Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions
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 evaluati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/10/7/505 |
| _version_ | 1827712572616343552 |
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| author | Jorge D. Machicado Eugene J. Koay Somashekar G. Krishna |
| author_facet | Jorge D. Machicado Eugene J. Koay Somashekar G. Krishna |
| author_sort | Jorge D. Machicado |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:18:36Z |
| format | Article |
| id | doaj.art-26e6614c925641d487f24ec2e5907858 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T18:18:36Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-26e6614c925641d487f24ec2e59078582023-11-20T07:29:40ZengMDPI AGDiagnostics2075-44182020-07-0110750510.3390/diagnostics10070505Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic LesionsJorge D. Machicado0Eugene J. Koay1Somashekar G. Krishna2Division of Gastroenterology and Hepatology, Mayo Clinic Heath System, Eau Claire, WI 54703, USADepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADivision of Gastroenterology, Hepatology and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USARadiomics, 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.https://www.mdpi.com/2075-4418/10/7/505radiomicsquantitative imagingtexturepancreatic cystintraductal papillary mucinous neoplasm |
| spellingShingle | Jorge D. Machicado Eugene J. Koay Somashekar G. Krishna Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions Diagnostics radiomics quantitative imaging texture pancreatic cyst intraductal papillary mucinous neoplasm |
| title | Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions |
| title_full | Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions |
| title_fullStr | Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions |
| title_full_unstemmed | Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions |
| title_short | Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions |
| title_sort | radiomics for the diagnosis and differentiation of pancreatic cystic lesions |
| topic | radiomics quantitative imaging texture pancreatic cyst intraductal papillary mucinous neoplasm |
| url | https://www.mdpi.com/2075-4418/10/7/505 |
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