The present application provides a
lung cancer type prediction method and
system based on a fusion
deep learning network. It dynamically segments CT images of
lung cancer patients, extracts tumor vascular distribution data and calculates three-dimensional density parameters and vascular rupture direction parameters, and simultaneously obtains a necrotic area volume
ratio sequence; separates circulating
tumor cells through spiral sorting of blood samples and
fluid control, and generates stiffness change parameters and deformation
recovery parameters derived from deformation trajectories; combines three-dimensional density, vascular rupture direction and stiffness parameters to extract the vascular rupture direction
time series offset, and dynamically and synchronously matches the stiffness change response to generate a synchronization set; constructs a
lag correlation between the vascular rupture direction offset and the deformation
recovery period, and determines the weight matching through cross-
modal analysis; combines the spatiotemporal characteristics of the necrotic area volume to output the
lung cancer subtype classification results. The present application integrates a cross-
modal correlation model of vascular dynamic offset and
cell mechanical response to achieve accurate discrimination of
lung cancer subtypes.