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64-Slice spiral computed tomography and three-dimensional reconstruction in the diagnosis of cystic pancreatic tumors
Zhaoxia Wen1, Fengqing Yao1, Yuxing Wang1
1Department of Radiology, Linyi People's Hospital, Linyi, Shandong 276003, P.R. China.
Abstract:
The present study aimed to describe the characteristics of cystic pancreatic tumors using computed tomography (CT) and to evaluate the diagnostic accuracy (DA) of post-imaging three-dimensional (3D) reconstruction. Clinical and imaging data, including multi-slice spiral CT scans, enhanced scans and multi-faceted reconstruction, from 30 patients with pathologically confirmed cystic pancreatic tumors diagnosed at the Linyi People's Hospital between August 2008 and June 2014 were retrospectively analyzed. Following the injection of Ultravist® 300 contrast agent, arterial, portal venous and parenchymal phase scans were obtained at 28, 60 and 150 sec, respectively, and 3D reconstructions of the CT images were generated. The average age of the patients was 38.4 years (range, 16-77 years), and the cohort included 5 males and 25 females (ratio, 1:5). The patients included 8 cases of mucinous cystadenoma (DA), 80%]; 9 cases of cystadenocarcinoma (DA, 84%); 6 cases of serous cystadenoma (DA, 100%); 3 cases of solid pseudopapillary tumor (DA, 100%); and 4 cases of intraductal papillary mucinous neoplasm (DA, 100%). 3D reconstructions of CT images were generated and, in the 4 cases of intraductal papillary mucinous neoplasm, the tumor was connected to the main pancreatic duct and multiple mural nodules were detected in one of these cases. The DA of the 3D-reconstructed images of cystic pancreatic tumors was 89.3%. The 64-slice spiral CT and 3D-reconstructed CT images facilitated the visualization of cystic pancreatic tumor characteristics, in particular the connections between the tumor and the main pancreatic duct. In conclusion, the 3D reconstruction of multi-slice CT data may provide an important source of information for the surgical team, in combination with the available clinical data.
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