The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma
The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.
Authors
- Zhenguo Yuan
- Zong-Ying Wang
- Xizhen Wang (ORCID: https://orcid.org/0000-0003-0993-1170)
- Yao Li (ORCID: https://orcid.org/0000-0003-1162-2166)
- Ximing Wang (ORCID: https://orcid.org/0000-0002-1213-1003)
- Zhifeng Chen (ORCID: https://orcid.org/0000-0001-7824-373X)
- An-Ning Ren
- He-Xin Liang
Institutions
- Weifang Medical University (CN)
- Shandong University (CN)
- Weifang People's Hospital (CN)
- Shandong Provincial Hospital (CN)
- Zoucheng People's Hospital (CN)
- Shandong First Medical University (CN)
- Monash University (AU)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1186/s12880-024-01414-1
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.45
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
- Key Technology Research and Development Program of Shandong