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    Pancreatic ductal adenocarcinoma: Machine learning-based quantitative computed tomography texture analysis for prediction of histopathological grade

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    Author
    Qiu, W.
    Wang, Z.
    Chen, R.
    Date
    2019
    Journal
    Cancer Management and Research
    Publisher
    Dove Medical Press Ltd
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.2147/CMAR.S218414
    Abstract
    Purpose: To assess the performance of combining computed tomography (CT) texture analysis with machine learning for discriminating different histopathological grades of pancreatic ductal adenocarcinoma (PDAC). Methods: From July 2012 to August 2017, this retrospective study comprised 56 patients with confirmed histopathological PDAC (32 men, 24 women, mean age 64.04±7.82 years) who had undergone preoperative contrast-enhanced CT imaging within 1 month before surgery. Two radiologists blinded to the histopathological outcome independently segmented lesions for quantitative texture analysis. Histogram features, co-occurrence, and run-length texture were calculated. A support-vector machine was constructed to predict the pathological grade of PDAC based on preoperative texture features. Results: Pathological analysis confirmed 37 low-grade PDAC (five well-differentiated/grade I and 32 moderately differentiated/grade II) and 19 high-grade PDAC (19 poorly differentiated/grade III) tumors. There were no significant differences in clinical or biological characteristics between patients with high-grade and low-grade tumors (P>0.05). There were significant differences between low-grade PDAC and high-grade PDAC on nine histogram features, seven run-length features, and two co-occurrence features. Cluster shade was the most important predictor (sensitivity 0.315). Using these texture features, the support-vector machine achieved 86% accuracy, 78% sensitivity, 95% and specificity. Conclusion: Machine learning-based CT texture analysis accurately predicted histopathological differentiation grade of PDAC based on preoperative texture features, leading to maximization patient survival and achievement of personalized precision treatment. Copyright 2019 Qiu et al.
    Sponsors
    This work was supported by the National Natural Science Foundation of China (grant 81771899) and the Primary Research and Development Plan of Jiangsu Province (BE2017772).
    Keyword
    Computed tomography
    Histopathological grade
    Machine learning
    Pancreatic ductal adenocarcinoma
    Texture analysis
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074229947&doi=10.2147%2fCMAR.S218414&partnerID=40&md5=641494ab01fbc5045076e72eaa7b711f; http://hdl.handle.net/10713/11397
    ae974a485f413a2113503eed53cd6c53
    10.2147/CMAR.S218414
    Scopus Count
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    UMB Open Access Articles 2019

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