Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients
Our findings underscore the potential of the BSL model in guiding the choice of adjuvant treatment for LGGs patients, potentially improving survival time. This study emphasizes the importance of ML in customizing patient care, understanding the nuances of treatment selection, and advancing personalized medicine.
Authors
- Weizhong Shi (ORCID: https://orcid.org/0000-0002-4862-6833)
- Enzhao Zhu (ORCID: https://orcid.org/0009-0006-4014-289X)
- Dan Shan (ORCID: https://orcid.org/0000-0002-2762-6770)
- Linlin Li (ORCID: https://orcid.org/0000-0002-1438-2738)
- Zhihao Chen (ORCID: https://orcid.org/0000-0003-0128-1409)
- Jiayi Wang (ORCID: https://orcid.org/0000-0003-1688-2864)
- Ziqin Xu
- Zhu Min (ORCID: https://orcid.org/0000-0003-1447-1279)
Institutions
- Tongji University (CN)
- East China University of Science and Technology (CN)
- Ollscoil na Gaillimhe – University of Galway (IE)
- Shanghai Hospital Development Center (CN)
- Columbia University (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-08-20
- DOI
- https://doi.org/10.1371/journal.pone.0306711
- Citations
- 1
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.44
Funders
- Health Research Board