Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

Journal: Respiratory research
PMID: 38730430

Abstract

BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed.

Authors

  • Meng-Rui Lee
    Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan leemr@ntu.edu.tw kuopc@cs.nthu.edu.tw.
  • Mu-Hsiang Kao
    Department. of Electrical Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.
  • Ya-Chu Hsieh
    Department. of Electrical Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.
  • Min Sun
    Division of Oncology, University of Pittsburgh Medical Center Hillman Cancer Center at St. Margaret, 200 Delafield Rd, Pittsburgh, PA, 15215, USA.
  • Kea-Tiong Tang
  • Jann-Yuan Wang
    Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
  • Chao-Chi Ho
    Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
  • Jin-Yuan Shih
    Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Chong-Jen Yu
    Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan.

Keywords

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