期刊
PANCREAS
卷 49, 期 3, 页码 301-312出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MPA.0000000000001495
关键词
texture analysis; imaging; pancreas; pancreatic cysts; pancreatic ductal adenocarcinoma (PDAC); pancreatic neuroendocrine tumor (PNET); F-18-FDG PET; CT-F-18-labeled fluoro-2-deoxyglucose positron emission tomography; computed tomography; ACM - angular co-occurrence matrix; AUC - area under the curve; AWE - average-weighted eccentricity; BD-IPMN - branch duct intraductal papillary mucinous neoplasm; CA 19-9-carbohydrate antigen 19-9; CECT - contrast-enhanced computed tomography; CGITA - Chang-Gung Image Texture Analysis; CI - confidence interval; CTA - computed tomography angiography; DFS - disease-free survival; EBF - enhanced boundary fraction; EIF - enhanced inside fraction; EUS; FNA - endoscopic ultrasound; fine-needle aspiration; FLCCF - filled largest connected component fraction; GLCM - gray-level co-occurrence matrix; HU - Hounsfield Unit; IBEX - Imaging Biomarker Explorer; IPAS - intrapancreatic accessory spleens; IPMN - intraductal papillary mucinous neoplasm; MCN - mucinous cystic neoplasms; MPP - mean of positive pixels; MR - magnetic resonance; OS - overall survivability; PDAC - pancreatic ductal adenocarcinoma; PET - positron emission tomography; PNET - pancreatic neuroendocrine tumor; RiF - radiographically inspired features; ROC - receiver operating characteristic; ROI - region of interest; SCA - serous cystadenoma; SSF - spatial scaling factor; SUV - standard uptake volume
资金
- National Institutes of Health/National Cancer Institute [F32 CA232352]
- Philips
- Ethicon
Radiologic characterization of pancreatic lesions is currently limited. Computed tomography is insensitive in detecting and characterizing small pancreatic lesions. Moreover, heterogeneity of many pancreatic lesions makes determination of malignancy challenging. As a result, invasive diagnostic testing is frequently used to characterize pancreatic lesions but often yields indeterminate results. Computed tomography texture analysis (CTTA) is an emerging noninvasive computational tool that quantifies gray-scale pixels/voxels and their spatial relationships within a region of interest. In nonpancreatic lesions, CTTA has shown promise in diagnosis, lesion characterization, and risk stratification, and more recently, pancreatic applications of CTTA have been explored. This review outlines the emerging role of CTTA in identifying, characterizing, and risk stratifying pancreatic lesions. Although recent studies show the clinical potential of CTTA of the pancreas, a clear understanding of which specific texture features correlate with high-grade dysplasia and predict survival has not yet been achieved. Further multidisciplinary investigations using strong radiologic-pathologic correlation are needed to establish a role for this noninvasive diagnostic tool.
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