A Timeseries-based Multimodal Deep Learning Approach for Lung Nodule Growth Prediction.
Authors
Affiliations (8)
Affiliations (8)
- Department of Information Management, Yuan Ze University, Taoyuan, 320, Taiwan.
- Division of Cardiology, Far Eastern Memorial Hospital, New Taipei, 220, Taiwan.
- Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan, 320, Taiwan.
- Department of Radiology, Far Eastern Memorial Hospital, New Taipei, 220, Taiwan.
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 106, Taiwan.
- Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, 100, Taiwan.
- Department of Information Management, Yuan Ze University, Taoyuan, 320, Taiwan. [email protected].
- Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, 320, Taiwan. [email protected].
Abstract
Lung nodules, while often benign, can become significant health concerns if their growth is not monitored accurately. Predicting lung nodule growth is critical for improving patient outcomes and guiding clinical decision-making. This study aims to develop a Multimodal Deep Learning Approach to enhance the accuracy of lung nodule growth prediction by integrating time-series CT image data with demographics and nodule-specific features. Data were collected from the Far Eastern Memorial Hospital, Taiwan, including CT image sequences of lung nodules and patient demographics and nodule-specific features. Using this dataset, a Multimodal Deep Learning framework was developed and optimized. The model's performance was assessed using metrics such as Accuracy, Precision, Sensitivity, F1-score, and AUC. The proposed Multimodal Deep Learning framework substantially outperformed traditional machine learning and unimodal models. Among all configurations, the repeat frame strategy achieved the best overall performance, with an accuracy of 0.929, precision of 0.878, sensitivity of 0.908, F1-score of 0.878, and AUC of 0.977. Paired t-test analysis confirmed that these improvements were statistically significant (p < 0.05) compared to other multimodal variants and baseline models. These results highlight the model's ability to effectively integrate image, demographics, and nodule-specific features, leading to superior predictive accuracy and robust clinical decision-support potential. By using the time-series of CT image data, along with demographics and nodule-specific features, the proposed Multimodal Deep Learning provides a reliable tool for predicting lung nodule growth. This advancement has significant implications for lung nodule management, offering clinicians a robust and dependable resource to support medical decision-making and improve patient care. The findings highlight the transformative potential of deep learning techniques in critical healthcare domains.