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ASO Author Reflections: The Clinical Use of Dual-Region Radiomics-Based Machine Learning in the Identification of Subcarinal Lymph Node Metastasis of Non-small Cell Lung Cancer

  • ASO Author Reflections
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References

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Authors and Affiliations

  1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China

    Hao-Ji Yan MD, PhD & Dong Tian MD, PhD

  2. Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan

    Hao-Ji Yan MD, PhD

  3. College of Clinical Medicine, North Sichuan Medical College, Nanchong, China

    Jia-Sheng Zhao MD

  4. Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, China

    Qing Liu MD

  5. College of Medical Imaging, North Sichuan Medical College, Nanchong, China

    Chen Yang MD

Authors
  1. Hao-Ji Yan MD, PhD
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  2. Jia-Sheng Zhao MD
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  3. Qing Liu MD
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  4. Chen Yang MD
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  5. Dong Tian MD, PhD
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Corresponding author

Correspondence to Dong Tian MD, PhD.

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Disclosure

Hao-Ji Yan, Jia-Sheng Zhao, Qing Liu, Chen Yang, and Dong Tian declare no conflicts of interest.

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This article refers to: Yan HJ, Zhao JS, Zuo HD, et al. Dual-region computed tomography radiomics-based machine learning predicts subcarinal lymph node metastasis in patients with non-small cell lung cancer. Annals Surgical Oncology. Epub 23 Mar 2024. https://doi.org/10.1245/s10434-024-15197-w.

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Yan, HJ., Zhao, JS., Liu, Q. et al. ASO Author Reflections: The Clinical Use of Dual-Region Radiomics-Based Machine Learning in the Identification of Subcarinal Lymph Node Metastasis of Non-small Cell Lung Cancer. Ann Surg Oncol (2024). https://doi.org/10.1245/s10434-024-15271-3

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  • DOI: https://doi.org/10.1245/s10434-024-15271-3

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