Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model
This article highlights that autoML promotes the accuracy and efficiency of feature selection and model construction. The radiomics model generated by autoML predicted the efficacy of immunotherapy in patients with advanced NSCLC effectively. This may provide a rapid and non-invasive method for making personalized clinical decisions.
Authors
- Wen Gao (ORCID: https://orcid.org/0000-0003-0893-5081)
- Zhuangxuan Ma (ORCID: https://orcid.org/0000-0003-3296-2952)
- Dewen Tang (ORCID: https://orcid.org/0009-0004-9813-9760)
- Siyun Lin
- Hou Huang
- Yu Yao
Institutions
- Shanghai Chest Hospital (CN)
- Huadong Hospital (CN)
- YangPu Geriatric Hospital (CN)
Publication Details
- Journal
- Diagnostic and Interventional Radiology
- Published
- 2025-01-16
- DOI
- https://doi.org/10.4274/dir.2024.242972
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16
Funders
- Shanghai Municipal Health Commission
- National Natural Science Foundation of China