1. Choi KS, Choi SH, Jeong B. Prediction of IDH genotype in gliomas with dynamic susceptibility
contrast perfusion MR imaging using an explainable recurrent neural network. Neuro-Oncology. 2019;
21(9):1197-1209.
 

2.  Park  E-A,  Lee  W,  So YH,  Choi  KS,  et  al.  Extremely  small  pseudoparamagnetic  iron  oxide
nanoparticle as a novel blood pool T1 magnetic resonance contrast agent for 3 T whole-heart coronary
angiography  in  canines:  comparison  with  gadoterate  meglumine.  Investigative  radiology.  2017;
52(2):128-133.
 

3. Choi KS, Kim SH, Kim SG, Han JK. Early gastric cancers: is CT surveillance necessary after curative
endoscopic  submucosal  resection  for  cancers  that  meet  the  expanded  criteria?  Radiology.  2016;
281(2):444-453.
 

4. Choi  KS, Choi YH, Cheon J-E, Kim WS, Kim IO. Intestinal malrotation in patients with situs
anomaly: Implication of the relative positions of the superior mesenteric artery and vein. European
journal of radiology. 2016; 85(10):1695-1700.
 

5. Choi KS, Lee JM, Joo I, Han JK, Choi BI. Evaluation of perihilar biliary strictures: does DWI provide
additional value to conventional MRI? American Journal of Roentgenology. 2015; 205(4):789-796.
 

6. Choi KS, Kim JD, Kim H-C, et al. Percutaneous aspiration embolectomy using guiding catheter for
the superior mesenteric artery embolism. Korean journal of radiology. 2015; 16(4):736-743.
 

7. Choi KS, You S-H, Han Y, Ye JC, Jeong B, Choi SH. Improving the Reliability of Pharmacokinetic
Parameters  at  Dynamic  Contrast-enhanced  MRI  in  Astrocytomas:  A  Deep  Learning  Approach.
Radiology. 2020; 297(1):178-188.
 

8. Choi KS, Lee W, Jung JH, Park E-A. Reproducibility of calcium scoring of the coronary arteries:
comparison between different vendors and iterative reconstructions. Acta Radiologica Open. 2020;9(4):2058460120922147.
 

9. Choi KS, Choi YH, Cheon J-E, Kim WS, Kim IO. Application of T1-weighted BLADE sequence to
abdominal magnetic resonance imaging of young children: a comparison with turbo spin echo sequence.
Acta Radiologica. 2020, 61(10):1406-1413.
 

10. Shim KY, Chung SW, Jeong JH, Hwang I, Park C-K, Choi  KS*, et al. Radiomics-based neural
network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced
MRI. Scientific Reports. 2021;11(1):9974.
 

11. Pak E, Choi KS, Choi SH, et al. Prediction of Prognosis in Glioblastoma Using Radiomics Features
of Dynamic Contrast-Enhanced MRI. Korean J Radiol. 2021; 22.
 

12. Choi, K. S., Kim, S., Kim, B. H., Jeon, H. J., Kim, J. H., Jang, J. H., & Jeong, B. (2021). Deep
graph neural network-based prediction of acute suicidal ideation in young adults. Scientific reports,
11(1), 1-11.
 

13. Kim, A. R., Choi, K. S., Kim, M. S., Kim, K. M., Kang, H., Kim, S., ... & Park, C. K. (2021).
Absolute quantification of tumor-infiltrating immune cells in high-grade glioma identifies prognostic
and radiomics values. Cancer Immunology, Immunotherapy, 70(7), 1995-2008.
 

14. Nam JY, Chung HJ, Choi KS, ... & Lee JH (2022). Deep learning model for diagnosing gastric
mucosal  lesions  using  endoscopic  images:  development,  validation,  and  method
comparison. Gastrointestinal Endoscopy, 95(2), 258-268.
 

15. Kim, M., Choi, K. S., Hyun, R. C., Hwang, I., Yun, T. J., Kim, S. M., & Kim, J. H. (2022). Free-
water diffusion tensor imaging detects occult periependymal abnormality in the AQP4-IgG-seropositive
neuromyelitis optica spectrum disorder. Scientific reports, 12(1), 1-10.
 

16.  Choi  KS.,  Sunwoo  L.  (2022).  Artificial  Intelligence  in  Neuroimaging:  Clinical
Applications.  Investig Magn Reson Imaging. 2022; 26(1):1-9.
 

17. Lee, J.Y., Yoo, R.-E., Rhim, J.H., Lee, K.H., Choi, K.S., Hwang, I., Kang, K.M., Kim, J.-h. (2022).
Validation of Ultrasound Risk Stratification Systems for Cervical Lymph Node Metastasis in Patients
with Thyroid Cancer. Cancers, 14(9), 2106.
 

18. Kang, H., Witanto, J.N., Pratama, K., Lee, D., Choi, K.S., Choi, S.H., Kim, K.-M., Kim, M.-S.,
Kim, J.W., Kim, Y.H., Park, S.J. and Park, C.-K. (2023), Fully Automated MRI Segmentation and
Volumetric Measurement of Intracranial Meningioma Using Deep Learning. J Magn Reson Imaging,
57(3): 871-881.
 

