Mu Zhou1, Baishali Chaudhury2, Lawrence O Hall3, Dmitry B Goldgof3, Robert J Gillies2, Robert A Gatenby2. 1. Stanford Center for Biomedical Informatics, Stanford University, Stanford, California, USA. 2. Department of Radiology, H. Lee Moffitt Cancer and Research Institute, Tampa, Florida, USA. 3. Department of Computer Science and Engineering, University of South Florida, Tampa, Florida, USA.
Abstract
PURPOSE: Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults. Most GBMs exhibit extensive regional heterogeneity at tissue, cellular, and molecular scales, but the clinical relevance of the observed spatial imaging characteristics remains unknown. We investigated pretreatment magnetic resonance imaging (MRI) scans of GBMs to identify tumor subregions and quantify their image-based spatial characteristics that are associated with survival time. MATERIALS AND METHODS: We quantified tumor subregions (termed habitats) in GBMs, which are hypothesized to capture intratumoral characteristics using multiple MRI sequences. For proof-of-concept, we developed a computational framework that used intratumoral grouping and spatial mapping to identify GBM tumor subregions and yield habitat-based features. Using a feature selector and three classifiers, experimental results from two datasets are reported, including Dataset1 with 32 GBM patients (594 tumor slices) and Dataset2 with 22 GBM patients, who did not undergo resection (261 tumor slices) for survival group prediction. RESULTS: In both datasets, we show that habitat-based features achieved 87.50% and 86.36% accuracies for survival group prediction, respectively, using leave-one-out cross-validation. Experimental results revealed that spatially correlated features between signal-enhanced subregions were effective for predicting survival groups (P < 0.05 for all three machine-learning classifiers). CONCLUSION: The quantitative spatial-correlated features derived from MRI-defined tumor subregions in GBM could be effectively used to predict the survival time of patients. LEVEL OF EVIDENCE: 2 J. MAGN. RESON. IMAGING 2017;46:115-123.
PURPOSE:Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults. Most GBMs exhibit extensive regional heterogeneity at tissue, cellular, and molecular scales, but the clinical relevance of the observed spatial imaging characteristics remains unknown. We investigated pretreatment magnetic resonance imaging (MRI) scans of GBMs to identify tumor subregions and quantify their image-based spatial characteristics that are associated with survival time. MATERIALS AND METHODS: We quantified tumor subregions (termed habitats) in GBMs, which are hypothesized to capture intratumoral characteristics using multiple MRI sequences. For proof-of-concept, we developed a computational framework that used intratumoral grouping and spatial mapping to identify GBM tumor subregions and yield habitat-based features. Using a feature selector and three classifiers, experimental results from two datasets are reported, including Dataset1 with 32 GBM patients (594 tumor slices) and Dataset2 with 22 GBM patients, who did not undergo resection (261 tumor slices) for survival group prediction. RESULTS: In both datasets, we show that habitat-based features achieved 87.50% and 86.36% accuracies for survival group prediction, respectively, using leave-one-out cross-validation. Experimental results revealed that spatially correlated features between signal-enhanced subregions were effective for predicting survival groups (P < 0.05 for all three machine-learning classifiers). CONCLUSION: The quantitative spatial-correlated features derived from MRI-defined tumor subregions in GBM could be effectively used to predict the survival time of patients. LEVEL OF EVIDENCE: 2 J. MAGN. RESON. IMAGING 2017;46:115-123.
Authors: M Iv; M Zhou; K Shpanskaya; S Perreault; Z Wang; E Tranvinh; B Lanzman; S Vajapeyam; N A Vitanza; P G Fisher; Y J Cho; S Laughlin; V Ramaswamy; M D Taylor; S H Cheshier; G A Grant; T Young Poussaint; O Gevaert; K W Yeom Journal: AJNR Am J Neuroradiol Date: 2018-12-06 Impact factor: 3.825
Authors: M Zhou; J Scott; B Chaudhury; L Hall; D Goldgof; K W Yeom; M Iv; Y Ou; J Kalpathy-Cramer; S Napel; R Gillies; O Gevaert; R Gatenby Journal: AJNR Am J Neuroradiol Date: 2017-10-05 Impact factor: 3.825