Deep learning-based quantification of T2-FLAIR mismatch sign: extending IDH mutation prediction in adult-type diffuse lower-grade glioma
Question Does deep-learning-based quantification of the T2-FLAIR mismatch sign provide accurate prediction of IDH-mutant, 1p/19q non-codeleted astrocytomas and enhance identification of IDH mutation status? Findings Quantifying the T2-FLAIR mismatch sign with a fully automated segmentation tool achieved high accuracy in identifying IDH-mutant, 1p/19q non-codeleted astrocytomas, and enhanced IDH status prediction. Clinical relevance Integrating the qT2FM into clinical protocols enhances diagnostic precision and guides treatment strategies, underscoring the role of advanced imaging in neuro-oncology.
Authors
- Kyu Sung Choi (ORCID: https://orcid.org/0000-0002-5175-3307)
- Taehyuk Ham (ORCID: https://orcid.org/0000-0002-3432-2177)
- Roh‐Eul Yoo (ORCID: https://orcid.org/0000-0002-5625-5921)
- Inpyeong Hwang (ORCID: https://orcid.org/0000-0002-1291-8973)
- Tae Jin Yun (ORCID: https://orcid.org/0000-0001-8441-4574)
- Ji Ye Lee (ORCID: https://orcid.org/0000-0002-3929-6254)
- Koung Mi Kang (ORCID: https://orcid.org/0000-0001-9643-2008)
- Young Hun Jeon (ORCID: https://orcid.org/0000-0001-7672-3932)
- Chul‐Ho Sohn (ORCID: https://orcid.org/0000-0003-0039-5746)
- Ji‐hoon Kim (ORCID: https://orcid.org/0000-0002-6349-6950)
- Kyung Hoon Lee
- Seung Hong Choi
- Seong Yun Jeong
Institutions
- Seoul National University (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- Kangbuk Samsung Hospital (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- European Radiology
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1007/s00330-025-11475-7
- Citations
- 9
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 14.63
Funders
- Seoul National University
- Seoul National University Hospital