[1] Joo, B., Ahn, S. S.*, An, C., Han, K., Choi, D., Kim, H., Park, J. E., Kim, H. S., & Lee, S. K. (2023). Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis. Journal of neuroradiology = Journal de neuroradiologie, 50(4), 388–395.

[2] Park, Y. W., Vollmuth, P., Foltyn-Dumitru, M., Sahm, F., Ahn, S. S.*, Chang, J. H., & Kim, S. H. (2023). The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 2-Summary of Imaging Findings on Pediatric-Type Diffuse High-Grade Gliomas, Pediatric-Type Diffuse Low-Grade Gliomas, and Circumscribed Astrocytic Gliomas. Journal of magnetic resonance imaging : JMRI, 58(3), 690–708. 

[3] Park, Y. W., Vollmuth, P., Foltyn-Dumitru, M., Sahm, F., Ahn, S. S.*, Chang, J. H., & Kim, S. H. (2023). The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 1-Key Points of the Fifth Edition and Summary of Imaging Findings on Adult-Type Diffuse Gliomas. Journal of magnetic resonance imaging : JMRI, 58(3), 677–689.

[4] Jun, Y., Park, Y. W., Shin, H., Shin, Y., Lee, J. R., Han, K., Ahn, S. S.*, Lim, S. M., Hwang, D., & Lee, S. K. (2023). Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning. European radiology, 33(9), 6124–6133.

[5] Park, Y. W., Han, K., Kim, S., Kwon, H., Ahn, S. S.*, Moon, J. H., Kim, E. H., Kim, J., Kang, S. G., Chang, J. H., Kim, S. H., & Lee, S. K. (2023). Revisiting prognostic factors in glioma with leptomeningeal metastases: a comprehensive analysis of clinical and molecular factors and treatment modalities. Journal of neuro-oncology, 162(1), 59–68. 

[1] Park, Y. W., Eom, J., Kim, D., Ahn, S. S. *, Kim, E. H., Kang, S. G., ... & Lee, S. K. (2022). A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from high-grade gliomas. European Radiology, 1-10.

[2] Park, Y. W., Kim, S., Park, C. J., Ahn, S. S. *, Han, K., Kang, S. G., ... & Lee, S. K. (2022). Adding radiomics to the 2021 WHO updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status. European Radiology, 1-10.

[3] Bang, M., Park, Y. W., Eom, J., Ahn, S. S., Kim, J., Lee, S. K., & Lee, S. H.* (2022). An interpretable radiomics model for the diagnosis of panic disorder with or without agoraphobia using magnetic resonance imaging. Journal of Affective Disorders, 305, 47-54.

[4] Park, Y. W., Shin, S. J., Eom, J., Lee, H., You, S. C.*, Ahn, S. S.*, ... & Lee, S. K. (2022). Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation. Scientific reports, 12(1), 1-9.

[5] Shin, I., Park, Y. W. *, Ahn, S. S., Kim, J., Chang, J. H., Kim, S. H., & Lee, S. K. (2022). Clinical factors and conventional MRI may independently predict progression-free survival and overall survival in adult pilocytic astrocytomas. Neuroradiology, 1-9.

[6] Shin, I., Park, Y. W.*, Ahn, S. S., Kang, S. G., Chang, J. H., Kim, S. H., & Lee, S. K. (2022). Clinical and diffusion parameters may noninvasively predict tert promoter mutation status in grade ii meningiomas. Journal of Neuroradiology, 49(1), 59-65.

[7] Park, C. J., Park, Y. W., Ahn, S. S., Kim, D., Kim, E. H., Kang, S. G., ... & Lee, S. K*. (2022). Quality of radiomics research on brain metastasis: a roadmap to promote clinical translation. Korean journal of radiology, 23(1), 77.

[1] Sohn BS, An C, Kim D, Ahn SS*, Han K, Kim SH, Kang SG, Chang JH, Lee SK. Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant. J Neurooncol. 2021 Dec;155(3):267-276

[2] Ahn SS, Cha S*. Pre- and Post-Treatment Imaging of Primary Central Nervous System Tumors in the Molecular and Genetic Era. Korean J Radiol. 2021 Nov;22(11):1858-1874.

[3] Park YW, Ahn SS*, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging. Neuroradiology. 2021 Nov;63(11):1811-1822.

