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Synchronous liver metastasis (SLM) remains a major challenge for rectal cancer. Early detection of SLM is a key factor to improve the survival rate of rectal cancer. In this radiomics study, we predicted the SLM based on the radiomics of primary rectal cancer. A total of 328 radiomics features were extracted from the T2WI images of 194 patients. The least absolute shrinkage and selection operator (LASSO) regression was used to reduce the feature dimension and to construct the radiomics signature. after LASSO, principal component analysis (PCA) was used to sort the features of the surplus characteristics, and selected the features of the total contribution of 85%. Then the prediction model was built by linear regression, and the decision curve analysis was used to judge the net benefit of LASSO and PCA. In addition, we used two independent cohorts for training (n = 135) and validation (n = 159). We found that the model based on LASSO dimensionality construction had the maximum net benefit (in the training set (AUC [95% confidence interval], 0.857 [0.787–0.912]) and in the validation set (0.834 [0.714–0.918]). The radiomics nomogram combined with clinical risk factors and LASSO features showed a good predictive performance in the training set (0.921 [0.862–0.961]) and validation set (0.912 [0.809–0.97]). Our study indicated that radiomics based on primary rectal cancer could provide a non-invasive way to predict the risk of SLM in clinical practice.

MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases