TY - JOUR
T1 - Radiomics for the diagnosis and differentiation of pancreatic cystic lesions
AU - Machicado, Jorge D.
AU - Koay, Eugene J.
AU - Krishna, Somashekar G.
N1 - Funding Information:
Funding: Eugene Koay was supported by the MD Anderson Cancer Center Support (Core) Grant CA016672 and U54CA210181-01, 5U01CA214263-02, 1P50CA221707-01A1, U01CA196403, 5R01CA218004-02, and 5U01CA200468-04.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
PY - 2020/7/21
Y1 - 2020/7/21
N2 - 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.
AB - 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.
KW - Intraductal papillary mucinous neoplasm
KW - Pancreatic cyst
KW - Quantitative imaging
KW - Radiomics
KW - Texture
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U2 - 10.3390/diagnostics10070505
DO - 10.3390/diagnostics10070505
M3 - Review article
C2 - 32708348
AN - SCOPUS:85089894031
SN - 2075-4418
VL - 10
JO - Diagnostics
JF - Diagnostics
IS - 7
M1 - 505
ER -