[4] Choi, E. Y., Park, Y. W.*, Lee, M., Kim, M., Lee, C. S., Ahn, S. S., ... & Lee, S. K. (2021). Magnetic resonance imaging-visible perivascular spaces in the basal ganglia are associated with the diabetic retinopathy stage and cognitive decline in patients with type 2 diabetes. Frontiers in aging neuroscience, 755.

[5] Park YW, Kim D, Eom J, Ahn SS*, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. A diagnostic tree for differentiation of adult pilocytic astrocytomas from high-grade gliomas. Eur J Radiol. 2021 Oct;143:109946.

[6] Park YW, Jun Y, Lee Y, Han K, An C, Ahn SS*, Hwang D, Lee SK. Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging. Eur Radiol. 2021 Sep;31(9):6686-6695.

[7] Sohn, B., Choi, Y. S., Ahn, S. S., Kim, H., Han, K., Lee, S. K., & Kim, J.* (2021). Machine learning based radiomic HPV phenotyping of oropharyngeal SCC: A feasibility study using MRI. The Laryngoscope, 131(3), E851-E856.

[8] Kim, H. K., Hong, J. W., Moon, J. H., Ahn, S. S., Kim, E. H., Lee, S. K., ... & Park, Y.W.*, Ku, C. R. (2021). Efficacy and Cerebrospinal Fluid Rhinorrhea after Cabergoline Treatment in Patients with Bioactive Macroprolactinoma. Cancers, 13(21), 5374.

[9] An C, Park YW, Ahn SS*, Han K, Kim H, Lee SK. Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results. PLoS One. 2021 Aug 12;16(8):e0256152.

[10] Ahn SS, An C, Park YW, Han K, Chang JH, Kim SH, Lee SK, Cha S*. Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma. J Neurooncol. 2021 Aug;154(1):83-92.

[11] Park YW, Park JE, Ahn SS*, Kim EH, Kang SG, Chang JH, Kim SH, Choi SH, Kim HS, Lee SK. Magnetic Resonance Imaging Parameters for Noninvasive Prediction of Epidermal Growth Factor Receptor Amplification in Isocitrate Dehydrogenase-Wild-Type Lower-Grade Gliomas: A Multicenter Study. Neurosurgery. 2021 Jul 15;89(2):257-265.

[12] Shin I, Kim H, Ahn SS*, Sohn B, Bae S, Park JE, Kim HS, Lee SK. Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images. AJNR Am J Neuroradiol. 2021 May;42(5):838-844.

[13] Park CJ, Han K, Kim H, Ahn SS*, Choi D, Park YW, Chang JH, Kim SH, Cha S, Lee SK. MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas. AJNR Am J Neuroradiol. 2021 Mar;42(3):448-456.

[14] Park YW, An C, Lee J, Han K, Choi D, Ahn SS*, Kim H, Ahn SJ, Chang JH, Kim SH, Lee SK. Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer. Neuroradiology. 2021 Mar;63(3):343-352.

[15] Bang, Minji, et al. "An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum." Translational psychiatry 11.1 (2021): 1-8.

[16] Park YW, Ahn SS*, Kim EH, Kang SG, Chang JH, Kim SH, Zhou J, Lee SK. Differentiation of recurrent diffuse glioma from treatment-induced change using amide proton transfer imaging: incremental value to diffusion and perfusion parameters. Neuroradiology. 2021 Mar;63(3):363-372

[17] Park, Y. W., Eom, J., Kim, S., Kim, H., Ahn, S. S., Ku, C. R., ... & Kim, E.H.*,... Lee, S. K. (2021). Radiomics with ensemble machine learning predicts dopamine agonist response in patients with prolactinoma. The Journal of Clinical Endocrinology & Metabolism, 106(8), e3069-e3077.

[18] Won, S. Y., Park, Y. W.*, Ahn, S. S., Moon, J. H., Kim, E. H., Kang, S. G., ... & Lee, S. K. (2021). Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications. European Journal of Radiology, 138, 109673.

[19] Park YW, Choi D, Park JE, Ahn SS*, Kim H, Chang JH, Kim SH, Kim HS, Lee SK. Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation. Sci Rep. 2021 Feb 3;11(1):2913.

[20] B. Sohn, K.-Y. Park, J. Choi, J.H. Koo, K. Han, B. Joo, S.Y. Won, J. Cha, H.S. Choi and S.-K. Lee. Deep Learning–Based Software Improves Clinicians' Detection Sensitivity of Aneurysms on Brain TOF-MRA. American Journal of Neuroradiology October 2021, 42 (10) 1769-1775;

[1] Won, S. Y., Park, Y. W., Park, M.*, Ahn, S. S., Kim, J., & Lee, S. K. (2020). Quality reporting of radiomics analysis in mild cognitive impairment and Alzheimer's disease: a roadmap for moving forward. Korean journal of radiology, 21(12), 1345.

