Figure 1 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)
Deep learning of histologic features from RMS tumor tissue. A, (i) Samples are randomly selected for training, validation, or holdout test groups with k-fold cross validation. Networks were trained to recognize (ii) basic histological characteristics and (iii) features associated with FOXO1 fusion status, or (iv) other relevant RMS mutations. (v) A predictive model was also developed to predict disease risk using only an H&E image. B, Representative H&E images (left), expert pathologist manual annotation (middle), and pixel-level segmentation results of the A.I. algorithm (right). C, Histogram of the weighted IoU scores from holdout test data (n = 29). Samples corresponding to B are indicated. D, Average and weighted intersection over union (IoU) scores of A.I. performance across a 3-fold cross-validation set compared with a pathologist manual annotation.