Machine Learning-Assisted Multimodal Early Screening of Lung Cancer Based on a Multiplexed Laser-Induced Graphene Immunosensor.

Journal: ACS nano
Published Date: Jul 11, 2025

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Early detection is critical for improving patient outcomes, yet current screening methods, such as low-dose computed tomography (CT), often lack the sensitivity and specificity required for early-stage detection. Here, we present a multimodal early screening platform that integrates a multiplexed laser-induced graphene (LIG) immunosensor with machine learning to enhance the accuracy of lung cancer diagnosis. Our platform enables the rapid, cost-effective, and simultaneous detection of four tumor markers─neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), p53, and SOX2─with limits of detection (LOD) as low as 1.62 pg/mL. By combining proteomic data from the immunosensor with deep learning-based CT imaging features and clinical data, we developed a multimodal predictive model that achieves an area under the curve (AUC) of 0.936, significantly outperforming single-modality approaches. This platform offers a transformative solution for early lung cancer screening, particularly in resource-limited settings, and provides potential technical support for precision medicine in oncology.

Authors

  • Yongsheng Cai
    Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing 100191, China.
  • Lihui Ke
    Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing 100191, China.
  • Anxu Du
    Department of Thoracic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
  • Jiancheng Dong
    Department of Medical Informatics, Medical School, Nantong University, Nantong, Jiangsu 226001, China.
  • Zitong Gai
    School of Pharmacy, Liaocheng Vocational and Technical College, Liaocheng 252000, China.
  • Lichun Gao
    School of Management, Wuhan University of Technology, Wuhan, Hubei 430070, China.
  • Xiaoxiao Yang
    Nantong Key Laboratory of Public Health and Medical Analysis, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, PR China.
  • Hao Han
    Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore.
  • Minghua Du
    Department of Emergency, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China.
  • Guangliang Qiang
    Department of Thoracic Surgery, Peking University Third Hospital, Beijing 100191, China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Bo Wei
    1 Department of General Surgery, Chinese PLA General Hospital, Beijing 100853, China.
  • Yubo Fan
    State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. yubofan@buaa.edu.cn.
  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.

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