American Journal of Neuroradiology: 40 (3)

Index by author

March 01, 2019; Volume 40,Issue 3

Sakai, O.
1. Head & Neck
  
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  Etiology-Specific Mineralization Patterns in Patients with Labyrinthitis Ossificans
  K. Buch, B. Baylosis, A. Fujita, M.M. Qureshi, K. Takumi, P.C. Weber and O. Sakai
  
  American Journal of Neuroradiology March 2019, 40 (3) 551-557; DOI: https://doi.org/10.3174/ajnr.A5985
2. Head & Neck
  
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  CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [¹⁸F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV-Positive Patients with Head and Neck Squamous Cell Carcinoma
  H. Kuno, N. Garg, M.M. Qureshi, M.N. Chapman, B. Li, S.K. Meibom, M.T. Truong, K. Takumi and O. Sakai
  
  American Journal of Neuroradiology March 2019, 40 (3) 543-550; DOI: https://doi.org/10.3174/ajnr.A5974
Sakur, S.
1. Interventional
  
  Open Access
  Local Hemodynamic Conditions Associated with Focal Changes in the Intracranial Aneurysm Wall
  J.R. Cebral, F. Detmer, B.J. Chung, J. Choque-Velasquez, B. Rezai, H. Lehto, R. Tulamo, J. Hernesniemi, M. Niemela, A. Yu, R. Williamson, K. Aziz, S. Sakur, S. Amin-Hanjani, F. Charbel, Y. Tobe, A. Robertson and J. Frösen
  
  American Journal of Neuroradiology March 2019, 40 (3) 510-516; DOI: https://doi.org/10.3174/ajnr.A5970
Sanai, N.
1. FELLOWS' JOURNAL CLUBAdult Brain
  
  Open Access
  Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning
  L.S. Hu, H. Yoon, J.M. Eschbacher, L.C. Baxter, A.C. Dueck, A. Nespodzany, K.A. Smith, P. Nakaji, Y. Xu, L. Wang, J.P. Karis, A.J. Hawkins-Daarud, K.W. Singleton, P.R. Jackson, B.J. Anderies, B.R. Bendok, R.S. Zimmerman, C. Quarles, A.B. Porter-Umphrey, M.M. Mrugala, A. Sharma, J.M. Hoxworth, M.G. Sattur, N. Sanai, P.E. Koulemberis, C. Krishna, J.R. Mitchell, T. Wu, N.L. Tran, K.R. Swanson and J. Li
  
  American Journal of Neuroradiology March 2019, 40 (3) 418-425; DOI: https://doi.org/10.3174/ajnr.A5981
  
  The authors evaluated tumor cell density using a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. They collected 82 image-recorded biopsy samples, from 18 patients with primary GBM. With multivariate modeling, transfer learning improved performance (r = 0.88) compared with one-model-fits-all (r = 0.39). They conclude that transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
Sanelli, P.C.
1. Adult Brain
  
  Open Access
  Dynamic Contrast-Enhanced MRI Reveals Unique Blood-Brain Barrier Permeability Characteristics in the Hippocampus in the Normal Brain
  J. Ivanidze, M. Mackay, A. Hoang, J.M. Chi, K. Cheng, C. Aranow, B. Volpe, B. Diamond and P.C. Sanelli
  
  American Journal of Neuroradiology March 2019, 40 (3) 408-411; DOI: https://doi.org/10.3174/ajnr.A5962
2. Adult Brain
  
  Open Access
  Alterations in Blood-Brain Barrier Permeability in Patients with Systemic Lupus Erythematosus
  J.M. Chi, M. Mackay, A. Hoang, K. Cheng, C. Aranow, J. Ivanidze, B. Volpe, B. Diamond and P.C. Sanelli
  
  American Journal of Neuroradiology March 2019, 40 (3) 470-477; DOI: https://doi.org/10.3174/ajnr.A5990
Sati, P.
1. Adult Brain
  
