Machine Learning-Assisted Multimodal Early Screening of Lung Cancer Based on a Multiplexed Laser-Induced Graphene Immunosensor.
Authors
Affiliations (8)
Affiliations (8)
- 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.
- Department of Thoracic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
- School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
- School of Pharmacy, Liaocheng Vocational and Technical College, Liaocheng 252000, China.
- School of Management, Wuhan University of Technology, Wuhan, Hubei 430070, China.
- People's Hospital of Rizhao, Rizhao, Shandong 276825, China.
- Department of Emergency, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China.
- Department of Thoracic Surgery, Peking University Third Hospital, Beijing 100191, China.
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.