[2] Bae S, Ahn SS*, Kim BM, Kim DJ, Kim YD, Nam HS, Heo JH, Lee SK. Hyperattenuating lesions after mechanical thrombectomy in acute ischaemic stroke: factors predicting symptomatic haemorrhage and clinical outcomes. Clin Radiol. 2021 Jan;76(1):80.e15-80.e23.

[3] Park, Y. W., Choi, D., Park, M.*, Ahn, S. J., Ahn, S. S., Suh, S. H., ... & Alzheimer’s Disease Neuroimaging Initiative. (2021). Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging. Journal of Alzheimer's Disease, 79(2), 483-491.

[4] Park YW, Ahn SS*, Park CJ, Han K, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas. Eur Radiol. 2020 Dec;30(12):6475-6484.

[5] Sim, Y., Lee, S. E., Kim, E. K., & Kim, S.* (2020). A radiomics approach for the classification of fibroepithelial lesions on breast ultrasonography. Ultrasound in Medicine & Biology, 46(5), 1133-1141.

[6] Joo B, Ahn SS*, Yoon PH, Bae S, Sohn B, Lee YE, Bae JH, Park MS, Choi HS, Lee SK. A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance. Eur Radiol. 2020 Nov;30(11):5785-5793.

[7] Sim, Y., Chung, M. J., Kotter, E., Yune, S., Kim, M., Do, S., ... & Choi, B. W.* (2020). Deep convolutional neural network–based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology, 294(1), 199-209.

[8] Park, Y. W., Kang, Y., Ahn, S. S., Ku, C. R., Kim, E. H.*, Kim, S. H., ... & Lee, S. K. (2020). Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas. Pituitary, 23(6), 691-700.

[9] Park YW, Kim S, Ahn SS*, Han K, Kang SG, Chang JH, Kim SH, Lee SK, Park SH. Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade. Eur Radiol. 2020 Aug;30(8):4615-4622.

[10] Bae S, Ahn SS*, Kim BM, Kim DJ, Kim YD, Nam HS, Heo JH, Lee SK. Hyperattenuating lesions after mechanical thrombectomy in acute ischaemic stroke: factors predicting symptomatic haemorrhage and clinical outcomes. Clin Radiol. 2020 Sep 17:S0009-9260(20)30373-1

[11] Bae S, An C, Ahn SS*, Kim H, Han K, Kim SW, Park JE, Kim HS, Lee SK. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Sci Rep. 2020 Jul 21;10(1):12110

[12] Park CJ, Han K, Shin H, Ahn SS*, Choi YS, Park YW, Chang JH, Kim SH, Jain R, Lee SK. MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas. Eur Radiol. 2020 Jun;30(6):3035-3045

[13] Choi YS, Ahn SS*, Chang JH, Kang SG, Kim EH, Kim SH, Jain R, Lee SK. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction. Eur Radiol. 2020 Jul;30(7):3834-3842.

[14] Park, Y. W., Choi, Y. S., Kim, S. E., Choi, D., Han, K., Kim, H., ... & Lee, H. W.* (2020). Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls. Scientific reports, 10(1), 1-8.

[15] Park, Y. W., Choi, D., Lee, J., Ahn, S. S., Lee, S. K., Lee, S. H., & Bang, M.* (2020). Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics. Schizophrenia Research, 223, 337-344.

[1] Park YW, Oh J, You SC, Han K, Ahn SS*, Choi YS, Chang JH, Kim SH, Lee SK. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol. 2019 Aug;29(8):4068-4076.

[2] Park, Y. W., Choi, Y. S.*, Ahn, S. S., Chang, J. H., Kim, S. H., & Lee, S. K. (2019). Radiomics MRI phenotyping with machine learning to predict the grade of lower-grade gliomas: a study focused on nonenhancing tumors. Korean journal of radiology, 20(9), 1381-1389.

[3] Park, Y. W., Shin, N. Y.*, Chung, S. J., Kim, J., Lim, S. M., Lee, P. H., ... & Ahn, K. J. (2019). Magnetic Resonance Imaging–Visible Perivascular Spaces in Basal Ganglia Predict Cognitive Decline in Parkinson's Disease. Movement Disorders, 34(11), 1672-1679.

