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

Abstract

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.

Keywords: Rectal cancer; Metachronous liver metastases; Magnetic resonance imaging; Radiomics; Machine learning

Core Tip: In recent years, with the rapid development of data and information technology, imaging omics has been gradually applied in the clinical diagnosis and treatment of tumors, as it can non-invasive extract high-throughput heterogeneity information within tumors and integrate patient clinical information to improve the accuracy of models. Up to now, imaging omics models based on computed tomography or magnetic resonance imaging (MRI) images have shown potential application value in preoperative T and N staging and efficacy evaluation of rectal cancer. However, there is currently very little imaging omics research based on MRI of primary rectal cancer tumors. In fact, MRI is the most accurate imaging method for diagnosing rectal cancer, which can better display the invasion of adjacent lymph nodes, blood vessels, or surrounding organs by primary rectal cancer tumors. In view of this, this study attempts to establish a non-invasive preoperative prediction model for metachronous liver metastasis in rectal cancer based on the imaging omics features of the initial diagnosis MRI images of rectal cancer, combined with machine learning algorithms, and verify its effectiveness. This will provide clinical assistance for clinicians to make personalized monitoring and treatment decision.