Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer
The INTRAPERI_GradientBoosting model, which integrates intra-tumoral and peritumoral features, performs better than the INTRA intra-tumoral and PERI peritumoral radiomics models in predicting the efficacy of IT therapy in LA-NSCLC. Additionally, the nomogram based on INTRAPERI intra-tumoral and peritumoral features combined with independent clinical predictors has clinical predictive value.
Authors
- Zhengjun Guo (ORCID: https://orcid.org/0009-0005-7013-0008)
- Xiao‐Hui Bao (ORCID: https://orcid.org/0000-0002-6721-3807)
- XiaoYue Zhang
- Feng Cai (ORCID: https://orcid.org/0000-0003-4473-6477)
- ZhenZhou Yang
- Zhou Xiu
- Dan Tang
- Zhi X. Chen (ORCID: https://orcid.org/0000-0002-1149-1998)
- Min Lv
- Yang Yang (ORCID: https://orcid.org/0000-0001-6788-2843)
- Jie He (ORCID: https://orcid.org/0000-0002-0285-5403)
- Guoyu Wang (ORCID: https://orcid.org/0000-0002-9234-1747)
- Feng Luo (ORCID: https://orcid.org/0000-0003-4008-271X)
Institutions
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-03-13
- DOI
- https://doi.org/10.1186/s12885-025-13804-x
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Chongqing