Comprehensive review of Wernicke encephalopathy: pathophysiology, clinical symptoms and imaging findings Y Ota, AA Capizzano, T Moritani, S Naganawa, R Kurokawa, A Srinivasan Japanese journal of radiology 38, 809-820, 2020 | 110 | 2020 |
Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis S Naganawa, K Enooku, R Tateishi, H Akai, K Yasaka, J Shibahara, ... European radiology 28, 3050-3058, 2018 | 47 | 2018 |
A review of clinical and imaging findings in tumefactive demyelination M Nakayama, S Naganawa, M Ouyang, KA Jones, J Kim, AA Capizzano, ... American Journal of Roentgenology 217 (1), 186-197, 2021 | 28 | 2021 |
Machine learning-based multiparametric magnetic resonance imaging radiomics for prediction of H3K27M mutation in midline gliomas SG Kandemirli, B Kocak, S Naganawa, K Ozturk, SSF Yip, S Chopra, ... World neurosurgery 151, e78-e85, 2021 | 24 | 2021 |
Assessment of MR imaging and CT in differentiating hereditary and nonhereditary paragangliomas Y Ota, S Naganawa, R Kurokawa, JR Bapuraj, A Capizzano, J Kim, ... American Journal of Neuroradiology 42 (7), 1320-1326, 2021 | 12 | 2021 |
Performance and robustness of machine learning-based radiomic COVID-19 severity prediction SSF Yip, Z Klanecek, S Naganawa, J Kim, A Studen, L Rivetti, R Jeraj medRxiv, 2020.09. 07.20189977, 2020 | 12 | 2020 |
Role of delayed-time-point imaging during abdominal and pelvic cancer screening using FDG-PET/CT in the general population S Naganawa, T Yoshikawa, K Yasaka, E Maeda, N Hayashi, O Abe Medicine 96 (46), e8832, 2017 | 11 | 2017 |
Usefulness of T2 star‐weighted imaging in ovarian cysts and tumors N Takahashi, O Yoshino, E Maeda, S Naganawa, M Harada, K Koga, ... Journal of Obstetrics and Gynaecology Research 42 (10), 1336-1342, 2016 | 11 | 2016 |
Texture analysis of T2-weighted MRI predicts SDH mutation in paraganglioma S Naganawa, J Kim, SSF Yip, Y Ota, A Srinivasan, T Moritani Neuroradiology 63, 547-554, 2021 | 9 | 2021 |
Deep Learning for differentiation of breast masses detected by screening ultrasound elastography T Fukuda, H Tsunoda, K Yagishita, S Naganawa, K Hayashi, Y Kurihara Ultrasound in Medicine & Biology 49 (4), 989-995, 2023 | 3 | 2023 |
Vaginal delivery-related changes in the pelvic organ position and vaginal cross-sectional area in the general population S Naganawa, E Maeda, A Hagiwara, S Amemiya, W Gonoi, S Hanaoka, ... Clinical Imaging 50, 86-90, 2018 | 2 | 2018 |
Dural and Leptomeningeal Diseases: Anatomy, Causes, and Neuroimaging Findings R Kurokawa, M Kurokawa, S Isshiki, T Harada, M Nakaya, A Baba, ... RadioGraphics 43 (9), e230039, 2023 | 1 | 2023 |
Primary and metastatic spine tumors PW Hitchon, S Naganawa, J Kim, RW Woodroffe, LC Helland, MC Smith, ... Diffusion-Weighted MR Imaging of the Brain, Head and Neck, and Spine, 803-838, 2021 | 1 | 2021 |
Brain neoplasm JR Bapuraj, T Moritani, S Naganawa, A Hiwatashi, C Becker, Y Umemura, ... Diffusion-Weighted MR Imaging of the Brain, Head and Neck, and Spine, 521-625, 2021 | 1 | 2021 |
Performance and robustness of machine learning-based radiomic COVID-19 severity prediction Z Klanecek, S Naganawa, J Kim, L Rivetti, A Studen, S Yip, R Jeraj APS March Meeting Abstracts 2021, H71. 115, 2021 | 1 | 2021 |
Succinate detection in glomus jugulare paraganglioma on MRS as a marker for SDHB mutation S Naganawa, AA Capizzano, Y Ota, J Kim, A Srinivasan, T Moritani Otolaryngology Case Reports 16, 100207, 2020 | 1 | 2020 |
Coronavirus Disease 2019‐Associated Brachial Plexus Neuritis B Otemuyiwa, S Naganawa, J Kim, A Capizzano, T Moritani Neurographics 13 (4), 280-283, 2023 | | 2023 |
Temporal DCE Profile of Brain Metastasis with A Comparison of Pseudoprogression Cases S Turk, R Kurokawa, S Naganawa, J Wallace, T Ma, T Johnson, T Moritani, ... medRxiv, 2022.12. 19.22283618, 2022 | | 2022 |
Neuroradiology Manifestations of Li-Fraumeni Syndrome: Epidemiology, Genetics, Imaging Findings, and Management S Naganawa, T Donohue, A Capizzano, Y Ota, J Kim, A Srinivasan, ... Neurographics 10 (4), 228-235, 2020 | | 2020 |
Performance and Robustness of Machine Learning-based Radiomic COVID-19 Severity Prediction (preprint) SSF Yip, Z Klanecek, S Naganawa, J Kim, A Studen, L Rivetti, R Jeraj | | 2020 |