Integration of intratumoral/peritumoral radiomics and deep learning for predicting overall survival in non-small cell lung cancer patients: a multicenter study
The combined model integrates intratumoral/peritumoral radiomics features and 3D DL features and effectively predicts the OS of NSCLC patients, offering a novel tool for personalized radiotherapy strategies.
Authors
- Jianfeng Qiu (ORCID: https://orcid.org/0000-0002-8049-3743)
- Qiusheng Wang (ORCID: https://orcid.org/0000-0002-3197-6327)
- Yinglun Sun
- Fuli Zhang (ORCID: https://orcid.org/0000-0002-4175-4795)
- Na Lu (ORCID: https://orcid.org/0000-0003-1026-9524)
- Diandian Chen (ORCID: https://orcid.org/0000-0003-4578-1720)
- Yuteng Pan
- Huijuan Zhang (ORCID: https://orcid.org/0000-0003-3060-4246)
- Yanjun Yu
- Yong-Xin Liu (ORCID: https://orcid.org/0000-0002-6506-7148)
- Yanxiang Gao (ORCID: https://orcid.org/0000-0003-2331-5956)
- Huayong Jiang (ORCID: https://orcid.org/0000-0002-4977-300X)
Institutions
- Chinese PLA General Hospital (CN)
- First Affiliated Hospital of Jinan University (CN)
- Shandong First Medical University (CN)
- Beihang University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3389/fonc.2025.1669200
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00