Extended Data Fig. 5: CHIEF predicted IDH status of glioma samples in several patient cohorts.
From: A pathology foundation model for cancer diagnosis and prognosis prediction
CHIEF classified glioma samples with and without IDH mutation. Here, we showed that CHIEF successfully predicted IDH mutation status in both high and low histological grade groups defined by conventional visual-based histopathology assessment. a. Regions with increased cellularity and perinuclear halos received high model attention in IDH-mutant samples, while regions showing poorer cell adhesion received high attention in IDH-wildtype slides. We used samples from the MUV-GBM dataset as an example for this visualization. The bottom figures show the corresponding image tiles. Six experienced pathologists (see Methods) examined these tiles independently and annotated the morphological patterns correlated with regions receiving high and low attention. b. IDH-mutant gliomas from the six cohorts exhibit a similar bi-modal distribution along the attention score axis. In contrast, IDH-wildtype gliomas display an unimodal distribution with mostly low-attention image regions. We normalized the attention scores to a range from 0 to 1, representing the importance of each image tile to the prediction output by CHIEF. These analyses included samples from TCGA-GBM (n = 834), MUV-GBM (n = 507), HMS-GBM (n = 88), TCGA-LGG (n = 842), MUV-LGG (n = 365), and HMS-LGG (n = 82). In these violin plots, the central white dots represent the median, the thick black bars indicate the interquartile range (IQR), and the thin black lines (whiskers) extend to 1.5 times the IQR from the first and third quartiles. The width of the violin represents the density of data at different values.
