Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response
The construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.
Authors
- Liuqing Zhou
- Shoukang Li (ORCID: https://orcid.org/0000-0001-7625-8524)
- Tao Zhou (ORCID: https://orcid.org/0000-0002-9265-4946)
- Yuan Tian (ORCID: https://orcid.org/0009-0006-3773-8131)
- Hai‐Ying Sun (ORCID: https://orcid.org/0000-0001-5648-337X)
Institutions
- Union Hospital (HK)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-12-19
- DOI
- https://doi.org/10.3389/fimmu.2024.1469895
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Hubei Province
- Tongji University