[4] Lee M, Han K, Ahn SS*, Bae S, Choi YS, Hong JB, Chang JH, Kim SH, Lee SK. The added prognostic value of radiological phenotype combined with clinical features and molecular subtype in anaplastic gliomas. J Neurooncol. 2019 Mar;142(1):129-138.

[5] Joo B, Han K, Ahn SS*, Choi YS, Chang JH, Kang SG, Kim SH, Zhou J, Lee SK. Amide proton transfer imaging might predict survival and IDH mutation status in high-grade glioma. Eur Radiol. 2019 Dec;29(12):6643-6652

[1] Park YW, Han K, Ahn SS*, Choi YS, Chang JH, Kim SH, Kang SG, Kim EH, Lee SK. Whole-Tumor Histogram and Texture Analyses of DTI for Evaluation of IDH1-Mutation and 1p/19q-Codeletion Status in World Health Organization Grade II Gliomas. American Journal of Neuroradiology. 2018 Apr;39(4):693-698

[2] Jun Y, Eo T, Kim T, Shin H, Hwang D, Bae SH, Park YW, Lee HJ, Choi BW, Ahn SS*. Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors. Sci Rep. 2018 Jun 21;8(1):9450

[3] Park YW, Han K, Ahn SS*, Bae S, Choi YS, Chang JH, Kim SH, Kang SK and Lee S-K. Prediction of IDH1-Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade Gliomas. American Journal of Neuroradiology. 2018 Jan;39(1);37-42

[4] Joo B, Han K, Choi YS, Lee SK, Ahn SS*, Chang JH, Kang SG, Kim SH, Zhou J. Amide proton transfer imaging for differentiation of benign and atypical meningiomas. Eur Radiol. 2018 Jan;28(1):331-339

[1] Park M, Lee SK, Chang JH, Kang SG, Kim EH, Kim SH, Song MK, Ma BG, Ahn SS*. Elderly patients with newly diagnosed glioblastoma: can preoperative imaging descriptors improve the predictive power of a survival model? J Neurooncol. 2017 Sep;134(2):423-431

[2] Choi YS,  Ahn SS*, Lee SK, Chang JH, Kang SG,  Kim SH, Zhou J. Amide proton transfer imaging to discriminate between low-and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume. Eur Radiol. 2017 Aug;27(8):3181-3189

[3] Jeong HK, Han K, Zhou J, Zhao Y, Choi YS, Lee SK, Ahn SS*. Characterizing amide proton transfer imaging in haemorrhage brain lesions using 3T MRI. Eur Radiol. 2017 Apr 27: 1577-1584

[4] Ahn SS, Han J*. Ecchordosis physaliphora presenting with abducens nerve palsy. J AAPOS. 2016 Jun 20: 266-8.

[5] Sohn, B., Koh, Y. W., Kang, W. J., Lee, J. H., Shin, N. Y., & Kim, J.* (2016). Is there an additive value of 18 F-FDG PET-CT to CT/MRI for detecting nodal metastasis in oropharyngeal squamous cell carcinoma patients with palpably negative neck?. Acta Radiologica, 57(11), 1352-1359.

[6] Choi YS, Kim DW, Lee S-K, Chang JH, Kang S-G, Kim EH, Kim SH, Rim TH, Ahn SS*. The Added Prognostic Value of Preoperative Dynamic Contrast-Enhanced MRI Histogram Analysis in Patients with Glioblastoma: Analysis of Overall and Progression-Free Survival. American Journal of Neuroradiology. 2015 Dec 36: 2235-41.

[7] Park M, Lee S-K, Choi J, Kim S-H, Shin N-Y, Kim J, Ahn SS*. Differentiation between Cystic Pituitary Adenomas and Rathke Cleft Cysts: A Diagnostic Model Using MRI. American Journal of Neuroradiology. 2015 Oct 36: 1866-73.

[8] Sohn, B., Lim, J. S., Kim, H., Myoung, S., Choi, J., Kim, N. K., & Kim, M. J.* (2015). MRI-detected extramural vascular invasion is an independent prognostic factor for synchronous metastasis in patients with rectal cancer. European radiology, 25(5), 1347-1355.

