Radiomics and machine learning for predicting metachronous liver metastasis in rectal cancer

Abstract

A recent study by Long et al used a predictive model to explore the efficacy of radiomics based on multiparametric magnetic resonance imaging in predicting metachronous liver metastasis (MLM) in newly diagnosed rectal cancer (RC) patients. The machine learning algorithms, particularly the random forest model (RFM), appeared well-matched to the complex nature of radiomics data. The predictive capabilities of the RFM, as evidenced by the area under the curve of 0.919 in the training cohort and 0.901 in the validation cohort, highlighted its potential clinical utility. However, we highlighted several methodological limitations, including excluding genomic markers, potential biases from the retrospective design, limited generalizability due to a single-center study, and variability in image interpretation. We propose further investigation into integrating multi-omic data, conducting larger multicenter studies, and utilizing advanced imaging techniques. Additionally, we highlighted the importance of interdisciplinary collaboration to improve predictive model development and advocate for cost-effectiveness analyses to facilitate clinical integration. Overall, this predictive model may improve the early detection and management of MLM in RC patients, with promising avenues for future exploration. Ongoing research in this domain can potentially improve clinical outcomes and the quality of care for RC patients.

Keywords: Rectal cancer; Liver metastases; Neoplasm; Metastasis; Machine learning; Magnetic resonance imaging; Radiomics

Core Tip: In a recent study by Long et al, multiparametric magnetic resonance imaging and radiomics were utilized to anticipate the occurrence of metachronous liver metastasis in individuals newly diagnosed with rectal cancer. The random forest model, a predictive model component, demonstrated significant accuracy, achieving area under the curve values of 0.919 in the training cohort and 0.901 in the validation cohort, highlighting its potential for non-invasive risk assessment. By integrating radiomic features with clinical data, the model can support tailored treatment strategies and improve patient care. Nevertheless, it is important for future research to address methodological limitations, such as the exclusion of genomic markers, potential biases from the retrospective design, and the necessity for external validation across varied patient populations. Expanding the model to integrate multi-omic data and advanced imaging techniques has the potential to further its clinical significance and practicality.