19. Yun, S. Y., Choi, K. S., Suh, C. H., Kim, S. C., Heo, H., Shim, W. H., ... & Kim, J. H. (2023). Risk estimation  for  idiopathic  normal-pressure  hydrocephalus:  development  and  validation  of  a  brain morphometry–based nomogram. European Radiology, 1-12.

20. Lee JO, Ahn SS, Choi KS*, Lee J, Jang J, Park JH, Hwang I, Park CK, Park SH, Chung JW, Choi SH. Added Prognostic Value of 3D Deep Learning-Derived Features from Preoperative MRI for Adult-type Diffuse Gliomas. Neuro Oncol. 2023 Oct 19:noad202. doi: 10.1093/neuonc/noad202. Epub ahead of print. PMID: 37855826.

21. Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Choi KS, Park JE, Ahn SS, Chang JH, Kim SH. The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 3-Summary of Imaging Findings on Glioneuronal and Neuronal Tumors. J Magn Reson Imaging. 2023 Sep 16. doi: 10.1002/jmri.29016. Epub ahead of print. PMID: 37715567.

22. Kim M, Choi KS, Hyun RC, Hwang I, Kwon YN, Sung JJ, Kim SM, Kim JH. Structural disconnection is associated with disability in the neuromyelitis optica spectrum disorder. Brain Imaging Behav. 2023 Sep 7. doi: 10.1007/s11682-023-00792-4. Epub ahead of print. PMID: 37676409.

23. Jeon YH, Lee JY, Yoo RE, Rhim JH, Lee KH, Choi KS, Hwang I, Kang KM, Kim JH. Validation of Ultrasound and Computed Tomography-Based Risk Stratification System and Biopsy Criteria for Cervical Lymph Nodes in Preoperative Patients With Thyroid Cancer. Korean J Radiol. 2023 Sep;24(9):912-923. doi: 10.3348/kjr.2023.0215. PMID: 37634645; PMCID: PMC10462897. 

24. Choi KS, Hwang I, Moon JH, Kim M. Progressive reduction in basal ganglia explains and predicts cerebral structural alteration in type 2 diabetes. J Cereb Blood Flow Metab. 2023 Aug 26:271678X231197273. doi: 10.1177/0271678X231197273. Epub ahead of print. PMID: 37632261.

25. Heo D, Lee J, Yoo RE, Choi SH, Kim TM, Park CK, Park SH, Won JK, Lee JH, Lee ST, Choi KS, Lee JY, Hwang I, Kang KM, Yun TJ. Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4). Sci Rep. 2023 Aug 24;13(1):13864. doi: 10.1038/s41598-023-41171-9. PMID: 37620555; PMCID: PMC10449894.

26. Yoo H, Yoo RE, Choi SH, Hwang I, Lee JY, Seo JY, Koh SY, Choi KS, Kang KM, Yun TJ. Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI. Eur Radiol. 2023 Jul 27. doi: 10.1007/s00330-023-09918-0. Epub ahead of print. PMID: 37498386. 

27. Choi KS. Editorial for "Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics". J Magn Reson Imaging. 2023 Oct 31. doi: 10.1002/jmri.29102. Epub ahead of print. PMID: 37905976 

28. Choi KS. Deep Learning Applications in Perfusion MRI: Recent Advances and Current Challenges . Investig Magn Reson Imaging. 2022;26(4):246-255

29. Kim BH, Lee H, Choi KS*, Nam JG, Park CK, Park SH, Chung JW, Choi SH. Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge. Cancers (Basel). 2022 Oct 3;14(19):4827. doi: 10.3390/cancers14194827. PMID: 36230750; PMCID: PMC9562637

30. Hwang EJ, Lee JS, Lee JH, Lim WH, Kim JH, Choi KS, Choi TW, Kim TH, Goo JM, Park CM. Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs. Radiology. 2021 Nov;301(2):455-463. doi: 10.1148/radiol.2021210578. Epub 2021 Aug 31. PMID: 34463551.

31. Park YW, Choi KS, Kim S, Han K, Park JE, Ahn SS, Choi SH, Kim HS, Chang JH, Kim SH, Lee SK. Incorporating Supramaximal Resection in Survival Stratification of IDH-wildtype Glioblastoma: A Refined Multi-Institutional Recursive Partitioning Analysis, Clinical Cancer Research. 2024 Nov, 30(21):4866-4875. 

32. Kim, M., Hwang, I., Park, J. H., Chung, J. W., Kim, S. M., Kim, J. H., & *Choi, K. S. (2024). Comparative analysis of glymphatic system alterations in multiple sclerosis and neuromyelitis optica spectrum disorder using MRI indices from diffusion tensor imaging. Human Brain Mapping, 45(5), e26680.

33. Ji, S. H., Yoo, R. E., Choi, S. H., Lee, W. J., Lee, S. T., Jeon, Y. H., Choi KS ... & Yun, T. J. (2024). Dynamic contrast-enhanced MRI quantification of altered vascular permeability in autoimmune encephalitis. Radiology, 310(3), e230701.

