Cellular Density in Adult Glioma, Estimated with MR Imaging Data and a Machine Learning Algorithm, Has Prognostic Power Approaching World Health Organization Histologic Grading in a Cohort of 1181 Patients.
Journal
AJNR. American journal of neuroradiology
ISSN: 1936-959X
Titre abrégé: AJNR Am J Neuroradiol
Pays: United States
ID NLM: 8003708
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
received:
10
02
2022
accepted:
01
07
2022
pubmed:
16
9
2022
medline:
30
12
2022
entrez:
15
9
2022
Statut:
ppublish
Résumé
Recent advances in machine learning have enabled image-based prediction of local tissue pathology in gliomas, but the clinical usefulness of these predictions is unknown. We aimed to evaluate the prognostic ability of imaging-based estimates of cellular density for patients with gliomas, with comparison to the gold standard reference of World Health Organization grading. Data from 1181 (207 grade II, 246 grade III, 728 grade IV) previously untreated patients with gliomas from a single institution were analyzed. A pretrained random forest model estimated voxelwise tumor cellularity using MR imaging data. Maximum cellular density was correlated with the World Health Organization grade and actual survival, correcting for covariates of age and performance status. A maximum estimated cellular density of >7681 nuclei/mm Image-based estimation of glioma cellularity is a promising biomarker for predicting survival, approaching the prognostic power of World Health Organization grading, with added values of early availability, low risk, and low cost.
Sections du résumé
BACKGROUND AND PURPOSE
Recent advances in machine learning have enabled image-based prediction of local tissue pathology in gliomas, but the clinical usefulness of these predictions is unknown. We aimed to evaluate the prognostic ability of imaging-based estimates of cellular density for patients with gliomas, with comparison to the gold standard reference of World Health Organization grading.
MATERIALS AND METHODS
Data from 1181 (207 grade II, 246 grade III, 728 grade IV) previously untreated patients with gliomas from a single institution were analyzed. A pretrained random forest model estimated voxelwise tumor cellularity using MR imaging data. Maximum cellular density was correlated with the World Health Organization grade and actual survival, correcting for covariates of age and performance status.
RESULTS
A maximum estimated cellular density of >7681 nuclei/mm
CONCLUSIONS
Image-based estimation of glioma cellularity is a promising biomarker for predicting survival, approaching the prognostic power of World Health Organization grading, with added values of early availability, low risk, and low cost.
Identifiants
pubmed: 36109124
pii: ajnr.A7620
doi: 10.3174/ajnr.A7620
pmc: PMC9575543
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1411-1417Subventions
Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA249373
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM007093
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR003167
Pays : United States
Informations de copyright
© 2022 by American Journal of Neuroradiology.