E D H Gates1,2, D Suki3, A Celaya1, J S Weinberg3, S S Prabhu3, R Sawaya3, J T Huse4, J P Long5, D Fuentes1, D Schellingerhout6. 1. From the Departments of Imaging Physics (E.D.H.G., A.C., D.F.). 2. University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences (E.D.H.G.), Houston, Texas. 3. Neurosurgery (D. Suki, J.S.W., S.S.P., R.S.). 4. Translational Molecular Pathology (J.T.H.). 5. Biostatistics (J.P.L.). 6. Neuroradiology and Imaging Physics (D. Schellingerhout) Dawid.Schellingerhout@mdanderson.org.
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 = 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.
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 = 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.
Authors: R J Jackson; G N Fuller; D Abi-Said; F F Lang; Z L Gokaslan; W M Shi; D M Wildrick; R Sawaya Journal: Neuro Oncol Date: 2001-07 Impact factor: 12.300
Authors: Felix Sahm; David Capper; Astrid Jeibmann; Antje Habel; Werner Paulus; Dirk Troost; Andreas von Deimling Journal: Arch Neurol Date: 2011-12-12
Authors: Evan D H Gates; Adrian Celaya; Dima Suki; Dawid Schellingerhout; David Fuentes Journal: J Appl Clin Med Phys Date: 2022-02-11 Impact factor: 2.102
Authors: Christian Hartmann; Bettina Hentschel; Matthias Simon; Manfred Westphal; Gabriele Schackert; Jörg C Tonn; Markus Loeffler; Guido Reifenberger; Torsten Pietsch; Andreas von Deimling; Michael Weller Journal: Clin Cancer Res Date: 2013-08-05 Impact factor: 13.801