ArticleAnnals of surgical oncology2026
Automated Radiomics Model for Preoperative Pancreatic Neuroendocrine Tumor Grade Prediction.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPancreatic neuroendocrine tumor (PNET) behavior depends on tumor grade and genomics. Radiomics may identify these factors noninvasively. This study developed an automated pipeline from segmentation to radiomics modeling to preoperatively predict tumor grade. PATIENTS AND
methodsPatients resected from 2003 to 2021 with adequate preoperative arterial phase computed tomography (CT) scans were divided into training and test cohorts. The training cohort underwent manual pancreas and tumor region segmentation to train an auto-segmentation model; radiomic features extracted from tumor regions were used to develop a radiomics model for grade prediction (I versus II/III), which was evaluated in the automatically segmented test cohort. Associations between radiomic and genomic features were assessed.
resultsIn total, 182 patients were divided into training (n = 140) and test (n = 42) cohorts. Grade I and II/III lesions were in 113 (62%) and 69 (38%) patients, respectively. Median tumor size was 24 mm (6, 200). The auto-segmentation model segmented tumor regions in 90% (38) of the test cohort. In this group (n = 38), the radiomics model produced receiver operating characteristic curve (AUC) of 0.85 (0.73, 0.96). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.88 (0.78,0.98), 0.62 (0.46, 0.77), 0.65 (0.50, 0.80), and 0.87 (0.76,0.97), respectively. DAXX loss was associated with three radiomic features, and ATRX loss with one. In < 2 cm lesions, auto-segmentation was successful in 75% (9/12) of the test cohort, with accurate grade prediction in 67% (6/9) of cases.
conclusionsThe auto-segmentation model correctly identified tumor regions, and the radiomics model accurately predicted grade; new associations between genomic and radiomic features were identified. This automated pipeline can incorporate a radiomics model into preoperative PNET decision-making.
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