Index by author
Cadena, G.
- EDITOR'S CHOICEAdult BrainOpen AccessDeep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in GliomasP. Chang, J. Grinband, B.D. Weinberg, M. Bardis, M. Khy, G. Cadena, M.-Y. Su, S. Cha, C.G. Filippi, D. Bota, P. Baldi, L.M. Poisson, R. Jain and D. ChowAmerican Journal of Neuroradiology July 2018, 39 (7) 1201-1207; DOI: https://doi.org/10.3174/ajnr.A5667
MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify IDH1 mutation status, 1p/19q codeletion, and MGMT promotor methylation status. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. The authors conclude that this shows the feasibility of a deep-learning CNN approach for the accurate classification of individual genetic mutations of both low- and high-grade gliomas and that the relevant MR imaging features acquired from an added dimensionality-reduction technique are concordant with existing literature, showing that neural networks are capable of learning key imaging components without prior feature selection or human directed training.
Capizzano, A.A.
- Head and Neck ImagingOpen AccessRole of the Apparent Diffusion Coefficient as a Predictor of Tumor Progression in Patients with ChordomaT. Sasaki, T. Moritani, A. Belay, A.A. Capizzano, S.P. Sato, Y. Sato, P. Kirby, S. Ishitoya, A. Oya, M. Toda and K. TakahashiAmerican Journal of Neuroradiology July 2018, 39 (7) 1316-1321; DOI: https://doi.org/10.3174/ajnr.A5664
Caruso, P.
- Pediatric NeuroimagingYou have accessBalanced Steady-State Free Precession Sequence (CISS/FIESTA/3D Driven Equilibrium Radiofrequency Reset Pulse) Increases the Diagnostic Yield for Spinal Drop Metastases in Children with Brain TumorsK. Buch, P. Caruso, D. Ebb and S. RinconAmerican Journal of Neuroradiology July 2018, 39 (7) 1355-1361; DOI: https://doi.org/10.3174/ajnr.A5645
Cha, S.
- EDITOR'S CHOICEAdult BrainOpen AccessDeep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in GliomasP. Chang, J. Grinband, B.D. Weinberg, M. Bardis, M. Khy, G. Cadena, M.-Y. Su, S. Cha, C.G. Filippi, D. Bota, P. Baldi, L.M. Poisson, R. Jain and D. ChowAmerican Journal of Neuroradiology July 2018, 39 (7) 1201-1207; DOI: https://doi.org/10.3174/ajnr.A5667
MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify IDH1 mutation status, 1p/19q codeletion, and MGMT promotor methylation status. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. The authors conclude that this shows the feasibility of a deep-learning CNN approach for the accurate classification of individual genetic mutations of both low- and high-grade gliomas and that the relevant MR imaging features acquired from an added dimensionality-reduction technique are concordant with existing literature, showing that neural networks are capable of learning key imaging components without prior feature selection or human directed training.
Chang, P.
- EDITOR'S CHOICEAdult BrainOpen AccessDeep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in GliomasP. Chang, J. Grinband, B.D. Weinberg, M. Bardis, M. Khy, G. Cadena, M.-Y. Su, S. Cha, C.G. Filippi, D. Bota, P. Baldi, L.M. Poisson, R. Jain and D. ChowAmerican Journal of Neuroradiology July 2018, 39 (7) 1201-1207; DOI: https://doi.org/10.3174/ajnr.A5667
MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify IDH1 mutation status, 1p/19q codeletion, and MGMT promotor methylation status. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. The authors conclude that this shows the feasibility of a deep-learning CNN approach for the accurate classification of individual genetic mutations of both low- and high-grade gliomas and that the relevant MR imaging features acquired from an added dimensionality-reduction technique are concordant with existing literature, showing that neural networks are capable of learning key imaging components without prior feature selection or human directed training.
Chang, T.
