International Journal of Clinical Research

Objective Exploring the diagnostic value of dual energy CT imaging omics feature model for liver metastasis of colorectal cancer. Methods Select 160 colorectal cancer patients admitted to our hospital from January 2021 to December 2024, including 80 cases in the liver metastasis group and 80 cases in the non metastasis group. Obtain 100 kVp, Sn 140 kVp, fused images, and highest contrast to noise ratio (CNR) images through dual energy CT scanning, and compare them. Extract radiomics features and construct support vector machine (SVM), random forest (RF), and logistic regression (LR) models to evaluate the diagnostic performance of the models in the training set (70%) and validation set (30%). Results There was no statistically significant difference (P>0.05) in the CT values of synchronous liver metastases among the sequences of 100 kVp images, Sn 140 kVp images, fused images, and CNR images. There were statistically significant differences in image noise, CT differences of synchronous liver metastases, and CNR (all P<0.05). The SVM model performed the best in the validation set, with an accuracy of 0.944, AUC of 0.982, sensitivity of 0.929, and specificity of 0.958. Conclusion The SVM model constructed based on dual energy CT imaging omics features can significantly improve the diagnostic accuracy of colorectal cancer liver metastasis and has high application value.

Colorectal cancer; Liver metastasis; Dual energy CT; Imaging omics feature model; Diagnostic value