Open AccessInformation theory approaches to improve glioma diagnostic workflows in surgical neuropathology
Author(s) -
Cevik Lokman,
Landrove Marilyn Vazquez,
Aslan Mehmet Tahir,
Khammad Vasilii,
Garagorry Guerra Francisco Jose,
CabelloIzquierdo Yolanda,
Wang Wesley,
Zhao Jing,
Becker Aline Paixao,
Czeisler Catherine,
Rendeiro Anne Costa,
Véras Lucas Luis Sousa,
Za Maicon Fernando,
Reis Rui Manuel,
Matsushita Marcus de Medeiros,
Ozduman Koray,
Pamir M. Necmettin,
Ersen Danyeli Ayca,
Pearce Thomas,
Felicella Michelle,
Eschbacher Jennifer,
Arakaki Naomi,
Martinetto Horacio,
Parwani Anil,
Thomas Diana L.,
Otero José Javier
Publication year - 2022
Publication title -
brain pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.986
H-Index - 132
eISSN - 1750-3639
pISSN - 1015-6305
DOI - 10.1111/bpa.13050
Subject(s) - atrx , neuropathology , workflow , computer science , imaging biomarker , glioma , feature (linguistics) , medicine , bioinformatics , radiology , magnetic resonance imaging , pathology , biology , mutation , disease , cancer research , database , biochemistry , linguistics , philosophy , gene
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.