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153 results on '"Choi, Yangsean"'

19. Magnetic resonance imaging-based radiomics for the prediction of progression-free survival in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis

Author

Choi, Yangsean

Subjects
Neoplasms, Medicine and Health Sciences, Medical Specialties, Diseases, Radiology, Translational Medical Research
Abstract

This project is a systematic review and meta-analysis investigating the prognostic significance of MRI-based radiomics in patients with nasopharyngeal carcinoma.

Published
2022
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27. Neuroimaging findings of brain MRI and CT in patients with COVID-19: A systematic review and meta-analysis

Author

Choi, Yangsean and Lee, Min Kyoung

Subjects
Adult, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), ICH, intracranial hemorrhage, Encephalopathy, Neuroimaging, SARS-CoV-2, severe acute respiratory syndrome coronavirus 2, Article, I2, Higgin’s inconsistency index, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Prevalence, medicine, Severe acute respiratory syndrome coronavirus 2, Humans, Radiology, Nuclear Medicine and imaging, cardiovascular diseases, COVID-19, coronavirus disease 2019, Aged, Brain Diseases, medicine.diagnostic_test, Cerebral infarction, business.industry, ADEM, acute disseminated encephalomyelitis, Brain, COVID-19, Magnetic resonance imaging, General Medicine, medicine.disease, Magnetic Resonance Imaging, Confidence interval, CI, confidence interval, PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-analyses, PRES, posterior reversible encephalopathy syndrome, Radiology Nuclear Medicine and imaging, 030220 oncology & carcinogenesis, Meta-analysis, business, Tomography, X-Ray Computed, Encephalitis
Abstract

Highlights • The prevalence of acute/subacute infarcts was the most common in COVID-19 patients with neurologic symptoms. • Cerebral microhemorrhages (6.9 %) and spontaneous ICH (5.4 %) were similarly prevalent. • The prevalence of encephalitis/encephalopathy (3.3 %) was less frequent., Purpose To comprehensively evaluate the incidences of abnormal neuroimaging findings in patients with COVID-19 via a systematic review and meta-analysis. Method PubMed-MEDLINE and EMBASE were searched for original articles reporting imaging findings of the brain in adult patients with COVID-19 between January 1, 2020 and October 9, 2020. Abnormal neuroimaging findings were categorized as (1) cerebral microhemorrhages, (2) acute spontaneous intracranial hemorrhage (ICH), (3) acute to subacute infarcts, and (4) encephalitis or encephalopathy. Pooled incidences of neuroimaging findings were assessed using random-effects modeling. Between-study heterogeneity was explored by using the χ2 statistic for pooled incidences and the inconsistency index I2. The quality of the studies was evaluated using the Risk of Bias Assessment Tool for Nonrandomized Studies. Subgroup meta-regression analysis was performed to identify potential sources of heterogeneity. Results Twenty-one eligible papers, including 2125 patients, were identified. The pooled incidences of cerebral microhemorrhages, acute spontaneous ICH, acute/subacute infarcts, and encephalitis/encephalopathy were 6.9 % (95 % confidence interval [CI], 4.9 %–8.9 %), 5.4 % (95 % CI, 3.1 %–7.6 %), 24.0 % (95 % CI, 16.1 %–31.8 %), and 3.3 % (95 % CI, 1.9 %–4.7 %), respectively. Substantial heterogeneities were noted for all neuroimaging findings (I2 = 87 %–97 %). Significant publication biases were present in the pooled incidences. In the subgroup meta-regression analysis, patients with mean or median ages over 65 years showed a significantly lower incidence of encephalitis/encephalopathy (P < 0.001). Furthermore, studies reported that patients in ICU had significantly higher incidences of cerebral microhemorrhages (P

Published
2020

41. Diagnostic accuracy and efficiency of combined acquisition of low-dose time-resolved and single-phase high-resolution contrast-enhanced magnetic resonance angiography in a single session for pre-angiographic evaluation of spinal vascular disease

Author

Shin, Jae Ho, primary, Choi, Yangsean, additional, Park, Borim, additional, Shin, Na-Young, additional, Jang, Jinhee, additional, Choi, Hyun Seok, additional, Jung, So Lyung, additional, Ahn, Kookjin, additional, and Kim, Bum-soo, additional

Published
2019
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42. Comparison of efficacy and complications between radiofrequency ablation and repeat surgery in the treatment of locally recurrent thyroid cancers: a single-center propensity score matching study

Author

Choi, Yangsean, primary, Jung, So Lyung, additional, Bae, Ja-Sung, additional, Lee, So-Hee, additional, Jung, Chan-Kwon, additional, Jang, Jinhee, additional, Shin, Na-Young, additional, Choi, Hyun Seok, additional, Ahn, Kook-Jin, additional, and Kim, Bum-Soo, additional

Published
2019
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49. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.

Author

Choi, Yangsean, Nam, Yoonho, Lee, Youn Soo, Kim, Jiwoong, Ahn, Kook-Jin, Jang, Jinhee, Shin, Na-Young, Kim, Bum-Soo, and Jeon, Sin-Soo

Subjects
*FORECASTING, *GLIOBLASTOMA multiforme, *RECEIVER operating characteristic curves, *OLIGODENDROGLIOMAS, *COMPUTERS in medicine, *RESEARCH, *GENETIC mutation, *RESEARCH methodology, *GLIOMAS, *MAGNETIC resonance imaging, *RETROSPECTIVE studies, *PHARMACOKINETICS, *EVALUATION research, *MEDICAL cooperation, *BRAIN tumors, *DIAGNOSTIC imaging, *COMPARATIVE studies, *OXIDOREDUCTASES, *LONGITUDINAL method, *ALGORITHMS
Abstract

Purpose: This study aimed to determine whether MR-based radiomics of glioblastoma can predict the isocitrate dehydrogenase-1 (IDH1) mutation status and compare predictive performances between manual and fully automatic deep-learning segmentations.Method: Forty-five glioblastoma patients with pretreatment T2-weighted MRIs were retrospectively evaluated. Manual segmentations of glioblastoma and peri-tumoral edema were trained via a deep neural network (V-Net). An independent external cohort of 137 glioblastoma patients from the Cancer Imaging Archive was also included (test set 1, n = 46; test set 2, n = 91). Test set 1-without known IDH1 status-was used to calculate dice similarity coefficients (DSC) between the two segmentation methods (manual & V-Net). From test set 2, all-relevant radiomic features were selected via a random forest-based wrapper algorithm for IDH1 prediction. Receiver operating characteristics (ROC) curves with areas under the curve (AUC) were plotted as performance metrics for both methods.Results: Among 136 patients (45 and 91 patients from our institution and test set 2, respectively), 17 patients (11.2 %) had IDH1 mutations. The mean DSC of test set 1 was 0.78 ± 0.14 (range, 0.34-0.94). A subset of 9 all-relevant features (8.4 %, 9/107) was selected. V-Net segmentation of the test set 2 yielded similar performance in predicting IDH1 mutation as compared to manual segmentation (V-Net AUC = 0.86 vs. manual AUC = 0.90). The optimal cut-point threshold of AUC yielded 86.8 % accuracy for manual segmentation and 75.8 % for V-Net segmentation.Conclusions: V-Net showed robust segmentation capability of glioblastoma on T2-weighted MRI. All-relevant radiomics features from both segmentation methods yielded a similar performance in IDH1 prediction. [ABSTRACT FROM AUTHOR]

Published
2020
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