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

TitleMachine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas.
Publication TypeJournal Article
Year of Publication2022
AuthorsLiechty B, Xu Z, Zhang Z, Slocum C, Bahadir CD, Sabuncu MR, Pisapia DJ
JournalSci Rep
Date Published2022 Dec 31
KeywordsBrain Neoplasms, Glioma, Humans, Isocitrate Dehydrogenase, Machine Learning, Magnetic Resonance Imaging, Mutation

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 human and ML predictions demonstrates predictive performance comparable to the consensus of two expert neuropathologists (hybrid classifier AUC = 0.921 vs. two-neuropathologist consensus AUC = 0.920). We also show that models trained at different levels of magnification exhibit different types of errors, supporting the value of aggregation across spatial scales in the ML approach. Finally, we present a detailed interpretation of our multi-scale ML ensemble model which reveals that predictions are driven by human-identifiable features at the patch-level.

Alternate JournalSci Rep
PubMed ID36587030
PubMed Central IDPMC9805452
Related Faculty: 
Benjamin L. Liechty, M.D. David Pisapia, M.D.

Pathology & Laboratory Medicine 1300 York Avenue New York, NY 10065 Phone: (212) 746-6464
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