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Automated histologic diagnosis of CNS tumors with machine learning

Authors
  • Khalsa, Siri Sahib S1
  • Hollon, Todd C1
  • Adapa, Arjun2
  • Urias, Esteban2
  • Srinivasan, Sudharsan2
  • Jairath, Neil2
  • Szczepanski, Julianne3
  • Ouillette, Peter3
  • Camelo-Piragua, Sandra3
  • Orringer, Daniel A4
  • 1 Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA
  • 2 Medical School, University of Michigan, Ann Arbor, MI 48109, USA
  • 3 Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
  • 4 Department of Neurosurgery, New York University, New York, NY 10012, USA
Type
Published Article
Journal
CNS Oncology
Publisher
Future Medicine
Publication Date
Jun 23, 2020
Volume
9
Issue
2
Identifiers
DOI: 10.2217/cns-2020-0003
PMID: 32602745
PMCID: PMC7341168
Source
PubMed Central
Keywords
License
Green

Abstract

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often established when a small specimen is sent for immediate interpretation by a neuropathologist. Access to neuropathologists may be limited in resource-poor settings, which has prompted several groups to develop machine learning algorithms for automated interpretation. Most attempts have focused on fixed histopathology specimens, which do not apply in the intraoperative setting. The greatest potential for clinical impact probably lies in the automated diagnosis of intraoperative specimens. Successful future studies may use machine learning to automatically classify whole-slide intraoperative specimens among a wide array of potential diagnoses.

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