Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma
Open accessAbstract
Background Timely and accurate intraoperative cryosection evaluations remain the gold standard for guiding surgical treatments for gliomas. However, the tissue-freezing process often generates artifacts that make histologic interpretation difficult. In addition, the 2021 WHO Classification of Tumors of the Central Nervous System incorporates molecular profiles in the diagnostic categories, so standard visual evaluation of cryosections alone cannot completely inform diagnoses based on the new classification system.Methods To address these challenges, we develop the context-aware Cryosection Histopathology Assessment and Review Machine (CHARM) using samples from 1,524 glioma patients from three different patient populations to systematically analyze cryosection slides.Findings Our CHARM models successfully identified malignant cells (AUROC = 0.98 ± 0.01 in the independent validation cohort), distinguished isocitrate dehydrogenase (IDH)-mutant tumors from wild type (AUROC = 0.79-0.82), classified three major types of molecularly defined gliomas (AUROC = 0.88-0.93), and identified the most prevalent subtypes of IDH-mutant tumors (AUROC = 0.89-0.97). CHARM further predicts clinically important genetic alterations in low-grade glioma, including ATRX, TP53, and CIC mutations, CDKN2A/B homozygous deletion, and 1p/19q codeletion via cryosection images.Conclusions Our approaches accommodate the evolving diagnostic criteria informed by molecular studies, provide real-time clinical decision support, and will democratize accurate cryosection diagnoses.Funding Supported in part by the National Institute of General Medical Sciences grant R35GM142879, the Google Research Scholar Award, the Blavatnik Center for Computational Biomedicine Award, the Partners' Innovation Discovery Grant, and the Schlager Family Award for Early Stage Digital Health Innovations.
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Several studies have shown potential for AI to facilitate intraoperative cancer diagnosis using label-free and cryosectioned tissue samples.
“several studies have demonstrated the potential of using AI to facilitate intraoperative diagnosis (e.g., during surgical resection) using label-free (40,41) and cryosectioned (42,43) tissue samples.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [42]
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- Authors
- Nasrallah, M. P., Zhao, J., Tsai, C. C., Meredith, D., Marostica, E., Ligon, K. L., Golden, J. A., & Yu, K.-H.
- Affiliations
- University of Pennsylvania (US)
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Dana-Farber Cancer Institute (US)
- Cedars-Sinai Medical Center (US)
- Corresponding author
- Yu, Kun-Hsing
- ORCID
- Venue
- Med
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 4
- Issue
- 8
- Pages
- 526-540.e4
- ISSN
- 2666-6340 (ISSN-L)
- PMC
- PMC10527821
- OpenAlex
- W4383533409
- Cited by
- 67
- References
- 52
- Citations by year
- 2026: 17 · 2025: 31 · 2024: 18 · 2023: 1
- FWCI
- 6.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 376 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Med (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Mass General Brigham Inc
- NIGMS
- Brigham and Women's Hospital
- Harvard Medical School
- Google LLC
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- glioma, IDH mutation, glioma molecular classification, ATRX mutation, TP53 mutation, CIC mutation, CDKN2A/B homozygous deletion, 1p/19q codeletion, low-grade glioma, histopathology image analysis, clinical decision support
- MeSH terms
- Machine Learning, Brain Neoplasms, Glioma, Homozygote, Humans, World Health Organization, Sequence Deletion
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