PurposeTo develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC).Materials and methodsThis observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n= 112, single-energy CT, SECT), a test set (n= 29, single-energy CT, SECT) and a validation cohort (n= 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined.ResultsRS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test,p= 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram.ConclusionCT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization.Critical relevance statementRadiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s13244-023-01459-w.