Index by author
Yang, Guang
- EDITOR'S CHOICENeurovascular/Stroke ImagingYou have accessA Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke OutcomesYongkai Liu, Preya Shah, Yannan Yu, Jai Horsey, Jiahong Ouyang, Bin Jiang, Guang Yang, Jeremy J. Heit, Margy E. McCullough-Hicks, Stephen M. Hugdal, Max Wintermark, Patrik Michel, David S. Liebeskind, Maarten G. Lansberg, Gregory W. Albers and Greg ZaharchukAmerican Journal of Neuroradiology April 2024, 45 (4) 406-411; DOI: https://doi.org/10.3174/ajnr.A8140
The authors in this study used a deep learning-based predictive model (DLPD) that incorporated DWI and clinical data from the acute period to predict 90-day mRS outcomes and compared its predictions with those made by physicians. The results showed that the clinical and imaging fused deep learning model is noninferior to expert physicians in predicting specific mRS outcomes and unfavorable prognoses.
Yang, YanXing
- Brain Tumor ImagingYou have accessImproved Detection of Target Metabolites in Brain Tumors with Intermediate TE, High SNR, and High Bandwidth Spin-Echo Sequence at 5TWenbo Sun, Dan Xu, YanXing Yang, Linfei Wen, Hanjiang Yu, Yaowen Xing, Xiaopeng Song, Huan Li and Haibo XuAmerican Journal of Neuroradiology April 2024, 45 (4) 461-467; DOI: https://doi.org/10.3174/ajnr.A8150
Yokka, Akira
- NeurointerventionYou have accessEvaluation of Conditions for the Development of Cryogenic Spinal Cord Injury Using a Canine Model: An Experimental Study on the Safety of Cryoablation for Metastatic Spinal TumorsMotoya Kobayashi, Satoshi Kato, Satoru Demura, Noriaki Yokogawa, Akira Yokka, Yusuke Nakade, Ryohei Annen, Toshifumi Gabata and Hiroyuki TsuchiyaAmerican Journal of Neuroradiology April 2024, 45 (4) 424-431; DOI: https://doi.org/10.3174/ajnr.A8151
Yokogawa, Noriaki
- NeurointerventionYou have accessEvaluation of Conditions for the Development of Cryogenic Spinal Cord Injury Using a Canine Model: An Experimental Study on the Safety of Cryoablation for Metastatic Spinal TumorsMotoya Kobayashi, Satoshi Kato, Satoru Demura, Noriaki Yokogawa, Akira Yokka, Yusuke Nakade, Ryohei Annen, Toshifumi Gabata and Hiroyuki TsuchiyaAmerican Journal of Neuroradiology April 2024, 45 (4) 424-431; DOI: https://doi.org/10.3174/ajnr.A8151
Yu, Hanjiang
- Brain Tumor ImagingYou have accessImproved Detection of Target Metabolites in Brain Tumors with Intermediate TE, High SNR, and High Bandwidth Spin-Echo Sequence at 5TWenbo Sun, Dan Xu, YanXing Yang, Linfei Wen, Hanjiang Yu, Yaowen Xing, Xiaopeng Song, Huan Li and Haibo XuAmerican Journal of Neuroradiology April 2024, 45 (4) 461-467; DOI: https://doi.org/10.3174/ajnr.A8150
Yu, Yannan
- EDITOR'S CHOICENeurovascular/Stroke ImagingYou have accessA Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke OutcomesYongkai Liu, Preya Shah, Yannan Yu, Jai Horsey, Jiahong Ouyang, Bin Jiang, Guang Yang, Jeremy J. Heit, Margy E. McCullough-Hicks, Stephen M. Hugdal, Max Wintermark, Patrik Michel, David S. Liebeskind, Maarten G. Lansberg, Gregory W. Albers and Greg ZaharchukAmerican Journal of Neuroradiology April 2024, 45 (4) 406-411; DOI: https://doi.org/10.3174/ajnr.A8140
The authors in this study used a deep learning-based predictive model (DLPD) that incorporated DWI and clinical data from the acute period to predict 90-day mRS outcomes and compared its predictions with those made by physicians. The results showed that the clinical and imaging fused deep learning model is noninferior to expert physicians in predicting specific mRS outcomes and unfavorable prognoses.
