Development of a Novel Combined Nomogram Model Integrating Deep Learning-Pathomics, Radiomics, and TLS Score to Predict the Risk of Liver Metastasis in Colorectal Cancer Patients

This study retrospectively analyzed 167 colorectal cancer patients from November 2013 to February 2021 in Zhongda Hospital of Southeast University, divided into liver metastasis group (37 cases) and no liver metastasis group (130 cases), and the data were divided into training set and test set according to 7:3. The study constructed traditional clinical, imaging histology, pathohistology, TLS score and multimodal integration models, and screened features by LASSO regression. The results showed that the multimodal integration model had an AUC of 0.94, an accuracy of 0.927, and precision and F1 scores of 1.000 and 0.800, respectively, which were superior to other models, with high predictive value and diagnostic efficacy, and its column-line diagram could be used in the clinic.