Objectives:　The　probability　of　Breast　Imaging　Reporting　and　Data　Systems　(BI-RADS)　4　lesions　being　malignant　is　2%-95%,　which　shows　the　difficulty　to　make　a　diagnosis.　Radiomics　models　based　on　magnetic　resonance　imaging　(MRI)　can　replace　clinicopathological　diagnosis　with　high　performance.　In　the　present　study,　we　developed　and　tested　a　radiomics　model　based　on　MRI　images　that　can　predict　the　malignancy　of　BI-RADS　4　breast　lesions.　Methods:　We　retrospective　enrolled　a　total　of　216　BI-RADS　4　patients　MRI　and　clinical　information.　We　extracted　3,474　radiomics　features　from　dynamic　contrast-enhanced　(DCE),　T-2-weighted　images　(T2WI),　and　diffusion-weighted　imaging　(DWI)　MRI　images.　Least　absolute　shrinkage　and　selection　operator　(LASSO)　and　logistic　regression　were　used　to　select　features　and　build　radiomics　models　based　on　different　sequence　combinations.　We　built　eight　radiomics　models　which　were　based　on　DCE,　DWI,　T2WI,　DCE+DWI,　DCE+T2WI,　DWI+T2WI,　and　DCE+DWI+T2WI　and　a　clinical　predictive　model　built　based　on　the　visual　assessment　of　radiologists.　A　nomogram　was　constructed　with　the　best　radiomics　signature　combined　with　patient　characteristics.　The　calibration　curves　for　the　radiomics　signature　and　nomogram　were　conducted,　combined　with　the　Hosmer-Lemeshow　test.　Results:　Pearson＇s　correlation　was　used　to　eliminate　3,329　irrelevant　features,　and　then　LASSO　and　logistic　regression　were　used　to　screen　the　remaining　feature　coefficients　for　each　model　we　built.　Finally,　12　related　features　were　obtained　in　the　model　which　had　the　best　performance.　These　12　features　were　used　to　build　a　radiomics　model　in　combination　with　the　actual　clinical　diagnosis　of　benign　or　malignant　lesion　labels　we　have　obtained.　The　best　model　built　by　12　features　from　the　3　sequences　has　an　AUC　value　of　0.939　(95%　CI,　0.884-0.994)　and　an　accuracy　of　0.931　in　the　testing　cohort.　The　sensitivity,　specificity,　precision　and　Matthews　correlation　coefficient　(MCC)　of　testing　cohort　are　0.932,　0.923,　0.982,　and　0.791,　respectively.　The　nomogram　has　also　been　verified　to　have　calibration　curves　with　good　overlap.　Conclusions:　Radiomics　is　beneficial　in　the　malignancy　prediction　of　BI-RADS　4　breast　lesions.　The　radiomics　predictive　model　built　by　the　combination　of　DCE,　DWI,　and　T2WI　sequences　has　great　application　potential.