AIM:　This　study　aimed　to　develop　highly　precise　radiomics　and　deep　learning　models　to　accurately　detect　acute　lymphoblastic　leukemia　(ALL)　using　a　T1WI　image.　MATERIALS　AND　METHODS:　A　total　of　604　brain　magnetic　resonance　data　of　ALL　group　and　normal　children　(NC)　group.　Two　radiologists　independently　retrieved　radiomics　features　after　manually　delineating　the　area　of　interest　along　the　clivus　at　the　median　sagittal　position　of　T1WI.　According　to　the　9:1　ratio,　all　samples　were　randomly　divided　into　the　training　cohort　and　the　testing　cohort.　support　vector　machine　was　then　used　to　classify　the　radiomics　model　using　the　features　that　had　a　correlation　coef　ficient　of　greater　than　0.99　in　the　training　cohort.　The　Ef　ficientnet-B3　network　model　received　the　training　set　images　to　create　a　deep　learning　model.　The　sensitivity,　speci　ficity,　and　area　under　the　ROC　curve　were　calculated　in　order　to　evaluate　the　diagnostic　ef　ficacy　of　the　different　models　after　the　validation　of　two　aforementioned　models　in　the　testing　cohort.　RESULTS:　The　deep　learning　model　had　a　higher　AUC　value　of　0.981　than　the　radiomics　model　＇s　value　of　0.962　in　the　testing　cohort.　Delong　＇s　test　showed　no　statistical　difference　between　the　two　models　(　P　＞0.05).　The　accuracy/sensitivity/speci　ficity/negative　predictive　value/positive　predictive　value　achieved　0.9180/0.9565/0.8947/0.9714/0.8462　for　the　radiomics　model　and　0.9344/0.8696/0.9737/0.9250/0.9524　for　deep　learning　model.　CONCLUSIONS:　The　deep　learning　and　radiomics　models　showed　high　AUC　values　in　the　training　and　test　cohorts.　They　also　exhibited　good　diagnostic　ef　ficacy　for　predicting　ALL.　(c)　2024　The　Royal　College　of　Radiologists.　Published　by　Elsevier　Ltd.　All　rights　reserved.