Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

• Published in: Cancer medicine (Malden, MA) Vol. 7; no. 10; pp. 4924 - 4931
• Main Authors: Li, Jiali, Lu, Jingyu, Liang, Ping, Li, Anqin, Hu, Yao, Shen, Yaqi, Hu, Daoyu, Li, Zhen
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.10.2018; John Wiley and Sons Inc
• Subjects: Adult; Aged; atypical pancreatic neuroendocrine; Carcinoma, Pancreatic Ductal - diagnostic imaging; Clinical Cancer Research; Computed tomography; Contrast Media; Diagnosis; Diagnosis, Differential; Entropy; Female; Humans; Image Interpretation, Computer-Assisted; Male; Middle Aged; Neuroendocrine tumors; Neuroendocrine Tumors - diagnostic imaging; Original Research; Pancreas; Pancreatic cancer; pancreatic ductal adenocarcinomas; Pancreatic Neoplasms - diagnostic imaging; Preoperative Period; Retrospective Studies; ROC Curve; texture analysis; Tomography, X-Ray Computed - methods; New York; United States > US; computed tomography; pancreatic ductal adenocarcinomas; atypical pancreatic neuroendocrine; texture analysis
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.1746

Background To explore the application value of computed tomography (CT) texture analysis in differentiating atypical pancreatic neuroendocrine tumors (pNET) from pancreatic ductal adenocarcinomas (PDAC). Materials and methods This single‐center retrospective study was approved by local institutional review board, and the requirement for informed consent was waived. We retrospectively analyzed 127 patients with 50 PDACs and 77 pNETs in pathology database between January 2012 and May 2017.These patients successfully finished preoperative contrast‐enhanced CT test. Texture parameters (mean, median, 5th, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis and entropy) were extracted from portal images and compared between PDAC and 77 pNET groups using proper statistical method. The optimal parameters for differentiating PDACs and atypical pNETs were gained through receiver operating characteristic (ROC) curves. Results On the basis of arterial enhancement, 52 pNETs (67%, 52/77) were typical hypervascular and 25 pNETs (32%, 25/77) were atypical hypovascular. Compared with PDACs, atypical pNETs had statistically higher mean, median, 5th, 10th, and 25th percentiles (P = 0.006, 0.024, 0.000, 0.001, 0.021, respectively) and statistically lower skewness (P = 0.017). However, there were no difference for 75th, 90th percentiles, kurtosis and entropy between these two tumors (P = 0.232, 0.415, 0.143, 0.291, respectively). For differentiating PDACs and atypical pNETs, 5th percentile and 5th+skewness were optimal parameters for alone and combined diagnosis, respectively. Conclusion Volumetric CT texture features, especially combined diagnosis of 5th+skewness can be used as a quantitative tool to distinguish atypical pNETs from PDACs. Differentiating quantitatively atypical pNET from PDAC before treatment is difficult but this study had demonstrated an ability to achieve this by CTTA.