Radiomics in differential diagnosis of pancreatic tumors
The aim of this study was to assess whether radiomics could predict histotype of pancreatic ductal adenocarcinomas (PDAC) and pancreatic neuroendocrine tumors (PNET). Contrast-enhanced CT scans of 193 patients were retrospectively reviewed, encompassing 97 PDACs and 96 PNETs. Additionally, anamnesti...
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Elsevier
2025-06-01
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| Series: | European Journal of Radiology Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047725000188 |
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| author | Riccardo De Robertis Beatrice Mascarin Eda Bardhi Flavio Spoto Nicolò Cardobi Mirko D’Onofrio |
| author_facet | Riccardo De Robertis Beatrice Mascarin Eda Bardhi Flavio Spoto Nicolò Cardobi Mirko D’Onofrio |
| author_sort | Riccardo De Robertis |
| collection | DOAJ |
| description | The aim of this study was to assess whether radiomics could predict histotype of pancreatic ductal adenocarcinomas (PDAC) and pancreatic neuroendocrine tumors (PNET). Contrast-enhanced CT scans of 193 patients were retrospectively reviewed, encompassing 97 PDACs and 96 PNETs. Additionally, anamnestic data and laboratory data were evaluated. A total of 107 features were extracted for both the arterial and venous phases. ROC curves were constructed for the parameters with the highest AUC, considering two groups: one including all lesions and the other including only lesions smaller than 5 cm. The following feature differences were found to be statistically significant (p < 0.05). Without considering lesion size: for the arterial phase, 16 first-order and 38 s-order features; for the venous phase, 10 first-order and 20 s-order features. When considering lesion size: for the arterial phase, 16 first-order and 52 s-order features; for the venous phase, 11 first-order and 36 s-order features. The radiomics features with the highest AUC values included ART_firstorder_RootMeanSquared (AUC = 0.896, p < 0.01) in the arterial phase and VEN_firstorder_Median (AUC = 0.737, p < 0.05) in the venous phase for all lesions, and ART_firstorder_RootMeanSquared (AUC = 0.859, p < 0.01) and VEN_firstorder_Median (AUC = 0.713, p < 0.05) for lesions smaller than 5 cm. Texture analysis of pancreatic pathology has shown good predictability in defining the PNET histotype. This analysis potentially offering a non-invasive, imaging-based method to accurately differentiate between pancreatic tumor types. Such advancements could lead to more precise and personalized treatment planning, ultimately optimizing the use of medical resources. |
| format | Article |
| id | doaj-art-9f7a08d765104d9bb52697d8a1b49530 |
| institution | OA Journals |
| issn | 2352-0477 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj-art-9f7a08d765104d9bb52697d8a1b495302025-08-20T02:16:17ZengElsevierEuropean Journal of Radiology Open2352-04772025-06-011410065110.1016/j.ejro.2025.100651Radiomics in differential diagnosis of pancreatic tumorsRiccardo De Robertis0Beatrice Mascarin1Eda Bardhi2Flavio Spoto3Nicolò Cardobi4Mirko D’Onofrio5Department of Radiology, G.B. Rossi Hospital, University of Verona, Piazzale L.A. Scuro 10, Verona 37134, ItalyDepartment of Radiology, University of Verona, Verona, Italy; Corresponding author.Department of Radiology, University of Verona, Verona, ItalyDepartment of Radiology, University of Verona, Verona, ItalyDepartment of Radiology, G.B. Rossi Hospital, University of Verona, Piazzale L.A. Scuro 10, Verona 37134, ItalyDepartment of Radiology, G.B. Rossi Hospital, University of Verona, Piazzale L.A. Scuro 10, Verona 37134, ItalyThe aim of this study was to assess whether radiomics could predict histotype of pancreatic ductal adenocarcinomas (PDAC) and pancreatic neuroendocrine tumors (PNET). Contrast-enhanced CT scans of 193 patients were retrospectively reviewed, encompassing 97 PDACs and 96 PNETs. Additionally, anamnestic data and laboratory data were evaluated. A total of 107 features were extracted for both the arterial and venous phases. ROC curves were constructed for the parameters with the highest AUC, considering two groups: one including all lesions and the other including only lesions smaller than 5 cm. The following feature differences were found to be statistically significant (p < 0.05). Without considering lesion size: for the arterial phase, 16 first-order and 38 s-order features; for the venous phase, 10 first-order and 20 s-order features. When considering lesion size: for the arterial phase, 16 first-order and 52 s-order features; for the venous phase, 11 first-order and 36 s-order features. The radiomics features with the highest AUC values included ART_firstorder_RootMeanSquared (AUC = 0.896, p < 0.01) in the arterial phase and VEN_firstorder_Median (AUC = 0.737, p < 0.05) in the venous phase for all lesions, and ART_firstorder_RootMeanSquared (AUC = 0.859, p < 0.01) and VEN_firstorder_Median (AUC = 0.713, p < 0.05) for lesions smaller than 5 cm. Texture analysis of pancreatic pathology has shown good predictability in defining the PNET histotype. This analysis potentially offering a non-invasive, imaging-based method to accurately differentiate between pancreatic tumor types. Such advancements could lead to more precise and personalized treatment planning, ultimately optimizing the use of medical resources.http://www.sciencedirect.com/science/article/pii/S2352047725000188RadiomicsPancreatic TumorsTexture AnalysisContrast-Enhanced CT ScansPancreatic Ductal AdenocarcinomaPancreatic Neuroendocrine Tumors |
| spellingShingle | Riccardo De Robertis Beatrice Mascarin Eda Bardhi Flavio Spoto Nicolò Cardobi Mirko D’Onofrio Radiomics in differential diagnosis of pancreatic tumors European Journal of Radiology Open Radiomics Pancreatic Tumors Texture Analysis Contrast-Enhanced CT Scans Pancreatic Ductal Adenocarcinoma Pancreatic Neuroendocrine Tumors |
| title | Radiomics in differential diagnosis of pancreatic tumors |
| title_full | Radiomics in differential diagnosis of pancreatic tumors |
| title_fullStr | Radiomics in differential diagnosis of pancreatic tumors |
| title_full_unstemmed | Radiomics in differential diagnosis of pancreatic tumors |
| title_short | Radiomics in differential diagnosis of pancreatic tumors |
| title_sort | radiomics in differential diagnosis of pancreatic tumors |
| topic | Radiomics Pancreatic Tumors Texture Analysis Contrast-Enhanced CT Scans Pancreatic Ductal Adenocarcinoma Pancreatic Neuroendocrine Tumors |
| url | http://www.sciencedirect.com/science/article/pii/S2352047725000188 |
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