Objective To develop a machine learning model for predicting lymph node status of extrahepatic cholangiocarcinoma. Methods The multi-sequence MRI images of 101 patients with extrahepatic cholangiocarcinoma were retrospectively studied

which had underwent radical resection. MaZda software was used to delineate the lesions and extract the image texture features. The radiomics features were normalised using a max-min algorithm

and a synthetic minority oversampling algorithm was balanced the classification samples to generate new samples. The Spearman correlation analysis and the maximum correlation and minimum redundancy feature selection method were used to select the image features

and 20 most representative features were finally obtained. We arbitrarily selected 80% of the samples from the new sample data as the training set and the remaining 20% as the test set. The SVM prediction model was built and the performance of this model was evaluated using the subject operating characteristic curve （ROC）. Results The AUC of the training group was 0.98

the accuracy was 89.2%

the sensitivity was 92.9%

and the specificity was 89.4%. The AUC of the test group was 0.83

the accuracy was 82.2%

the sensitivity was 82.1%

and the specificity was 80.9%. Conclusion The SVM prediction model based on MRI image had good prediction performance

which provided a personalized preoperative auxiliary diagnosis strategy for clinicians.