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

Figure 1 Patient inclusion and prediction model construction process. MLM: Metachronous liver metastasis.

Figure 2 Selection of metachronous liver metastasis candidate predictive variables based on least absolute shrinkage and selection operator regression. A: Spearman correlation analysis; B: Least absolute shrinkage and selection operator regression analysis. BMI: Body mass index; MTD: Maximum tumor diameter; MRF: Mesenteric fascia; AFP: Alpha fetoprotein; CA: Carbohydrate antigen; CEA: Carcinoembryonic antigen.

Figure 3 Nomogram visual prediction model for predicting metachronous liver metastasis. A: Nomogram; B: Calibration curve. CEA: Carcinoembryonic antigen.

Figure 4 Random forest prediction model for predicting metachronous liver metastasis. A: The random forest prediction model based on machine learning algorithms; B: Predictive performance detection of models. The red dots represent patients with metachronous liver metastasis, and the blue dots represent patients without metachronous liver metastasis.

Figure 5 Performance evaluation of predictive models based on decision curve analysis. A: Training cohort; B: Testing cohort. RFM: Random forest model; GLRM: Generalized linear regression model.