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...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | , , , , , , , , , , , , , , , , , , , , , , , |
| 出版: |
2025
|
| 主題: | |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | <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> |
|---|