Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas.

ABSTRACT: While Machine Learning (ML) models have been increasingly applied to a range of histopathology tasks, there has been little emphasis on characterizing these models and contrasting them with human experts. We present a detailed empirical analysis comparing expert neuropathologists and ML models at predicting IDH mutation status in H&E-stained histology slides of infiltrating gliomas, both independently and synergistically. We find that errors made by neuropathologists and ML models trained using the TCGA dataset are distinct, representing modest agreement between predictions (human-vs.-human κ = 0.656; human-vs.-ML model κ = 0.598). While no ML model surpassed human performance on an independent institutional test dataset (human AUC = 0.901, max ML AUC = 0.881), a hybrid model aggregating h

SUBMITTER: Liechty B 

PROVIDER: S-EPMC9805452 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature