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Nathan Gaw
Arizona State University
CancerPathologyMagnetic resonance imagingMigraineMedicine
11Publications
4H-index
146Citations
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Publications 11
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#1Nathan Gaw (ASU: Arizona State University)H-Index: 4
#2Andrea Hawkins-Daarud (Mayo Clinic)H-Index: 11
Last. Jing Li (ASU: Arizona State University)H-Index: 18
view all 18 authors...
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-...
3 CitationsSource
#1Leland S. Hu (Mayo Clinic)H-Index: 10
#2Nathan Gaw (ASU: Arizona State University)H-Index: 4
Last. Jing Li (ASU: Arizona State University)H-Index: 18
view all 20 authors...
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#1Nathan Gaw (ASU: Arizona State University)H-Index: 4
#2Todd J. Schwedt (Mayo Clinic)H-Index: 28
Last. Jing Li (ASU: Arizona State University)H-Index: 18
view all 5 authors...
ABSTRACTReadily available imaging technologies have made it possible to acquire multiple imaging modalities with complementary information for the same patient. These imaging modalities describe different properties about the organ of interest, providing an opportunity for better diagnosis, staging and treatment assessments. However, existing research in combining multi-modality imaging data has not been transformed into a clinical decision support system due to lack of flexibility, accuracy, an...
1 CitationsSource
#1Kristin R. SwansonH-Index: 37
#2Nathan GawH-Index: 4
Last. Leland S. HuH-Index: 10
view all 17 authors...
1 CitationsSource
#1Pamela R. JacksonH-Index: 4
#2Nathan GawH-Index: 4
Last. Kristin R. SwansonH-Index: 37
view all 14 authors...
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#1Teresa WuH-Index: 32
#2Nathan GawH-Index: 4
Last. Bhavika K. PatelH-Index: 9
view all 11 authors...
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#1Catherine D. Chong (Mayo Clinic)H-Index: 12
#2Nathan Gaw (ASU: Arizona State University)H-Index: 4
Last. Todd J. Schwedt (Mayo Clinic)H-Index: 28
view all 6 authors...
BackgroundThis study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging (rs-fMRI) data that distinguish between individual migraine patients and healthy controls.MethodsThis study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain cla...
20 CitationsSource
#1Leland S. Hu (St. Joseph's Hospital and Medical Center)H-Index: 10
#2Shuluo Ning (ASU: Arizona State University)H-Index: 3
Last. J. Ross Mitchell (ASU: Arizona State University)H-Index: 28
view all 27 authors...
Abstract Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and te...
44 CitationsSource
#1Catherine D. Chong (Mayo Clinic)H-Index: 12
#2Nathan GawH-Index: 4
Last. Todd J. Schwedt (Mayo Clinic)H-Index: 28
view all 6 authors...
OBJECTIVE: To classify migraine by applying a novel machine-learning approach to resting-state functional connectivity magnetic resonance neuroimaging (rs-fMRI) data. BACKGROUND: Migraine is a prevalent neurological disorder that is associated with structural and functional brain alterations in regions associated with the perception and modulation of pain. This study explored the use of machine-learning techniques to develop discriminative brain-connectivity biomarkers from rs-fMRI data that dis...
#1Leland S. Hu (St. Joseph's Hospital and Medical Center)H-Index: 10
#2Shuluo Ning (ASU: Arizona State University)H-Index: 3
Last. Jing Li (ASU: Arizona State University)H-Index: 18
view all 25 authors...
Background Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use m...
42 CitationsSource
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