565-04-003 ISMRM Abstract

Multimodal MRI-Based Habitat Model for Predicting WHO Grading and Ki-67 Labeling Index in HGG: A Multicenter Study

Primary: Neuro - Tumors

Secondary: Analysis Methods - Radiomics

565-04-003 · Neuroradiomics · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row F

Keywords: Glioma Machine learning Habitat analysis Ki-67 expression

Accepted

Wei Zhao ^1,2, Yunling Wang¹, Yushan Chang², Hanjiaerbieke Kukun¹, Rui Xu¹, Pahati Tuxunjiang¹, Wei Sheng³

¹Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China

²Imaging Center, The Fourth Affiliated Hospital of Xinjiang Medical University, China

³MR Research collaboration, Siemens Healthineers Ltd., Chengdu, China

Presenting Author: Wei Zhao

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References

1. Louis DN, Perry A, Wesseling P et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology 23:1231-1251. doi:10.1093/neuonc/noab106 [doi]

2. Liu J, Cao S, Imbach KJ et al (2024) Multi-scale signaling and tumor evolution in high-grade gliomas. Cancer Cell 42:1217-1238.e1219. doi:10.1016/j.ccell.2024.06.004 [doi]

3. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2020) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18:203-211. doi:10.1038/s41592-020-01008-z [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/565-04-003