- Pediatric NeuroimagingOpen AccessCerebral Perfusion Is Perturbed by Preterm Birth and Brain InjuryE.S. Mahdi, M. Bouyssi-Kobar, M.B. Jacobs, J. Murnick, T. Chang and C. LimperopoulosAmerican Journal of Neuroradiology July 2018, 39 (7) 1330-1335; DOI: https://doi.org/10.3174/ajnr.A5669
Chen, L.
- FELLOWS' JOURNAL CLUBAdult BrainOpen AccessClinical Significance of Intraplaque Hemorrhage in Low- and High-Grade Basilar Artery Stenosis on High-Resolution MRIC. Zhu, X. Tian, A.J. Degnan, Z. Shi, X. Zhang, L. Chen, Z. Teng, D. Saloner, J. Lu and Q. LiuAmerican Journal of Neuroradiology July 2018, 39 (7) 1286-1292; DOI: https://doi.org/10.3174/ajnr.A5676
Patients with basilar artery stenosis (n=126; 66 symptomatic and 60 asymptomatic) underwent high-resolution MR imaging. The relationship between imaging findings (intraplaque hemorrhage, contrast enhancement, degree of stenosis, minimal lumen area, and plaque burden) and symptoms was analyzed. Intraplaque hemorrhage was identified in 22 patients (17.5%), including 21 (31.8%) symptomatic patients and 1 (1.7%) asymptomatic patient. Multivariate analysis showed that intraplaque hemorrhage was the strongest independent marker of symptomatic status. Contrast enhancement was also independently associated with symptomatic status. The authors conclude that intraplaque hemorrhage is present in both low- and high-grade stenotic basilar artery plaques and is independently associated with symptomatic stroke status. Intraplaque hemorrhage may identify high-risk plaque and provide new insight into the management of patients with stroke without significant stenosis.
Chien, C.
- Spine Imaging and Spine Image-Guided InterventionsOpen AccessMRI-Based Methods for Spinal Cord Atrophy Evaluation: A Comparison of Cervical Cord Cross-Sectional Area, Cervical Cord Volume, and Full Spinal Cord Volume in Patients with Aquaporin-4 Antibody Seropositive Neuromyelitis Optica Spectrum DisordersC. Chien, A.U. Brandt, F. Schmidt, J. Bellmann-Strobl, K. Ruprecht, F. Paul and M. ScheelAmerican Journal of Neuroradiology July 2018, 39 (7) 1362-1368; DOI: https://doi.org/10.3174/ajnr.A5665
Chow, D.
- EDITOR'S CHOICEAdult BrainOpen AccessDeep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in GliomasP. Chang, J. Grinband, B.D. Weinberg, M. Bardis, M. Khy, G. Cadena, M.-Y. Su, S. Cha, C.G. Filippi, D. Bota, P. Baldi, L.M. Poisson, R. Jain and D. ChowAmerican Journal of Neuroradiology July 2018, 39 (7) 1201-1207; DOI: https://doi.org/10.3174/ajnr.A5667
MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify IDH1 mutation status, 1p/19q codeletion, and MGMT promotor methylation status. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. The authors conclude that this shows the feasibility of a deep-learning CNN approach for the accurate classification of individual genetic mutations of both low- and high-grade gliomas and that the relevant MR imaging features acquired from an added dimensionality-reduction technique are concordant with existing literature, showing that neural networks are capable of learning key imaging components without prior feature selection or human directed training.
Cognard, C.
- NeurointerventionYou have accessRisk of Branch Occlusion and Ischemic Complications with the Pipeline Embolization Device in the Treatment of Posterior Circulation AneurysmsN. Adeeb, C.J. Griessenauer, A.A. Dmytriw, H. Shallwani, R. Gupta, P.M. Foreman, H. Shakir, J. Moore, N. Limbucci, S. Mangiafico, A. Kumar, C. Michelozzi, Y. Zhang, V.M. Pereira, C.C. Matouk, M.R. Harrigan, A.H. Siddiqui, E.I. Levy, L. Renieri, T.R. Marotta, C. Cognard, C.S. Ogilvy and A.J. ThomasAmerican Journal of Neuroradiology July 2018, 39 (7) 1303-1309; DOI: https://doi.org/10.3174/ajnr.A5696
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