ArticleEuropean radiology2024
Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Deep learning automatic segmentation and radiomics model for diagnosing pancreatic solid neoplasms in MRI.BMC cancer · 2025Article
- Clinical value of the nomogram model based on endoscopic ultrasonography radiomics and clinical indicators in identifying benign and malignant lesions of the pancreas.Frontiers in oncology · 2025Article
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6 authors.
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Abstract
objectivesTo assess MR-based radiomic analysis in preoperatively discriminating small (< 2 cm) pancreatic ductal adenocarcinomas (PDACs) from neuroendocrine tumors (PNETs).
methodsA total of 197 patients (146 in the training cohort, 51 in the validation cohort) from two centers were retrospectively collected. A total of 7338 radiomics features were extracted from T2-weighted, diffusion-weighted, T1-weighted, arterial phase, portal venous phase and delayed phase imaging. The optimal features were selected by the Mann-Whitney U test, Spearman's rank correlation test and least absolute shrinkage and selection operator method and used to construct the radiomic score (Rad-score). Conventional radiological and clinical features were also assessed. Multivariable logistic regression was used to construct a radiological model, a radiomic model and a fusion model.
resultsNine optimal features were identified and used to build the Rad-score. The radiomic model based on the Rad-score achieved satisfactory results with AUCs of 0.905 and 0.930, sensitivities of 0.780 and 0.800, specificities of 0.906 and 0.952 and accuracies of 0.836 and 0.863 for the training and validation cohorts, respectively. The fusion model, incorporating CA19-9, tumor margins, pancreatic duct dilatation and the Rad-score, exhibited the best performance with AUCs of 0.977 and 0.941, sensitivities of 0.914 and 0.852, specificities of 0.954 and 0.950, and accuracies of 0.932 and 0.894 for the training and validation cohorts, respectively.
conclusionsThe MR-based Rad-score is a novel image biomarker for discriminating small PDACs from PNETs. A fusion model combining radiomic, radiological and clinical features performed very well in differentially diagnosing these two tumors. CLINICAL RELEVANCE STATEMENT: A fusion model combining MR-based radiomic, radiological, and clinical features could help differentiate between small pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors. KEY POINTS: Preoperatively differentiating small pancreatic ductal adenocarcinomas (PDACs) and pancreatic neuroendocrine tumors (PNETs) is challenging. Multiparametric MRI-based Rad-score can be used for discriminating small PDACs from PNETs. A fusion model incorporating radiomic, radiological, and clinical features differentiated small PDACs from PNETs well.
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