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Early Detection of Lung Cancer Using a Convolutional Neural Network Integrating Multidimensional Clinical Information
Chia-Hui Chien1,2,3, Shih-Chuan Chang4, Muhammad Solihuddin Muhtar5
1Department of Computer Science, Middlesex University, London, UK.
Studies in Health Technology and Informatics
|August 8, 2025
Summary
This study developed an AI model using Taiwanese health data to predict early lung cancer. The Multi-channel Convolutional Neural Network achieved a 64% F1-score, improving early detection potential.
Area of Science:
- Oncology
- Medical Informatics
- Artificial Intelligence
Background:
- Lung cancer is a leading cause of cancer death, often diagnosed late.
- Current screening methods have limitations in cost, access, and accuracy.
- Early diagnosis is crucial for improving lung cancer patient survival rates.
Purpose of the Study:
- To develop a predictive model for early lung cancer detection using comprehensive clinical data.
- To leverage Taiwan's National Health Insurance Research Database (NHIRD) for model development.
- To evaluate the efficacy of an AI-driven approach for early lung cancer identification.
Main Methods:
- Utilized a Multi-channel Convolutional Neural Network (MC-CNN) architecture.
- Integrated multidimensional clinical data from the past three years, including diagnostics, medications, and lab results.
- Analyzed temporal and contextual patterns across diverse data modalities for prediction.
Main Results:
- The MC-CNN model achieved a F1-score of 64%.
- The proposed AI model demonstrated superior performance compared to several benchmark methods.
- Successfully integrated diverse clinical data for enhanced predictive accuracy.
Conclusions:
- AI-driven approaches show significant potential for facilitating early lung cancer detection.
- Integrating multidimensional clinical data enhances the predictive capabilities for lung cancer.
- This study highlights the practical application of AI in addressing critical healthcare challenges like early cancer diagnosis.