Yu, ZeChen
- Neuroimaging Physics/Functional Neuroimaging/CT and MRI TechnologyYou have accessVisualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary StudyBingYang Bian, Lin Hou, YaTing Chai, YueLuan Jiang, XingChen Pan, Yang Sun, HongChao Wang, DongDong Qiu, ZeChen Yu, Hua Zhao, HuiMao Zhang, FanYang Meng and Lei ZhangAmerican Journal of Neuroradiology April 2024, 45 (4) 504-510; DOI: https://doi.org/10.3174/ajnr.A8156
Zaaraoui, Wafaa
- Neurodegenerative Disorder ImagingYou have accessNeurofilament Light Chain Levels Interact with Neurodegenerative Patterns and Motor Neuron Dysfunction in Amyotrophic Lateral SclerosisPenelope Tilsley, Antoine Moutiez, Alexandre Brodovitch, Mohamed Mounir El Mendili, Benoit Testud, Wafaa Zaaraoui, Annie Verschueren, Shahram Attarian, Maxime Guye, José Boucraut, Aude-Marie Grapperon and Jan-Patrick StellmannAmerican Journal of Neuroradiology April 2024, 45 (4) 494-503; DOI: https://doi.org/10.3174/ajnr.A8154
Zaharchuk, Greg
- EDITOR'S CHOICENeurovascular/Stroke ImagingYou have accessA Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke OutcomesYongkai Liu, Preya Shah, Yannan Yu, Jai Horsey, Jiahong Ouyang, Bin Jiang, Guang Yang, Jeremy J. Heit, Margy E. McCullough-Hicks, Stephen M. Hugdal, Max Wintermark, Patrik Michel, David S. Liebeskind, Maarten G. Lansberg, Gregory W. Albers and Greg ZaharchukAmerican Journal of Neuroradiology April 2024, 45 (4) 406-411; DOI: https://doi.org/10.3174/ajnr.A8140
The authors in this study used a deep learning-based predictive model (DLPD) that incorporated DWI and clinical data from the acute period to predict 90-day mRS outcomes and compared its predictions with those made by physicians. The results showed that the clinical and imaging fused deep learning model is noninferior to expert physicians in predicting specific mRS outcomes and unfavorable prognoses.
Zanatta, Paolo
- FELLOWS' JOURNAL CLUBNeurovascular/Stroke ImagingYou have accessHemorrhage Volume Drives Early Brain Injury and Outcome in Poor-Grade Aneurysmal SAHPietro Panni, Franco Simionato, Roberta Cao, Alessandro Pedicelli, Enrico Marchese, Anselmo Caricato, Andrea Alexandre, Alberto Feletti, Mattia Testa, Paolo Zanatta, Nicola Gitti, Simone Piva, Dikran Mardighian, Vittorio Semeraro, Giordano Nardin, Emilio Lozupone, Giafranco Paiano, Edoardo Picetti, Vito Montanaro, Massimo Petranca, Carlo Bortolotti, Antonino Scibilia, Luigi Cirillo, Raffaele Aspide, Andrea Luigi Lanterna, Alessandro Ambrosi, Pietro Mortini, Maria Luisa Azzolini, Maria Rosa Calvi, Andrea Falini and on behalf of the POGASH InvestigatorsAmerican Journal of Neuroradiology April 2024, 45 (4) 393-399; DOI: https://doi.org/10.3174/ajnr.A8135
Early brain injury (radiologically defined by global cerebral edema) is a major determinant of clinical outcome in poor-grade aneurysmal SAH. In this retrospective study of 400 patients with poor-grade aneurysmal SAH, it was shown that intracerebral hemorrhage volume independently predicted global cerebral edema and long-term outcome, intraventricular hemorrhage volume predicted mortality and long-term outcome, and SAH volume predicted long-term clinical outcome.
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