Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology

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. Different clustering patterns with clinical, histologic, immunohistochemical, and molecular information on the brain cancer population simulation and information gain in the glioma simulation model for clinical decision‐making. Dimensionality reduction by principal component analysis is shown in A1–A4, and by t‐stochastic neighbor embedding in B1–B4, with the features delineated on the top of each graph. Each color represents a unique diagnosis in the WHO classification scheme. (A1 and B1) Dimensionality reduction with clinical features alone demonstrates only a few visible clusters. (A2 and B2) Incorporating clinical history with histology generates the commencement of clear layering in PCA and discrete clusters with t‐SNE. (A3 and B3) Inclusion of immunohistochemical data impro