Development and validation of a machine learning model based on laboratory parameters for preoperative prediction of Ki-67 expression in gliomas
The authors successfully constructed an LR model based on routine laboratory parameters, with relatively high sensitivity and specificity, to preoperatively predict the level of Ki-67 in patients with gliomas, which might be helpful for prognostic evaluation and clinical decision-making.
Authors
- Lijin Lin (ORCID: https://orcid.org/0000-0001-7689-917X)
- Qingwen Luo
- Jinlan Huang (ORCID: https://orcid.org/0000-0002-1370-2381)
- Guanglei Zhong (ORCID: https://orcid.org/0000-0002-9122-9147)
- Sufei Zheng
- Zhou Yu (ORCID: https://orcid.org/0000-0002-2358-0090)
- Dong-Mei Chen (ORCID: https://orcid.org/0000-0003-0329-5118)
- Shihao Zheng
- Yazhi Chen
- Shoupeng Ding
Institutions
- Fujian Medical University (CN)
- First Affiliated Hospital of Fujian Medical University (CN)
- County Hospital (GB)
- Fujian Provincial Hospital (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Journal of neurosurgery
- Published
- 2025-03-29
- DOI
- https://doi.org/10.3171/2024.11.jns241673
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36