Abstract
Radiomics, also known as quantitative imaging or texture analysis, involves extracting a large number of features traditionally unmeasured in conventional radiological cross-sectional images and converting them into mathematical models. This review describes this approach and its use in the evaluation of pancreatic cystic lesions (PCLs). This discipline has the potential of more accurately assessing, classifying, risk stratifying, and guiding the management of PCLs. Existing studies have provided important insight into the role of radiomics in managing PCLs. Although these studies are limited by the use of retrospective design, single center data, and small sample sizes, radiomic features in combination with clinical data appear to be superior to the current standard of care in differentiating cyst type and in identifying mucinous PCLs with high-grade dysplasia. Combining radiomic features with other novel endoscopic diagnostics, including cyst fluid molecular analysis and confocal endomicroscopy, can potentially optimize the predictive accuracy of these models. There is a need for multicenter prospective studies to elucidate the role of radiomics in the management of PCLs.
Highlights
The widespread use of high-resolution cross-sectional abdominal imaging has increased the incidental detection of pancreatic cystic lesions (PCLs) on imaging studies performed for unrelated reasons [1]
Sometimes referred to as quantitative imaging, implies the extraction of a vast number of features from medical images, and its conversion to high-dimensional data [21]. Imaging features such as size, shape, physical transport properties, and texture are extracted from conventional CT, MRI, or positron emission tomography (PET) images into a database, and are used to create statistical models that can improve diagnostic, prognostic, and predictive accuracy [22]
The electronic search strategy was conducted on 25 June 2020 using a combination of phrases indicating the diseases of interest [“pancreatic cyst”, “pancreatic cystic lesion(s)”, “intraductal papillary mucinous neoplasm”, “mucinous cyst”, “serous cystadenoma”, “pancreatic cancer”] and imaging technology [“radiomic(s)”, “texture analysis”, “quantitative imaging”]
Summary
The widespread use of high-resolution cross-sectional abdominal imaging has increased the incidental detection of pancreatic cystic lesions (PCLs) on imaging studies performed for unrelated reasons [1]. The diagnosis, risk-stratification, and management of PCLs have relied heavily on qualitative imaging characteristics and endoscopic ultrasound (EUS) with cyst fluid analysis (cytology, carcinoembryonic antigen [CEA], and amylase level) as a standard of care [5,6,7]. These diagnostic tools are suboptimal in the accurate differentiation of premalignant or malignant cysts, Diagnostics 2020, 10, 505; doi:10.3390/diagnostics10070505 www.mdpi.com/journal/diagnostics. This review aims to depict this novel technology and its application in the management of PCLs, including assessing the current limitations and discussing future directions
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