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

Core Tip

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.