Endoscopic Rectal Ultrasound‐Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal Cancer

Abstract

ObjectivesTo develop and validate an ultrasound‐based radiomics model to predict synchronous liver metastases (SLM) in rectal cancer (RC) patients preoperatively.MethodsTwo hundred and thirty‐nine RC patients were included in this study and randomly divided into training and validation cohorts. A total of 5936 radiomics features were calculated on the basis of ultrasound images to build a radiomic model and obtain a radiomics score (Rad‐score) using logistic regression. Meanwhile, clinical characteristics were collected to construct a clinical model. The radiomics–clinical model was developed and validated by integrating the radiomics features with the selected clinical characteristics. The performances of three models were evaluated and compared through their discrimination, calibration, and clinical usefulness.ResultsThe radiomics model was developed based on 13 radiomic features. The radiomics–clinical model, which incorporated Rad‐score, CEA, and CA199, exhibited favorable discrimination and calibration with areas under the receiver operating characteristic curve (AUC) of 0.920 (95% CI: 0.874–0.965) in the training cohorts and 0.855 (95% CI: 0.759–0.951) in the validation cohorts. And the AUC of the radiomics–clinical model was 0.849 (95% CI: 0.771–0.927) for the training cohorts and 0.780 (95% CI: 0.655–0.905) for the validation cohorts, the clinical model was 0.811 (95% CI: 0.718–0.905) for the training cohorts and 0.805 (95% CI: 0.645–0.965) for the validation cohorts. Moreover, decision curve analysis (DCA) further confirmed the clinical utility of the radiomics–clinical model.ConclusionsThe radiomics–clinical model performed satisfactory predictive performance, which can help improve clinical diagnosis performance and outcome prediction for SLM in RC patients.