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    Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images

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    Author
    Ren, S.
    Zhang, J.
    Chen, R.
    Date
    2019
    Journal
    Frontiers in Oncology
    Publisher
    Frontiers Media S.A.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3389/fonc.2019.01171
    Abstract
    Purpose: To investigate the potential of computed tomography (CT) imaging features and texture analysis to differentiate between mass-forming pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: Thirty patients with pathologically proved MFP and 79 patients with PDAC were included in this study. Clinical data and CT imaging features of the two lesions were evaluated. Texture features were extracted from arterial and portal phase CT images using commercially available software (AnalysisKit). Multivariate logistic regression analyses were used to identify relevant CT imaging and texture parameters to discriminate MFP from PDAC. Receiver operating characteristic curves were performed to determine the diagnostic performance of predictions. Results: MFP showed a larger size compared to PDAC (p = 0.009). Cystic degeneration, pancreatic ductal dilatation, vascular invasion, and pancreatic sinistral portal hypertension were more frequent and duct penetrating sign was less frequent in PDAC compared to MFP. Arterial CT attenuation, arterial, and portal enhancement ratios of MFP were higher than PDAC (p < 0.05). In multivariate analysis, arterial CT attenuation and pancreatic duct penetrating sign were independent predictors. Texture features in arterial phase including SurfaceArea, Percentile40, InverseDifferenceMoment_angle90_offset4, LongRunEmphasis_angle45_offset4, and uniformity were independent predictors. Texture features in portal phase including LongRunEmphasis_angle135_offset7, VoxelValueSum, LongRunEmphasis_angle135_offset4, and GLCMEntropy_angle45_offset1 were independent predictors. Areas under the curve of imaging feature-based, texture feature-based in arterial and portal phases, and the combined models were 0.84, 0.96, 0.93, and 0.98, respectively. Conclusions: CT texture analysis demonstrates great potential to differentiate MFP from PDAC. Copyright 2019 The Authors.
    Sponsors
    This study was funded by Jiangsu Provincial Key Research and Development Program (BE2017772), Administration of Traditional Chinese Medicine of Jiangsu Province (ZD201907), and the National Natural Science Foundation of China (81771899).
    Keyword
    chronic pancreatitis
    computed tomography
    machine learning
    pancreatic ductal adenocarcinoma
    texture analysis
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075370331&doi=10.3389%2ffonc.2019.01171&partnerID=40&md5=334b25657a87ec1d2a5748c27a9af572; http://hdl.handle.net/10713/11469
    ae974a485f413a2113503eed53cd6c53
    10.3389/fonc.2019.01171
    Scopus Count
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    UMB Open Access Articles 2019

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