Index by author
March 01, 2019; Volume 40,Issue 3
Soni, N.
- LetterYou have accessEngorged Medullary Veins in Neurosarcoidosis: A Reflection of Underlying Phlebitis?G. Bathla, N. Soni, T. Moritani and A.A. CapizzanoAmerican Journal of Neuroradiology March 2019, 40 (3) E14-E15; DOI: https://doi.org/10.3174/ajnr.A5951
Spelle, L.
- NeurointerventionOpen AccessAneurysm Characteristics, Study Population, and Endovascular Techniques for the Treatment of Intracranial Aneurysms in a Large, Prospective, Multicenter Cohort: Results of the Analysis of Recanalization after Endovascular Treatment of Intracranial Aneurysm StudyM. Gawlitza, S. Soize, C. Barbe, A. le Clainche, P. White, L. Spelle and L. Pierot ARETA Study GroupAmerican Journal of Neuroradiology March 2019, 40 (3) 517-523; DOI: https://doi.org/10.3174/ajnr.A5991
Sun, J.
- Adult BrainOpen AccessComplementary Roles of Dynamic Contrast-Enhanced MR Imaging and Postcontrast Vessel Wall Imaging in Detecting High-Risk Intracranial AneurysmsH. Qi, X. Liu, P. Liu, W. Yuan, A. Liu, Y. Jiang, Y. Li, J. Sun and H. ChenAmerican Journal of Neuroradiology March 2019, 40 (3) 490-496; DOI: https://doi.org/10.3174/ajnr.A5983
Swanson, K.R.
- FELLOWS' JOURNAL CLUBAdult BrainOpen AccessAccurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer LearningL.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. LiAmerican 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.
In this issue
American Journal of Neuroradiology
Vol. 40, Issue 3
1 Mar 2019
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