Literature DB >> 32168248

Texture Analysis: An Emerging Clinical Tool for Pancreatic Lesions.

Adam M Awe, Victoria R Rendell1, Meghan G Lubner2, Emily R Winslow3.   

Abstract

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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Year:  2020        PMID: 32168248      PMCID: PMC7135958          DOI: 10.1097/MPA.0000000000001495

Source DB:  PubMed          Journal:  Pancreas        ISSN: 0885-3177            Impact factor:   3.243


  39 in total

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5.  CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas.

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6.  Surgical resectability of pancreatic adenocarcinoma: CTA.

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Review 7.  Role of magnetic resonance imaging in the detection and characterization of solid pancreatic nodules: An update.

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8.  Diagnostic Yield From Screening Asymptomatic Individuals at High Risk for Pancreatic Cancer: A Meta-analysis of Cohort Studies.

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9.  Prevalence of unsuspected pancreatic cysts on MDCT.

Authors:  Thomas A Laffan; Karen M Horton; Alison P Klein; Bruce Berlanstein; Stanley S Siegelman; Satomi Kawamoto; Pamela T Johnson; Elliot K Fishman; Ralph H Hruban
Journal:  AJR Am J Roentgenol       Date:  2008-09       Impact factor: 3.959

10.  Molecular pathology of pancreatic neuroendocrine tumors.

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Journal:  J Gastrointest Oncol       Date:  2012-09
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  5 in total

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2.  Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach.

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Review 3.  Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions.

Authors:  Jorge D Machicado; Eugene J Koay; Somashekar G Krishna
Journal:  Diagnostics (Basel)       Date:  2020-07-21

Review 4.  Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

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Review 5.  Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications.

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Journal:  Cancers (Basel)       Date:  2022-03-24       Impact factor: 6.639

  5 in total

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