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Combining handcrafted radiomics features and deep learning features within different regions of interest in planning CT images for the prediction of symptomatic radiation pneumonitis in locally advanced non-small cell lung cancer

Crossref DOI link: https://doi.org/10.1186/s13014-026-02896-9

Published Online: 2026-07-31

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Li, Xuan

Hang, Hao

Kong, Yan

Zhang, Jinmeng

Su, Mingming

Sun, Zongqiong

Zhu, Yan

Yang, Fei

Zhao, Qing

Ni, Jianjiao

Huang, Jianfeng
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (No. 12205120)

Youth Scientific Research Project of Wuxi Municipal Health and Health Commission (Q201941)

Sponsored by Natural Science Foundation of Shanghai (No. 25ZR1402081)

Wuxi Taihu Talent Training Project (No. HB2020054)

Translational medicineProject of Wuxi Commission of Health (ZH202101)

Clinical research and translational medicine program of Affiliated Hospital of Jiangnan University (LCYJ202239)
License Information

Text and Data Mining valid from 2026-07-31

Accepted Manuscript valid from 2026-07-31
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Article History

Received: 26 November 2025

Accepted: 22 July 2026

First Online: 31 July 2026

Declarations

Ethics approval and consent to participate: This study was approved by the Institutional Review Boards of Fudan University Shanghai Cancer Center and Affiliated Hospital of Jiangnan University (Approval numbers: 090977-1; LS2019034), and was conducted in accordance with the Declaration of Helsinki.

Consent for publication: Not applicable.

Competing interests: The authors declare no competing interests.

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