network for survival. Patients with FN-RMS (<i>n</i> = 264) were partitioned into three groups based on EFS length to ensure even sampling and fed into a deep learning architecture with a Cox proportional hazard layer to assess risk. <b>B,</b> Kaplan–Meier curves for EFS based on COG clinical risk group assessment. <b>C,</b> Kaplan–Meier curves for event-free survival based on A.I. hazard prediction. <b>D,</b> Kaplan–Meier curves for OS based on COG clinical risk group assessment. <b>E,</b> Kaplan–Meier curves for OS based on the same A.I.-predicted risk grouping as in <b>C</b>.</p> |
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