Objectives　To　develop　and　test　an　optimized　radiomics　model　based　on　multi-planar　automated　breast　volume　scan　(ABVS)　images　to　identify　malignant　and　benign　breast　lesions.　Methods　Patients　(n　=　200)　with　breast　lesions　who　underwent　ABVS　examinations　were　included.　For　each　patient,　208　radiomics　features　were　extracted　from　the　ABVS　images,　including　axial　plane　and　coronal　plane.　Recursive　feature　elimination,　random　forest,　and　chi-square　test　were　used　to　select　features.　A　support　vector　machine,　logistic　regression,　and　extreme　gradient　boosting　were　utilized　as　classifiers　to　differentiate　malignant　and　benign　breast　lesions.　The　area　under　the　curve,　sensitivity,　specificity,　accuracy,　and　precision　was　used　to　evaluate　the　performance　of　the　radiomics　models.　Generalization　of　the　radiomics　models　was　verified　through　5-fold　cross-validation.　Results　For　a　single　plane　or　a　combination　of　planes,　a　combination　of　recursive　feature　elimination,　and　support　vector　machine　yielded　the　best　performance　when　identifying　breast　lesions.　The　machine　learning　models　based　on　a　combination　of　planes　performed　better　than　those　based　on　a　single　plane.　Regarding　the　axial　plane　and　coronal　plane,　the　machine　learning　model　using　a　combination　of　recursive　feature　elimination　and　support　vector　machine　yielded　the　optimal　identification　performance:　average　area　under　the　curve　(0.857　+/-　0.058,　95%　confidence　interval,　0.763-0.957);　the　average　values　of　sensitivity,　specificity,　accuracy,　and　precision　were　87.9,　68.2,　80.7,　and　82.9%,　respectively.　Conclusions　The　optimized　radiomics　model　based　on　ABVS　images　can　provide　valuable　information　for　identifying　benign　and　malignant　breast　lesions　preoperatively　and　guide　the　accurate　clinical　treatment.　Further　external　validation　is　required.