Objective　The　preoperative　prediction　of　the　overall　survival　(OS)　status　of　patients　with　head　and　neck　cancer　(HNC)　is　significant　value　for　their　individualized　treatment　and　prognosis.　This　study　aims　to　evaluate　the　impact　of　adding　3D　deep　learning　features　to　radiomics　models　for　predicting　5-year　OS　status.　Methods　Two　hundred　twenty　cases　from　The　Cancer　Imaging　Archive　public　dataset　were　included　in　this　study;　2212　radiomics　features　and　304　deep　features　were　extracted　from　each　case.　The　features　were　selected　by　univariate　analysis　and　the　least　absolute　shrinkage　and　selection　operator,　and　then　grouped　into　a　radiomics　model　containing　Positron　Emission　Tomography　/Computed　Tomography　(PET/CT)　radiomics　features　score,　a　deep　model　containing　deep　features　score,　and　a　combined　model　containing　PET/CT　radiomics　features　score　+3D　deep　features　score.　TumorStage　model　was　also　constructed　using　initial　patient　tumor　node　metastasis　stage　to　compare　the　performance　of　the　combined　model.　A　nomogram　was　constructed　to　analyze　the　influence　of　deep　features　on　the　performance　of　the　model.　The　10-fold　cross-validation　of　the　average　area　under　the　receiver　operating　characteristic　curve　and　calibration　curve　were　used　to　evaluate　performance,　and　Shapley　Additive　exPlanations　(SHAP)　was　developed　for　interpretation.　Results　The　TumorStage　model,　radiomics　model,　deep　model,　and　the　combined　model　achieved　areas　under　the　receiver　operating　characteristic　curve　of　0.604,　0.851,　0.840,　and　0.895　on　the　train　set　and　0.571,　0.849,　0.832,　and　0.900　on　the　test　set.　The　combined　model　showed　better　performance　of　predicting　the　5-year　OS　status　of　HNC　patients　than　the　radiomics　model　and　deep　model.　The　combined　model　was　shown　to　provide　a　favorable　fit　in　calibration　curves　and　be　clinically　useful　in　decision　curve　analysis.　SHAP　summary　plot　and　SHAP　The　SHAP　summary　plot　and　SHAP　force　plot　visually　interpreted　the　influence　of　deep　features　and　radiomics　features　on　the　model　results.　Conclusions　In　predicting　5-year　OS　status　in　patients　with　HNC,　3D　deep　features　could　provide　richer　features　for　combined　model,　which　showed　outperformance　compared　with　the　radiomics　model　and　deep　model.　©　Wolters　Kluwer　Health,　Inc.　All　rights　reserved.