Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/54739

Título:

Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology

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Autor:

Cevik, Lokman
Vázquez Landrove, Marilyn
Aslan, Mehmet Tahir
Khammad, Vasilii
Garagorry Guerra, Francisco José
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
Ersen Danyeli, Ayca
Pearce, Thomas
Felicella, Michelle
Eschbacher, Jennifer
Arakaki, Naomi
Martinetto, Horacio
Parwani, Anil
Thomas, Diana L.
Otero, José Javier

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Tipo de documento:

Artículo

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Palabras clave:

1p/19q codeletion
cIMPACT
Glioma
Image segmentation
Information theory
Machine learning

Descriptores:

NEOPLASIAS ENCEFÁLICAS
PATOLOGÍA
ABERRACIONES CROMOSÓMICAS
CROMOSOMAS HUMANOS PAR 1
CROMOSOMAS HUMANOS PAR 19
GLIOMA
ECOSISTEMA
HUMANOS
HIBRIDACIÓN FLUORESCENTE IN SITU
GENÉTICA
TEORÍA DE LA INFORMACIÓN
ISOCITRATO DESHIDROGENASA
MUTACIÓN
NEUROPATOLOGÍA
PROTEÍNA P53 SUPRESORA DE TUMOR
FLUJO DE TRABAJO

Año de publicación:

2022

Contenido:

Resumen:

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.

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Wiley

EN:

Brain pathology. 2022;32(5)

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Citación:

Cevik L, Vázquez Landrove M, Aslan M y otros. Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology. Brain pathology [en línea]. 2022;32(5). 17 p.

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Licencia Creative Commons Atribución (CC - By 4.0)

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