[en] To develop a classification system using imaging features to interpret breast non-mass lesions (NMLs) detected on US and to stratify their cancer risk. This retrospective study included 715 patients with 715 breast NMLs detected on breast US from 2012 to 2016. Each patient underwent mammography at the time of diagnosis. Radiologists assessed US and mammographic features and final BI-RADS categories. Multivariable logistic regression was used to find imaging features associated with malignancy in a development dataset (n = 460). A system to classify BI-RADS categories (3 to 5) was developed based on the odds ratios (ORs) of imaging features significantly associated with malignancy and validated in a distinct validation dataset (n = 255). Among 715 NMLs, 385 (53.8%) were benign and 330 (46.2%) were malignant. In the development dataset, the following B-mode US features were associated with malignancy (all p < 0.001): segmental distribution (OR = 3.03; 95% confidence interval [CI], 1.50–6.15), associated calcifications (OR = 4.26; 95% CI, 1.62–11.18), abnormal ductal change (OR = 4.91; 95% CI, 2.07–11.68), and posterior shadowing (OR = 20.20; 95% CI, 6.46–63.23). The following mammographic features were also associated with malignancy (all p < 0.001): calcifications (OR = 7.98; 95% CI, 3.06–20.81) and focal asymmetry (OR = 4.75; 95% CI, 1.90–11.88). In the validation dataset, our classification system using US and mammography showed a higher area under the curve (0.951–0.956) compared to when it was not applied (0.908–0911) to predict malignancy with BI-RADS categories (p < 0.05). Our classification system which incorporates US and mammographic features of breast NMLs can help interpret and manage all NMLs detected on breast US by stratifying cancer risk according to BI-RADS categories.