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.