Prediction of first-line immunotherapy response in patients with extensive-stage small cell lung cancer using a clinical-radiomics combined model
The nomogram that included ITR, delta radiomic features, and clinical risk factors had the best performance in predicting prognosis for patients with ES-SCLC who received immunotherapy compared to models relying solely on radiomic features or clinical risk factors, and has the potential to assist clinicians in making personalized treatment decisions.
Authors
- Xiaopeng Song (ORCID: https://orcid.org/0000-0003-0074-6489)
- Chunlin Zhao (ORCID: https://orcid.org/0000-0003-1333-1864)
- Fang Zhao (ORCID: https://orcid.org/0000-0002-5485-5333)
- Xingyi Li (ORCID: https://orcid.org/0000-0002-0368-4538)
- Jing Fan (ORCID: https://orcid.org/0000-0001-7629-1981)
- Zhenxiang Li (ORCID: https://orcid.org/0000-0002-9648-8869)
- Jiamao Lin (ORCID: https://orcid.org/0000-0001-7975-6713)
Institutions
- Heze Municipal Hospital (CN)
- Qilu Hospital of Shandong University (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-12-12
- DOI
- https://doi.org/10.3389/fimmu.2025.1688012
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00