The invention discloses an early-stage
lung cancer prediction method based on a multi-
modal eccDNA marker, and relates to the technical field of
liquid biopsy, and the method comprises the following steps: collecting a
peripheral blood sample, carrying out differential
centrifugal separation on
plasma, constructing a cfDNA
library, and carrying out double-end sequencing to obtain original
sequencing data; extracting structural features through an eccDNA model, and inputting the structural features into a first
machine learning model to generate a
circular DNA score; extracting variation features through an SNV model, and inputting the variation features into a second
machine learning model to generate a spectrum feature
score; extracting copy number features through a CNV model, and inputting the copy number features into a third
machine learning model to generate copy number scores; carrying out probability distribution calibration on the
circular DNA score, the spectrum feature score and the copy number score; inputting the three types of molecular features into a deep neural network to generate a first fusion score; inputting the calibrated score into a
logistic regression model to generate a second fusion score; and generating a final
lung cancer risk probability according to the first fusion score and the second fusion score.