565-02-008 ISMRM Abstract

ADC-Based MRI Radiomics Correlates of Overall Survival in H3K27M-Mutant Pediatric Diffuse Midline Gliomas

Primary: Pediatrics - Neuro

Secondary: Neuro - Tumors

565-02-008 · Neuro-Oncology and Ear, Nose, and Throat Cancer · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row F

Keywords: Pediatric Radiomics Brain tumor Diffuse midline glioma ADC

Accepted

Melisa Özakçakaya ¹, N. Tugay Guven¹, Gökçe Hale Hatay², Esra Sümer-Arpak³, Ayca Ersen Danyeli^4,5,6, Memet Özek⁷, Bahattin Tanrıkulu⁷, Alp Dinçer^4,5,8, Esin Ozturk Isik^1,4

¹Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey

²Siemens Healthineers, Istanbul, Turkey

³Division of Embedded Intelligent Systems LAB, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden

⁴Brain Tumor Research Group, Acibadem University, Istanbul, Turkey

⁵Acibadem University, Istanbul, Turkey

⁶Department of Pathology, Acibadem University, Istanbul, Turkey

⁷Department of Pediatric Neurosurgery, Acibadem University, Istanbul, Turkey

⁸Department of Radiology, Acibadem University, Istanbul, Turkey

Presenting Author: Melisa Özakçakaya

Synopsis

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References

1. Thust S, Micallef C, Okuchi S, et al. Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults. Quant Imaging Med Surg. 2021;11(1):43-56. doi:10.21037/qims-19-954 [doi]

2. Aboian MS, Tong E, Solomon DA, et al. Diffusion Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3-K27M Mutation Using Apparent Diffusion Coefficient Histogram Analysis. AJNR Am J Neuroradiol. 2019. doi:10.3174/ajnr.a6302 [doi]

3. Aboian MS, Solomon DA, Felton E, et al. Imaging Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3 K27M Mutation. AJNR Am J Neuroradiol. 2017;38(4):795-800. doi:10.3174/ajnr.a5076 [doi]

4. Peerlings J, Woodruff HC, Winfield JM, et al. Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Sci Rep. 2019;9(1). doi:10.1038/s41598-019-41344-5 [doi]

5. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging. 2020;11(1). doi:10.1186/s13244-020-00887-2 [doi]

6. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9);1323-1341. doi:10.1016/j.mri.2012.05.001 [doi]

7. Ashburner J. SPM: A history. NeuroImage. 2012;62(2):791-800. doi:10.1016/j.neuroimage.2011.10.025 [doi]

8. van Griethuysen JJ, Fedorov A, Parmar C, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer research. 2017;77(21):e104-e107. doi:https://doi.org/10.1158/0008-5472.CAN-17-0339 [doi]

9. Vallero SG, Bertero L, Morana G, et al. Pediatric diffuse midline glioma H3K27- altered: A complex clinical and biological landscape behind a neatly defined tumor type. Front Oncol. 2023;12. doi:10.3389/fonc.2022.1082062 [doi]

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http://echo.ismrm.org/p/ISMRM2026/565-02-008