title: Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study.
abstract: In this retrospective, IRB-exempt study, we analyzed data from 68 patients diagnosed with glioblastoma (GBM) in two institutions and investigated the relationship between tumor shape, quantified using algorithmic analysis of magnetic resonance images, and survival. Each patient's Fluid Attenuated Inversion Recovery (FLAIR) abnormality and enhancing tumor were manually delineated, and tumor shape was analyzed by automatic computer algorithms. Five features were automatically extracted from the images to quantify the extent of irregularity in tumor shape in two and three dimensions. Univariate Cox proportional hazard regression analysis was performed to determine how prognostic each feature was of survival. Kaplan Meier analysis was performed to illustrate the prognostic value of each feature. To determine whether the proposed quantitative shape features have additional prognostic value compared with standard clinical features, we controlled for tumor volume, patient age, and Karnofsky Performance Score (KPS). The FLAIR-based bounding ellipsoid volume ratio (BEVR), a 3D complexity measure, was strongly prognostic of survival, with a hazard ratio of 0.36 (95% CI 0.20-0.65), and remained significant in regression analysis after controlling for other clinical factors (P = 0.0061). Three enhancing-tumor based shape features were prognostic of survival independently of clinical factors: BEVR (P = 0.0008), margin fluctuation (P = 0.0013), and angular standard deviation (P = 0.0078). Algorithmically assessed tumor shape is statistically significantly prognostic of survival for patients with GBM independently of patient age, KPS, and tumor volume. This shows promise for extending the utility of MR imaging in treatment of GBM patients.
authors.affiliation: Electrical & Computer Engineering, Duke University, Box 90291, Durham, NC, 27708, USA. nicholas.czarnek@duke.edu.|Applied Machine Learning Lab, Duke University, Box 90291, 129 Hudson Hall, Durham, NC, 27708, USA. nicholas.czarnek@duke.edu.
authors.forename: Nicholas
authors.identifier: 0000-0002-7889-0184
authors.initials: N
authors.lastname: Czarnek
authors.affiliation: Department of Radiology, Duke University, 2424 Erwin Road (Hock Plaza), Suite 302, Durham, NC, 27705, USA.
authors.forename: Kal
authors.initials: K
authors.lastname: Clark
authors.affiliation: Department of Neurology, Duke University Medical Center, 3624, Durham, NC, 27710, USA.
authors.forename: Katherine B
authors.initials: KB
authors.lastname: Peters
authors.affiliation: Electrical & Computer Engineering, Duke University, Box 90291, Durham, NC, 27708, USA.|Department of Radiology, Duke University, 2424 Erwin Road (Hock Plaza), Suite 302, Durham, NC, 27705, USA.
authors.forename: Maciej A
authors.initials: MA
authors.lastname: Mazurowski
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D005260:Female; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D053208:Kaplan-Meier Estimate; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D055815:Young Adult
keywords: Glioblastoma; Magnetic resonance imaging; Prognosis; Shape; Survival