Machine learning algorithms integrating positron emission tomography/computed tomography features to predict pathological complete response after neoadjuvant chemoimmunotherapy in lung cancer
This ML models offer a non-invasive and effective approach for predicting pCR after neoadjuvant chemoimmunotherapy in NSCLC.
Authors
- Nan Song (ORCID: https://orcid.org/0000-0002-2812-4887)
- Shuyu Ji
- Zhenxin Sheng (ORCID: https://orcid.org/0000-0002-1460-3422)
- Ziyun Shen (ORCID: https://orcid.org/0000-0001-5013-8532)
- Zhifu Mi (ORCID: https://orcid.org/0000-0001-8106-0694)
- Yancheng Chen
- Peng Zhang (ORCID: https://orcid.org/0000-0001-5994-3001)
- Lele Zhang (ORCID: https://orcid.org/0000-0002-5595-3103)
- Shiyue Wan
- Huansha Yu
Institutions
- Shihezi University (CN)
- Tongji University (CN)
- Overseas Development Institute (GB)
- Shanghai Pulmonary Hospital (CN)
- First Affiliated Hospital of Shihezi University Medical College (CN)
- Orient Capital (China) (CN)
Publication Details
- Journal
- European Journal of Cardio-Thoracic Surgery
- Published
- 2025-04-12
- DOI
- https://doi.org/10.1093/ejcts/ezaf132
- Citations
- 3
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.56