RT Journal Article
SR Electronic
T1 Estimating Local Cellular Density in Glioma Using MR Imaging Data
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
DO 10.3174/ajnr.A6884
A1 E.D.H. Gates
A1 J.S. Weinberg
A1 S.S. Prabhu
A1 J.S. Lin
A1 J. Hamilton
A1 J.D. Hazle
A1 G.N. Fuller
A1 V. Baladandayuthapani
A1 D.T. Fuentes
A1 D. Schellingerhout
YR 2020
UL http://www.ajnr.org/content/early/2020/11/26/ajnr.A6884.abstract
AB BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.MATERIALS AND METHODS: We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.RESULTS: The random forest methodology estimated cellular density with R2 = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R2 = 0.52). Outputs were also reported as graphic maps.CONCLUSIONS: Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.AUCarea under the curveCDcellular densityDCEdynamic contrast-enhancedRFrandom forestT1CT1 postcontrast