  Open Access
  Quantitative Susceptibility Mapping to Assess Cerebral Vascular Compliance
  C. Birkl, C. Langkammer, P. Sati, C. Enzinger, F. Fazekas and S. Ropele
  
  American Journal of Neuroradiology March 2019, 40 (3) 460-463; DOI: https://doi.org/10.3174/ajnr.A5933
Sattur, M.G.
1. FELLOWS' JOURNAL CLUBAdult Brain
  
  Open Access
  Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning
  L.S. Hu, H. Yoon, J.M. Eschbacher, L.C. Baxter, A.C. Dueck, A. Nespodzany, K.A. Smith, P. Nakaji, Y. Xu, L. Wang, J.P. Karis, A.J. Hawkins-Daarud, K.W. Singleton, P.R. Jackson, B.J. Anderies, B.R. Bendok, R.S. Zimmerman, C. Quarles, A.B. Porter-Umphrey, M.M. Mrugala, A. Sharma, J.M. Hoxworth, M.G. Sattur, N. Sanai, P.E. Koulemberis, C. Krishna, J.R. Mitchell, T. Wu, N.L. Tran, K.R. Swanson and J. Li
  
  American Journal of Neuroradiology March 2019, 40 (3) 418-425; DOI: https://doi.org/10.3174/ajnr.A5981
  
  The authors evaluated tumor cell density using a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. They collected 82 image-recorded biopsy samples, from 18 patients with primary GBM. With multivariate modeling, transfer learning improved performance (r = 0.88) compared with one-model-fits-all (r = 0.39). They conclude that transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
Schiff, D.
1. FELLOWS' JOURNAL CLUBAdult Brain
  
  You have access
  Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas
  P.P. Batchala, T.J.E. Muttikkal, J.H. Donahue, J.T. Patrie, D. Schiff, C.E. Fadul, E.K. Mrachek, M.-B. Lopes, R. Jain and S.H. Patel
  
  American Journal of Neuroradiology March 2019, 40 (3) 426-432; DOI: https://doi.org/10.3174/ajnr.A5957
  
  One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor or cyst texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lowergrade gliomas; and 2)a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation ofthe classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers.
Schwamm, L.H.
1. Memorial
  
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  Robert H. Ackerman, MD, MPH (1935–2018)
  M.H. Lev, J.M. Romero, L.H. Schwamm, M.E. Cudkowicz and J.A. Brink
  
  American Journal of Neuroradiology March 2019, 40 (3) E12-E13; DOI: https://doi.org/10.3174/ajnr.A5989
Schwartz, E.
1. Pediatrics
  
  Open Access
  Underdevelopment of the Human Hippocampus in Callosal Agenesis: An In Vivo Fetal MRI Study
  V. Knezović, G. Kasprian, A. Štajduhar, E. Schwartz, M. Weber, G.M. Gruber, P.C. Brugger, D. Prayer and M. Vukšić
  
  American Journal of Neuroradiology March 2019, 40 (3) 576-581; DOI: https://doi.org/10.3174/ajnr.A5986
Severac, F.
1. Interventional
  
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  Predictors and Clinical Impact of Delayed Stent Thrombosis after Thrombectomy for Acute Stroke with Tandem Lesions
  R. Pop, I. Zinchenko, V. Quenardelle, D. Mihoc, M. Manisor, J.S. Richter, F. Severac, M. Simu, S. Chibbaro, O. Rouyer, V. Wolff and R. Beaujeux
  
  American Journal of Neuroradiology March 2019, 40 (3) 533-539; DOI: https://doi.org/10.3174/ajnr.A5976

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American Journal of Neuroradiology

Index by author

Sakai, O.

Sakur, S.

Sanai, N.

Sanelli, P.C.

Sati, P.

Sattur, M.G.

Schiff, D.

Schwamm, L.H.

Schwartz, E.

Severac, F.

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