[9] Ahn SS, Kim SH, Lee JE, Ahn KJ, Kim DJ, Choi HS, Kim J, Shin N-Y, Lee S-K*. Effects of Agmatine on Blood-Brain Barrier Stabilization Assessed by Permeability MRI in a Rat Model of Transient Cerebral Ischemia. American Journal of Neuroradiology. 2015 Feb 36: 283-88

[10] Park, Y., Kim, M. D.*, Jung, D. C., Lee, S. J., Kim, G., Park, S. I., ... & Lee, D. Y. (2015). Can measurement of apparent diffusion coefficient before treatment predict the response to uterine artery embolization for adenomyosis?. European Radiology, 25(5), 1303-1309.

[11] Park, Y. W., Kim, M. J., Han, S. W., Kim, D. W., & Lee, M. J.* (2015). Meaning of ureter dilatation during ultrasonography in infants for evaluating vesicoureteral reflux. European Journal of radiology, 84(2), 307-311.

[12] Ahn SS, Shin N-Y, Chang JH, Kim SH, Kim EH, Kim DW, Lee S-K*. Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging. Journal of Neurosurgery. 2014 Jun 20: 1-7

[13] Sohn, B., Kim, M. J., Koh, H., Han, K. H., & Lee, M. J.* (2014). Intestinal lesions in pediatric Crohn disease: comparative detectability among pulse sequences at MR enterography. Pediatric radiology, 44(7), 821-830.

[14] Ahn SS, Nam HS, Heo JH, Kim YD, Lee S-K, Han K, Choi BW, Kim EY*. Ischemic stroke: Measurement of Intracranial Artery Calcifications Can Improve Prediction of Asymptomatic Coronary Artery Disease. Radiology 2013; 268: 842-849

[15] Ahn SS, Nam HS, Heo JH, Kim YD, Lee S-K, Han K, Kim EY*. Quantification of intracranial internal carotid artery calcification on brain unenhanced CT: evaluation of its feasibility and assessment of the reliability of visual grading scales. European Radiology 2013; 23: 20-27

[16] Ahn SS, Kim J*, An C, Choi HS, Lee S-K, Koh YW, Kim S-H, Choi EC. Preoperative imaging evaluation of head and neck cancer: Comparison of 2D spin-echo and 3D THRIVE MRI techniques with resected tumours. Clinical Radiology 2012; 37: e98-e104

[17] Ahn SS, Kim EY*. Thrombus imaging in acute ischaemic stroke using thin-slice unenhanced CT: comparison of conventional sequential CT and helical CT. European Radiology 2012; 22: 2392-2396

[18] Ahn SS, Kim BM*, Suh SH, Kim DJ, Kim DI, Shin YS, Ha SY, Kwon YS. Spontaneous Symptomatic Intracranial Vertebrobasilar Dissection: Initial and Follow-up Imaging Findings. Radiology 2012; 264: 196-202

[19] Ahn SS, Lee S-K*. Diffusion Tensor Imaging: Exploring the Motor Networks and Clinical Applications. Korean Journal of Radiology 2011; 12: 651-661

[20] Ahn SS, Kim M-J*, Lim JS, Hong H-S, Chung YE, Choi J-Y. Added value of gadoxetic acid-enhanced hepatobiliary phase MR imaging in the diagnosis of hepatocellular carcinoma. Radiology 2010; 255: 459-466

[21] Ahn SS, Kim M-J*, Kim DK. An Insidious Pancreatic Lesion in a Young Woman with Recurrent Pancreatitis. Gastroenterology 2010; 139(1): e9

[22] Ahn SS, Kim E-K*, Kang DR, Lim S-K, Kwak JY, Kim MJ. Biopsy of Thyroid Nodules: Comparison of Three Sets of Guidelines. AJR 2010; 194: 31

[23] Ahn SS, Kim M-J*, Choi J-Y, Hong H-S, Chung YE, Lim JS. Indicative findings of pancreatic cancer in prediagnostic CT. European Radiology 2009; 19: 2448

[24] Ahn SS, Kim Y-J, Hur J, Lee H-J, Kim TH, Choe KO, Choi BW*. CT Detection of Subendocardial Fat in Myocardial Infarction. AJR 2009; 192: 532-537

[25] Ahn SS, Kim E-K*, Kwak JY, Kim MJ. Diagnosis of Parathyroid Adenoma Detected during Thyroid Ultrasound: Role of Parathormone Measurement in the Fine Needle Aspiration Washout. J Kor Soc Ultrasound Med. 2009; 28: 27

[26] Ahn SS, Seo Y-K, Baek S-E, Bae S-Y, Seol J-H, Lee H-Y, Park E-C. “The correlation of grade point average of medical school and the score of Korean medical licensing examination” Korean Journal of Medical Education 2004; 16: 25-32