Background <p>This study aimed to preoperatively predict Ki-67 proliferation levels in patients with pancreatic solid pseudopapillary neoplasm (pSPN) using radiomics features extracted from arterial phase helical CT images.</p> Methods <p>We retrospectively analyzed 92 patients (Ningbo Medical Center Lihuili Hospital: <i>n</i> = 64, Taizhou Central Hospital: <i>n</i> = 28) with pathologically confirmed pSPN from June 2015 to June 2023. Ki-67 positivity &gt; 3% was considered high. Radiomics features were extracted using PyRadiomics, with patients from training cohort (<i>n</i> = 64) and validation cohort (<i>n</i> = 28). A radiomics signature was constructed, and a CT radiomics score (CTscore) was calculated. Deep learning models were employed for prediction, with early stopping to prevent overfitting.</p> Results <p>Seven key radiomics features were selected via LASSO regression with cross-validation. The deep learning model demonstrated improved accuracy with demographics and CTscore, with key features such as Morphology and CTscore contributing significantly to predictive accuracy. The best-performing models, including GBM and deep learning algorithms, achieved high predictive performance with an AUC of up to 0.946 in the training cohort.</p> Conclusions <p>We developed a robust deep learning-based radiomics model using arterial phase CT images to predict Ki-67 levels in pSPN patients, identifying CTscore and Morphology as key predictors. This non-invasive approach has potential utility in guiding personalized preoperative treatment strategies.</p> Clinical trial number <p>Not applicable.</p>