Objective:To create radiomics models based on abbreviated multimodal magnetic resonance imaging (MRI) for the diagnosis of breast cancer.Methods:All breast MR imaging data between Jun 2014 and Mar 2019 were retrospectively collected. Patients with pathological results of puncture or surgical resection were involved in this study. One thousand three hundred and six patients (416 benign and 890 breast cancer) were divided into training cohort ( n=702), internal validation cohort ( n=302), and external validation cohort ( n=302). All images were reduced to: the joint model group [including T2 weighted imaging (T2WI), DWI (diffusion-weighted imaging) and first contrast-enhanced sequences], non-enhanced group (T2WI and DWI) and single-phase enhanced group (first contrast-enhanced sequences). Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimension of texture features. Three supervised machine learning algorithms (Bagging decision tree, Gaussian process, support vector machine) were used to predict benign and malignant breast lesions, and the best classifier was selected to construct breast cancer diagnosis model. Models were validated by internal and external validation cohorts. Results:The Gaussian process algorithm was chosen. The area under the curve (AUC) of the joint model and the non-enhanced model for predicting breast cancer were 0.903 and 0.893 for the training cohort, 0.893 and 0.863 for the internal validation cohort, and 0.878 and 0.864 for the external validation cohort.Conclusions:The radiomics model based on abbreviated multimodal MRI can accurately diagnose breast cancer. And the non-enhanced model can accurately diagnose breast cancer without contrast enhancement, which provides feasibility for simplifying the diagnosis process.