34. Choi, K. S., Park, C., Lee, J. Y., Lee, K. H., Jeon, Y. H., Hwang, I., ... & Kang, K. M. (2025). Prospective Evaluation of Accelerated Brain MRI Using Deep Learning-Based Reconstruction: Simultaneous Application to 2D Spin-Echo and 3D Gradient-Echo Sequences. Korean Journal of Radiology, 26(1), 54.

35. Yoon, J., Baek, N., Yoo, R. E., Choi, S. H., Kim, T. M., Park, C. K… Choi, K. S... & Yun, T. J. (2024). Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients. Scientific Reports, 14(1), 2171.

36. Ham, T., Lee, J. Y., Jeon, Y. H., Choi, K. S., Hwang, I., Yoo, R. E., ... & Kim, J. H. (2025). Safety and Efficacy of Ultrasound-Guided Thrombin Injection for Pseudoaneurysms Arising after Ultrasound-Guided Biopsy of Thyroid Nodules. American Journal of Neuroradiology, 46(1), 166-169. 

37. Lee, J., Jung, W., Yang, S., Park, J. H., Hwang, I., Chung, J. W., ... & Choi, K. S. (2024). Deep learning-based super-resolution and denoising algorithm improves reliability of dynamic contrast-enhanced MRI in diffuse glioma. Scientific Reports, 14(1), 25349.

38. Park, C. J., Choi, K. S., Park, J., Choi, S. H., Hwang, I., & Shin, T. (2024). Enhancement of artery visualization in contrast-enhanced cerebral MR angiography using generative neural networks. Biomedical Signal Processing and Control, 96, 106652. 

39. Jung, W., Jeong, G., Kim, S., Hwang, I., Choi, S. H., Jeon, Y. H., Choi, K. S ... & Kang, K. M. (2024). Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction. Neuroradiology, 1-12. 

40. Lee, J., Kim, D., Suh, C. H., Yun, S., Choi, K. S., Lee, S., ... & Lee, J. H. (2025). Automated Idiopathic Normal-Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis. American Journal of Neuroradiology, 46(1):33–40.

41. Otgonbaatar, C., Song, H., Jung, K. H., Hwang, I., Jeon, Y. H., Choi, K. S., ... & Sohn, C. H. (2024). Quantification of infarct core volume in patients with acute ischemic stroke using cerebral metabolic rate of oxygen (CMRO2) in CT perfusion. American Journal of Neuroradiology. 

42. Choi, K. S., Hwang, I., Park, C. K., Park, S. H., & Choi, S. H. (2025). New Subependymal Enhancement After Radiation Therapy in High‐Grade Glioma: Utilizing Morphological Features and DSC Perfusion MRI in Differentiate Progression and Post‐Radiation Changes. Journal of Magnetic Resonance Imaging, 61(4):1751–60.

43. Lee, H., Lee, J., Jang, J., Hwang, I., Choi, K. S., Park, J. H., ... & Choi, S. H. (2024). Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT. Neuroradiology, 66(4), 577-587.

44. Lyoo, Y. W., Lee, H., Lee, J., Park, J. H., Hwang, I., Chung, J. W., ... & Choi, K. S. (2025). Deep learning enhances reliability of dynamic contrast-enhanced MRI in diffuse gliomas: bypassing post-processing and providing uncertainty maps. European Radiology, 1-11.

45. Jang, J., Choi, K. S., Lee, J., Lee, H., Hwang, I., Park, J. H., ... & Kim, H. (2025). Unsupervised deep learning for blood-brain barrier leakage detection in diffuse glioma using dynamic contrast-enhanced MRI. Radiology: Artificial Intelligence, 7(3), e240507.

46. Koo, S. J., Yoo, R. E., Choi, K. S., Lee, K. H., Lee, H. B., Shin, D. J., ... & Choi, S. H. (2025). Deep Learning–Based Reconstruction for Accelerated Cervical Spine MRI: Utility in the Evaluation of Myelopathy and Degenerative Diseases. American Journal of Neuroradiology, 46(4), 750-757.

47. Kim, M., Hwang, I., Choi, K. S., Lee, J., Ryu, M., Park, J. H., & Moon, J. H. (2025). Normative Modeling Reveals Age‐Atypical Cortical Thickness Differences Between Hepatic Steatosis and Fibrosis in Non‐Alcoholic Fatty Liver Disease. Brain and Behavior, 15(4), e70466.

48. Jeon, Y. H., Choi, K. S., Lee, K. H., Jeong, S. Y., Lee, J. Y., Ham, T., ... & Sohn, C. H. (2025). Deep learning-based quantification of T2-FLAIR mismatch sign: extending IDH mutation prediction in adult-type diffuse lower-grade glioma. European Radiology, 1-10.

49. Jeon, Y. H., Park, C., Lee, K. H., Choi, K. S., Lee, J. Y., Hwang, I., ... & Kang, K. M. (2025). Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T. Neuroradiology, 1-11.

50. Kim, S., Bong, S. H., Yun, S., Kim, D., Yoo, J. H., Choi, K. S., ... & Jeong, B. (2025). Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study. Journal of Affective Disorders, 377, 225-234.