Ahn SS - Search Results

1

Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study.

Vollmuth P, Foltyn M, Huang RY, Galldiks N, Petersen J, Isensee F, van den Bent MJ, Barkhof F, Park JE, Park YW, Ahn SS, Brugnara G, Meredig H, Jain R, Smits M, Pope WB, Maier-Hein K, Weller M, Wen PY, Wick W, Bendszus M. Vollmuth P, et al. Among authors: ahn ss. Neuro Oncol. 2023 Mar 14;25(3):533-543. doi: 10.1093/neuonc/noac189. Neuro Oncol. 2023. PMID: 35917833 Free PMC article.

2

Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors.

Jun Y, Eo T, Kim T, Shin H, Hwang D, Bae SH, Park YW, Lee HJ, Choi BW, Ahn SS. Jun Y, et al. Among authors: ahn ss. Sci Rep. 2018 Jun 21;8(1):9450. doi: 10.1038/s41598-018-27742-1. Sci Rep. 2018. PMID: 29930257 Free PMC article. Clinical Trial.

3

Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

Park YW, Oh J, You SC, Han K, Ahn SS, Choi YS, Chang JH, Kim SH, Lee SK. Park YW, et al. Among authors: ahn ss. Eur Radiol. 2019 Aug;29(8):4068-4076. doi: 10.1007/s00330-018-5830-3. Epub 2018 Nov 15. Eur Radiol. 2019. PMID: 30443758

4

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.

Park YW, Choi YS, Ahn SS, Chang JH, Kim SH, Lee SK. Park YW, et al. Among authors: ahn ss. Korean J Radiol. 2019 Sep;20(9):1381-1389. doi: 10.3348/kjr.2018.0814. Korean J Radiol. 2019. PMID: 31464116 Free PMC article.

5

Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.

Park CJ, Choi YS, Park YW, Ahn SS, Kang SG, Chang JH, Kim SH, Lee SK. Park CJ, et al. Among authors: ahn ss. Neuroradiology. 2020 Mar;62(3):319-326. doi: 10.1007/s00234-019-02312-y. Epub 2019 Dec 9. Neuroradiology. 2020. PMID: 31820065

6

MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas.

Park CJ, Han K, Shin H, Ahn SS, Choi YS, Park YW, Chang JH, Kim SH, Jain R, Lee SK. Park CJ, et al. Among authors: ahn ss. Eur Radiol. 2020 Jun;30(6):3035-3045. doi: 10.1007/s00330-020-06683-2. Epub 2020 Feb 14. Eur Radiol. 2020. PMID: 32060714

7

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

Choi YS, Ahn SS, Chang JH, Kang SG, Kim EH, Kim SH, Jain R, Lee SK. Choi YS, et al. Among authors: ahn ss. Eur Radiol. 2020 Jul;30(7):3834-3842. doi: 10.1007/s00330-020-06737-5. Epub 2020 Mar 11. Eur Radiol. 2020. PMID: 32162004

8

Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade.

Park YW, Kim S, Ahn SS, Han K, Kang SG, Chang JH, Kim SH, Lee SK, Park SH. Park YW, et al. Among authors: ahn ss. Eur Radiol. 2020 Aug;30(8):4615-4622. doi: 10.1007/s00330-020-06788-8. Epub 2020 Apr 9. Eur Radiol. 2020. PMID: 32274524

9

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade.

Kim S, Park YW, Park SH, Ahn SS, Chang JH, Kim SH, Lee SK. Kim S, et al. Among authors: ahn ss. Brain Tumor Res Treat. 2020 Apr;8(1):36-42. doi: 10.14791/btrt.2020.8.e3. Brain Tumor Res Treat. 2020. PMID: 32390352 Free PMC article.

10

Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation.

Bae S, An C, Ahn SS, Kim H, Han K, Kim SW, Park JE, Kim HS, Lee SK. Bae S, et al. Among authors: ahn ss. Sci Rep. 2020 Jul 21;10(1):12110. doi: 10.1038/s41598-020-68980-6. Sci Rep. 2020. PMID: 32694637 Free PMC article.

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