Objective:　The　aim　of　the　work　described　here　was　to　develop　a　non-invasive　tool　based　on　the　radiomics　and　ultra-sound　features　of　automated　breast　volume　scanning　(ABVS),　clinicopathological　factors　and　serological　indicators　to　evaluate　axillary　lymph　node　metastasis　(ALNM)　in　patients　with　early　invasive　breast　cancer　(EIBC).Methods:　We　retrospectively　analyzed　179　ABVS　images　of　patients　with　EIBC　at　a　single　center　from　January　2016　to　April　2022　and　divided　the　patients　into　training　and　validation　sets　(ratio　8:2).　Additionally,　97　ABVS　images　of　patients　with　EIBC　from　a　second　center　were　enrolled　as　the　test　set.　The　radiomics　signature　was　established　with　the　least　absolute　shrinkage　and　selection　operator.　Significant　ALNM　predictors　were　screened　using　univariate　logistic　regression　analysis　and　further　combined　to　construct　a　nomogram　using　the　multivariate　logistic　regression　model.　The　receiver　operating　characteristic　curve　assessed　the　nomogram＇s　predictive　performance.Discussion:　The　constructed　radiomics　nomogram　model,　including　ABVS　radiomics　signature,　ultrasound　assess-ment　of　axillary　lymph　node　(ALN)　status,　convergence　sign　and　erythrocyte　distribution　width　(standard　devia-tion),　achieved　moderate　predictive　performance　for　risk　probability　evaluation　of　ALNs　in　patients　with　EIBC.　Compared　with　ultrasound,　the　nomogram　model　was　able　to　provide　a　risk　probability　evaluation　tool　not　only　for　the　ALNs　with　positive　ultrasound　features　but　also　for　micrometastatic　ALNs　(generally　without　positive　ultra-sound　features),　which　benefited　from　the　radiomics　analysis　of　multi-sourced　data　of　patients　with　EIBC.Conclusion:　This　ABVS-based　radiomics　nomogram　model　is　a　pre-operative,　non-invasive　and　visualized　tool　that　can　help　clinicians　choose　rational　diagnostic　and　therapeutic　protocols　for　ALNM.