Objectives:　Patients　with　T4　obstructive　colorectal　cancer　(OCC)　have　a　high　mortality　rate.　Therefore,　an　accurate　distinction　between　T4　and　T1–T3　(NT4)　in　OCC　is　an　important　part　of　preoperative　evaluation,　especially　in　the　emergency　setting.　This　paper　introduces　three　models　of　radiomics,　deep　learning,　and　deep　learning-based　radiomics　to　identify　T4　OCC.　Methods:　We　established　a　dataset　of　computed　tomography　(CT)　images　of　164　patients　with　pathologically　confirmed　OCC,　from　which　2537　slides　were　extracted.　First,　since　T4　tumors　penetrate　the　bowel　wall　and　involve　adjacent　organs,　we　explored　whether　the　peritumoral　region　contributes　to　the　assessment　of　T4　OCC.　Furthermore,　we　visualized　the　radiomics　and　deep　learning　features　using　the　t-distributed　stochastic　neighbor　embedding　technique　(t-SNE).　Finally,　we　built　a　merged　model　by　fusing　radiomic　features　with　deep　learning　features.　In　this　experiment,　the　performance　of　each　model　was　evaluated　by　the　area　under　the　receiver　operating　characteristic　curve　(AUC).　Results:　In　the　test　cohort,　the　AUC　values　predicted　by　the　radiomics　model　in　the　dilated　region　of　interest　(dROI)　was　0.770.　And　the　AUC　value　of　the　deep　learning　model　with　the　patches　extended　20-pixel　reached　0.936.　Combining　the　characteristics　of　radiomics　and　deep　learning,　our　method　achieved　an　AUC　value　of　0.947　in　the　T4　and　non-T4　(NT4)　classification,　and　increased　the　AUC　value　to　0.950　after　the　addition　of　clinical　features.　Conclusion:　The　prediction　results　of　our　merged　model　of　deep　learning　radiomics　outperformed　the　deep　learning　model　and　significantly　outperformed　the　radiomics　model.　The　experimental　results　demonstrate　that　combining　the　peritumoral　region　improves　the　prediction　performance　of　the　radiomics　model　and　the　deep　learning　model.　Graphical　abstract:　[Figure　not　available:　see　fulltext.].　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.