Pulmonary nodule growth prediction with anisotropic reaction-diffusion.
Authors
Affiliations (4)
Affiliations (4)
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
- School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: [email protected].
- First Affiliated Hospital of Jinan University, Guangzhou 510632, China.
- Guangdong Second Provincial General Hospital and Jinan University, Guangzhou 510317, China.
Abstract
Accurate prediction of pulmonary nodule growth is critical for early malignancy assessment and timely lung cancer diagnosis. However, pulmonary nodule growth is a complex biological process influenced by factors such as cellular proliferation, nutrient diffusion, and tissue microenvironment, which are usually nodule-specific and overlooked by traditional prediction models. Specifically, we leverage a reaction-diffusion system to exploit properties of a nodule and its surrounding parenchyma for predicting its growth trend, and implement the system using a convolutional operation. To achieve specific information about nodules, we employ a vision transformer to estimate the parameters of the reaction-diffusion system based on consecutive computed tomography scans of a nodule. By doing this, we integrate the reaction-diffusion mathematical modeling with deep neural networks to accurately predict future morphology of pulmonary nodules. We conduct experiments on the benchmark dataset from the National Lung Screening Trial (NLST) to demonstrate the effectiveness of our method. In addition, we also evaluate our method on an in-house dataset to validate its generalization ability and practicality. In particular, RD-ViT reduces volume and mass growth-prediction errors by approximately 50%-80%. Our method extracts nodule-specific information to accurately forecast future morphologies, validated on benchmark and in-house datasets. It offers a promising tool for personalized lung nodule management, enabling optimized surveillance and enhanced early detection of malignant transformation.