Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large-Scale Multicenter Study.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Jun 1, 2025

Abstract

Most lung cancer (LC) patients are diagnosed at advanced stages due to the lack of effective screening tools. This multicenter study analyzes 1043 saliva samples (334 LC cases and 709 non-LC cases) using a novel high-throughput platform for metabolic fingerprint acquisition. Machine learning identifies 35 metabolic features distinguishing LC from non-LC subjects, enabling the development of a classification model named SalivaMLD. In the validation set and test set, SalivaMLD demonstrates strong diagnostic performance, achieving an area under the curve of 0.849-0.850, a sensitivity of 81.69-83.33%, and a specificity of 74.23-74.39%, outperforming conventional tumor biomarkers. Notably, SalivaMLD exhibits superior accuracy in distinguishing early stage LC patients. Hence, this rapid and noninvasive screening method may be widely applied in clinical practice for LC detection.

Authors

  • Shuang Lin
    Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
  • Runlan Yan
    Department of Geriatrics, Zhejiang Key Laboratory of Traditional Chinese Medicine for the Prevention and Treatment of Senile Chronic Diseases, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, China.
  • Junqi Zhu
    School of Economics and Management, Anhui University of Science and Technology, Huainan 232000, China.
  • Bei Li
    State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China. Electronic address: beili@ciomp.ac.cn.
  • Yinyan Zhong
    Pengbu Subdistrict Community Healthcare Center, Shangcheng District, Hangzhou, 310000, China.
  • Shuang Han
    Department of Pathology, Affiliated Hospital of Jiangnan University, No. 1000 Hefeng Road, Wuxi City, Jiangsu Province, 214122, China. han_frost@163.com.
  • Huiting Wang
    Department of Thoracic Oncology and Surgery, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China.
  • Jianmin Wu
    Center for Cancer Bioinformatics, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Zhao Chen
  • Yuyue Jiang
    Respiratory and Critical Care Medicine Department, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China.
  • Aiwu Pan
    Department of Internal Medicine, the Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310058, China.
  • Xuqing Huang
    Respiratory and Critical Care Medicine Department, Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310000, China.
  • Xiaoming Chen
    College of Mathematics and Computer Science, Fuzhou University, Fujian province, China.
  • Pingya Zhu
    Well-healthcare Technologies, Co., Ltd., Hangzhou, 310012, China.
  • Sheng Cao
    Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China.
  • Wenhua Liang
    Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Peng Ye
    Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
  • Yue Gao
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.

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