The invention discloses a
hepatocellular carcinoma postoperative early
recurrence prediction method based on multi-
modal fusion. The method comprises the following steps: firstly, integrating clinical data of a
training set, a preoperative
enhanced CT image and a postoperative full-view digital
pathological image, and carrying out standardized correction; then, traditional image
omics features and
deep learning features are extracted from the CT image,
cell nucleus morphological features and
tumor microenvironment spatial configuration features are extracted from the
pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy
algorithm (mRMR) and
LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost
algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a
pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-
CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal
cross validation, foresight and external independent validation, risk layering is carried out based on the
prediction probability, and individualized postoperative management is guided.