Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology

Lokman Cevik, Marilyn Vazquez Landrove, Mehmet Tahir Aslan, Vasilii Khammad, Francisco Jose Garagorry Guerra, Yolanda Cabello-Izquierdo, Wesley Wang, Jing Zhao, Aline Paixao Becker, Catherine Czeisler, Anne Costa Rendeiro, Lucas Luis Sousa Véras, Maicon Fernando Zanon, Rui Manuel Reis, Marcus de Medeiros Matsushita, Koray Ozduman, M. Necmettin Pamir, Ayca Ersen Danyeli, Thomas Pearce, Michelle FelicellaJennifer Eschbacher, Naomi Arakaki, Horacio Martinetto, Anil Parwani, Diana L. Thomas, José Javier Otero

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Aims: Resource-strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. Methods: We used simple information theory calculations on a brain cancer simulation model and real-world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto-adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH-mutant tumors. Results: Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH-mutant tumors. The predictive models may facilitate the reduction of false-positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. Conclusions: We posit that this approach provides an improvement on the cIMPACT-NOW workflow recommendations for IDH-mutant tumors and a framework for future resource and testing allocation.

Original languageEnglish (US)
Article numbere13050
JournalBrain Pathology
Volume32
Issue number5
DOIs
StatePublished - Sep 2022
Externally publishedYes

Keywords

  • 1p/19q codeletion
  • cIMPACT
  • glioma
  • image segmentation
  • information theory
  • machine learning

ASJC Scopus subject areas

  • General Neuroscience
  • Pathology and Forensic Medicine
  • Clinical Neurology

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