Cellular Density in Adult Glioma, Estimated with MR Imaging Data and a Machine Learning Algorithm, Has Prognostic Power ApproachingWorld Health Organization Histologic Grading in a Cohort of 1181 Patients

Research output: Contribution to journalArticlepeer-review

Abstract

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/mm2 was associated with a worse prognosis and a univariate hazard ratio of 4.21 (P,.001); the multivariate hazard ratio after adjusting for covariates of age and performance status was 2.91 (P,.001). The concordance index between maximum cellular density (adjusted for covariates) and survival was 0.734. The hazard ratio for a high World Health Organization grade (IV) was 7.57 univariate (P,.001) and 5.25 multivariate (P,.001). The concordance index for World Health Organization grading (adjusted for covariates) was 0.761. The maximum cellular density was an independent predictor of overall survival, and a Cox model using World Health Organization grade, maximum cellular density, age, and Karnofsky performance status had a higher concordance (C ¼ 0.764; range 0.748-0.781) than the component predictors. 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.

Original languageEnglish (US)
Pages (from-to)1411-1417
Number of pages7
JournalAmerican Journal of Neuroradiology
Volume43
Issue number10
DOIs
StatePublished - Oct 1 2022

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

MD Anderson CCSG core facilities

  • Biostatistics Resource Group

Fingerprint

Dive into the research topics of 'Cellular Density in Adult Glioma, Estimated with MR Imaging Data and a Machine Learning Algorithm, Has Prognostic Power ApproachingWorld Health Organization Histologic Grading in a Cohort of 1181 Patients'. Together they form a unique fingerprint.

Cite this