Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas
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 pr...
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Published in | Scientific reports Vol. 12; no. 1; p. 22623 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Nature Publishing Group UK
31.12.2022
Nature Publishing Group |
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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 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. |
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AbstractList | 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. 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 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. |
ArticleNumber | 22623 |
Author | Slocum, Cheyanne Xu, Zhuoran Pisapia, David J. Liechty, Benjamin Sabuncu, Mert R. Zhang, Zhilu Bahadir, Cagla D. |
Author_xml | – sequence: 1 givenname: Benjamin surname: Liechty fullname: Liechty, Benjamin organization: Department of Pathology and Laboratory Medicine, Weill Cornell Medicine – sequence: 2 givenname: Zhuoran surname: Xu fullname: Xu, Zhuoran organization: Department of Pathology and Laboratory Medicine, Weill Cornell Medicine – sequence: 3 givenname: Zhilu surname: Zhang fullname: Zhang, Zhilu organization: School of Electrical and Computer Engineering, Cornell University and Cornell Tech – sequence: 4 givenname: Cheyanne surname: Slocum fullname: Slocum, Cheyanne organization: School of Medicine, Weill Cornell Medicine – sequence: 5 givenname: Cagla D. surname: Bahadir fullname: Bahadir, Cagla D. organization: Meinig School of Biomedical Engineering, Cornell University – sequence: 6 givenname: Mert R. surname: Sabuncu fullname: Sabuncu, Mert R. email: msabuncu@cornell.edu organization: School of Electrical and Computer Engineering, Cornell University and Cornell Tech, Department of Radiology, Weill Cornell Medicine – sequence: 7 givenname: David J. surname: Pisapia fullname: Pisapia, David J. email: djp2002@med.cornell.edu organization: Department of Pathology and Laboratory Medicine, Weill Cornell Medicine |
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Snippet | While Machine Learning (ML) models have been increasingly applied to a range of histopathology tasks, there has been little emphasis on characterizing these... Abstract While Machine Learning (ML) models have been increasingly applied to a range of histopathology tasks, there has been little emphasis on characterizing... |
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SubjectTerms | 631/114/1305 631/67/1857 Brain Neoplasms - genetics Brain Neoplasms - pathology Glioma Glioma - genetics Glioma - pathology Histology Histopathology Humanities and Social Sciences Humans Isocitrate Dehydrogenase - genetics Learning algorithms Machine Learning Magnetic Resonance Imaging multidisciplinary Mutation Predictions Science Science (multidisciplinary) |
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Title | Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas |
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