학술논문
Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology
Document Type
Report
Author
Cevik, Lokman; Landrove, Marilyn Vazquez; Aslan, Mehmet Tahir; Khammad, Vasilii; Guerra, Francisco Jose Garagorry; Cabello‐Izquierdo, Yolanda; Wang, Wesley; Zhao, Jing; Becker, Aline Paixao; Czeisler, Catherine; Rendeiro, Anne Costa; Véras, Lucas Luis Sousa; Zanon, Maicon Fernando; Reis, Rui Manuel; Matsushita, Marcus De Medeiros; Ozduman, Koray; Pamir, M. Necmettin; Danyeli, Ayca Ersen; Pearce, Thomas; Felicella, Michelle; Eschbacher, Jennifer; Arakaki, Naomi; Martinetto, Horacio; Parwani, Anil; Thomas, Diana L.; Otero, José Javier
Source
Brain Pathology. September 2022, Vol. 32 Issue 5
Subject
Language
English
ISSN
1015-6305
Abstract
Abbreviations INTRODUCTION The integration of molecular subtyping in brain tumor diagnosis for patients in low‐resource settings represents a huge inequity in clinical care and is the premier leadership challenge facing [...]
: 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.
: 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.