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Authors
Hu, Min
Hu, Xingyu
Chen, Wenxiang
Xia, Chengcheng
Yao, Weirong
Zuo, Minjing
Lin, Ze
Deng, Libin
Wu, Zhaoqiu
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Funding
Funding for this research was provided by:
Wu Jieping Medical Foundation of China (320.6750.2020-10-108)
National Natural Science Foundation of China (82125036)
National Key Research and Development Program of China (2023YFA1801900)
Jiangsu Provincial Natural Science Fund for Distinguished Young Scholar (BK20230042)
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Article History
Received: 21 April 2025
Accepted: 28 October 2025
First Online: 26 November 2025
Declarations
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: The authors declare no competing interests.
: The studies involving human participants underwent a thorough review and was approved by the ethics committees of Jiangxi Provincial People’s Hospital (Nanchang, China) and Second Affiliated Hospital of Nanchang University (Nanchang, China). Public database data were exempt from consent requirements.
: Written informed consent was waived by the Institutional Review Board.
: 724 of the 787 included cases have been previously reported in “Xia C, Zuo M, Lin Z, Deng L, Rao Y, Chen W, Chen J, Yao W, Hu M. Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis. Acad Radiol. 2024 Dec 26:S1076-6332(24)00965-6.” In methods and strategies, the two studies are completely different. This study has achieved all-round upgrades and innovations. First, it innovatively generates an indicator of the key imaging intratumoral heterogeneity dimension, greatly enriching the data dimension. Meanwhile, this study optimizes the deep learning strategy, providing new research ideas and methods for related fields.
