Pulmonary nodule growth prediction with anisotropic reaction-diffusion.

BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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%. CONCLUSIONS: 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.