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Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures

  • Chest Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Background

This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT).

Methods

Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures. Machine-learning algorithms were employed to develop radiomics signature while the DL signature was developed using neural architecture search. The diagnostic efficiency was primarily quantified with the area under receiver operating characteristic curve (AUC). We interpreted the reasoning process of the radiomics signature and DL signature by radiomics voxel mapping and attention weight tracking.

Results

A total of 674 patients with pathologically-confirmed NSCLC were included from two institutions, with 143 of them having DM. The radiomics signature achieved AUCs of 0.885, 0.854, and 0.733 in the internal validation, prospective validation, and external validation while those for DL signature were 0.893, 0.786, and 0.780. The proposed signatures achieved a promising performance in predicting the DM of NSCLC and outperformed the approaches proposed in previous studies. Interpretability analysis revealed that both radiomics and DL signatures could detect the variations among voxels inside tumors, which helped in identifying the DM of NSCLC.

Conclusions

Our study demonstrates the potential of LDCT-based radiomics and DL signatures for predicting DM in NSCLC. These signatures could help improve lung cancer screening regarding further diagnostic tests and treatment strategies.

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Availability of data and materials

The datasets analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This study was funded by grants from the National Natural Science Foundation of China (grants number 82000628 and 62176104), the Medical Scientific Research Foundation of Guangdong Province of China (grants number B2022113), and the Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine Foundation of Guangdong Province (2023LSYS001).

Author information

Author notes

  1. Xiaoyi Song and Xiaobei Duan contributed equally to this work.

Authors and Affiliations

  1. Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, Guangdong Province, China

    Xiaoyi Song, Man Li & Hong Shan

  2. Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, China

    Xiaoyi Song, Man Li & Hong Shan

  3. Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, 529030, China

    Xiaobei Duan

  4. Department of Nuclear Medicine, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, Guangdong Province, China

    Xinghua He, Yubo Wang, Bangxuan Deng & Ying Wang

  5. Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, China

    Kunwei Li

  6. Department of Radiology, Jiangmen Central Hospital, Jiangmen, 529030, China

    Xiangmeng Chen

  7. Department of Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, China

    Hong Shan

  8. Department of Information Technology and Data Center, the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, China

    Man Li

  9. Biobank of the Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, China

    Man Li

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Contributions

Guarantors of integrity of entire study, XYS, XBD, XHH, YBW, KWL, BXD, XMC, YW, ML, HS; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, XYS, XBD, WY, ML, HS; clinical studies, XYS, XBD, XHH, YBW, KWL, BXD, XMC, YW, HS; experimental studies, XYS, XBD, XHH, YBW, ML, HS; statistical analysis, XYS, XBD, ML; and manuscript editing, XYS, XBD, ML, HS.

Corresponding authors

Correspondence to Ying Wang, Man Li or Hong Shan.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

This study was approved by our institutional review board: Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University (approval number: [2021] K01-1). For patients in retrospective cohort, the written informed consents were waived while those of patients in prospective cohort were obtained in this study.

Consent to participate

For patients in retrospective cohort, the written informed consents were waived while those of patients in prospective cohort were obtained in this study.

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Song, X., Duan, X., He, X. et al. Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures. Radiol med 129, 239–251 (2024). https://doi.org/10.1007/s11547-024-01770-6

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  • DOI: https://doi.org/10.1007/s11547-024-01770-6

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