Lung nodules carry a risk of cancer transformation，and the rapidly increasing detection rate has led to the emergence of social concerns related to overtreatment，resulting in physical，mental，and economic burdens.The “nodule-cancer transformation” in the lungs represents the local manifestation of the body's systemic “pre-post disease”，and the timing of this critical state is crucial for achieving precise diagnosis and treatment.Guided by the holistic view of traditional Chinese medicine(TCM)，this study aims to explore the macroscopic signs and microscopic biological basis changes in the lung's “nodule-cancer transformation” process.Based on the principle of critical slowing down，multidisciplinary approaches were combined，such as biology，mathematics，and artificial intelligence，to propose a “four diagnostic imaging macro-information-dynamic network biomarkers(DNB)-exhaled gas microecology” model for the macro-micro mapping of lung conditions.Furthermore，a deep learning algorithm framework based on the variational autoencoder(VAE)，generative adversarial network(GAN)，and vision transformer(ViT) model，centered around the theory of dynamical systems，was constructed.This framework aimed to unveil the fundamental，fitting，and clinical multivariate characterization system of the “nodule-cancer transformation”，ultimately facilitating precise timing judgment for this critical state and providing early warning for lung cancer.