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Machine Learning-Assisted Multimodal Early Screening of Lung Cancer Based on a Multiplexed Laser-Induced Graphene
Yongsheng Cai1,2, Lihui Ke1,2, Anxu Du2
1Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing 100191, China.
This study introduces a novel multimodal platform for early lung cancer detection, integrating a laser-induced graphene immunosensor and machine learning. The advanced system significantly improves diagnostic accuracy, offering a promising tool for timely intervention.
Area of Science:
- Biomedical Engineering
- Oncology
- Nanotechnology
Background:
- Lung cancer is a leading cause of cancer mortality globally, often diagnosed at late stages.
- Current screening methods like low-dose CT have limitations in sensitivity and specificity for early detection.
Purpose of the Study:
- To develop and validate a multimodal early screening platform for lung cancer.
- To enhance diagnostic accuracy by integrating proteomic, imaging, and clinical data.
Main Methods:
- Development of a multiplexed laser-induced graphene (LIG) immunosensor for simultaneous detection of four lung cancer biomarkers: neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), p53, and SOX2.
- Integration of LIG immunosensor proteomic data with deep learning-based CT imaging features and clinical data.
- Creation of a multimodal predictive model for lung cancer diagnosis.
Main Results:
- The LIG immunosensor achieved low limits of detection (LOD) as low as 1.62 pg/mL for the targeted tumor markers.
- The multimodal predictive model demonstrated a high diagnostic performance with an area under the curve (AUC) of 0.936.
- The multimodal approach significantly outperformed single-modality screening methods.
Conclusions:
- The developed multimodal platform offers a transformative solution for early lung cancer detection.
- This technology shows potential for cost-effective and accurate screening, especially in resource-limited settings.
- The platform provides technical support for precision medicine in oncology.
