Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning.

ABSTRACT

OBJECTIVE: To construct and validate a multi-dimensional model based on multiple machine leaning algorithms to predict PCLM using multi-parameter magnetic resonance (MRI) sequences with clinical and imaging parameters. METHODS: A total of 148 PDAC retrospectively examined patients were classified as metastatic or non-metastatic based on results at 3 months after surgery. The radiomics features of the primary tumor were extracted from T2WI images, followed by dimension reduction. Then, multiple machine learning methods were used to construct models. Independent predictors were also screened using multifactor logistic regression and a nomogram was constructed in combination with the radiomics model. Area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the accuracy and reliability of the nomogram. RESULTS: The diagnostic efficacy of the radiomics model in the training and test set was 0.822 and 0.803, sensitivity was 0.742 and 0.692, and specificity was 0.792 and 0.875, respectively. The diagnostic efficacy of the nomogram in the training and test set was 0.866 and 0.832. CONCLUSION: A radiomics nomogram based on machine learning improved the accuracy of predicting PCLM and may be useful for early preoperative diagnosis.