Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis

BACKGROUND: The liver, as the main target organ for hematogenous metastasis of colorectal cancer, early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients. Herein, this study aims to investigate the application value of a combined machine learning (ML) based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis (MLM). AIM: To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer. METHODS: We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023. All participants were randomly assigned to the training or validation queue in a 7:3 ratio. We first apply generalized linear regression model (GLRM) and random forest model (RFM) algorithm to construct an MLM prediction model in the training queue, and evaluate the discriminative power of the MLM prediction model using area under curve (AUC) and decision curve analysis (DCA). Then, the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups. RESULTS: Among the 301 patients included in the study, 16.28% were ultimately diagnosed with MLM through pathological examination. Multivariate analysis showed that carcinoembryonic antigen, and magnetic resonance imaging radiomics were independent predictors of MLM. Then, the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation. The prediction performance of GLRM in the training and validation queue was 0.765 [95% confidence interval (CI): 0.710-0.820] and 0.767 (95%CI: 0.712-0.822), respectively. Compared with GLRM, RFM achieved superior performance with AUC of 0.919 (95%CI: 0.868-0.970) and 0.901 (95%CI: 0.850-0.952) in the training and validation queue, respectively. The DCA indicated that the predictive ability and net profit of clinical RFM were improved. CONCLUSION: By combining multiparameter magnetic resonance imaging with the effectiveness and robustness of ML-based predictive models, the proposed clinical RFM can serve as an insight tool for preoperative assessment of MLM risk stratification and provide important information for individual diagnosis and treatment of rectal cancer patients.