Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances
Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently...
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| Veröffentlicht in: | Canadian Association of Radiologists Journal 2023-05, Vol.74 (2), p.351-361 |
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| container_title | Canadian Association of Radiologists Journal |
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| creator | Barat, Maxime Marchese, Ugo Pellat, Anna Dohan, Anthony Coriat, Romain Hoeffel, Christine Fishman, Elliot K. Cassinotto, Christophe Chu, Linda Soyer, Philippe |
| description | Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed. |
| doi_str_mv | 10.1177/08465371221124927 |
| format | Article |
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Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.</description><identifier>ISSN: 0846-5371</identifier><identifier>EISSN: 1488-2361</identifier><identifier>DOI: 10.1177/08465371221124927</identifier><identifier>PMID: 36065572</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><subject>Adenocarcinoma ; Artificial Intelligence ; Cancer ; Carcinoma, Pancreatic Ductal ; Computed tomography ; Data acquisition ; Humans ; Image acquisition ; Lesions ; Magnetic resonance imaging ; Medical imaging ; Pancreas ; Pancreatic cancer ; Pancreatic carcinoma ; Pancreatic Neoplasms ; Pancreatic Neoplasms - pathology ; Radiomics ; Tomography, X-Ray Computed - methods ; Tumors</subject><ispartof>Canadian Association of Radiologists Journal, 2023-05, Vol.74 (2), p.351-361</ispartof><rights>The Author(s) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c434t-117d672f4e9d2ea6c0e616f8c99fdef2e9f25b0de4ae732304fe6222591fbb4e3</citedby><cites>FETCH-LOGICAL-c434t-117d672f4e9d2ea6c0e616f8c99fdef2e9f25b0de4ae732304fe6222591fbb4e3</cites><orcidid>0000-0002-5055-1682</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/08465371221124927$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/08465371221124927$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,315,781,785,793,21823,27926,27928,27929,43625,43626</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36065572$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Barat, Maxime</creatorcontrib><creatorcontrib>Marchese, Ugo</creatorcontrib><creatorcontrib>Pellat, Anna</creatorcontrib><creatorcontrib>Dohan, Anthony</creatorcontrib><creatorcontrib>Coriat, Romain</creatorcontrib><creatorcontrib>Hoeffel, Christine</creatorcontrib><creatorcontrib>Fishman, Elliot K.</creatorcontrib><creatorcontrib>Cassinotto, Christophe</creatorcontrib><creatorcontrib>Chu, Linda</creatorcontrib><creatorcontrib>Soyer, Philippe</creatorcontrib><title>Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances</title><title>Canadian Association of Radiologists Journal</title><addtitle>Can Assoc Radiol J</addtitle><description>Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.</description><subject>Adenocarcinoma</subject><subject>Artificial Intelligence</subject><subject>Cancer</subject><subject>Carcinoma, Pancreatic Ductal</subject><subject>Computed tomography</subject><subject>Data acquisition</subject><subject>Humans</subject><subject>Image acquisition</subject><subject>Lesions</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Pancreas</subject><subject>Pancreatic cancer</subject><subject>Pancreatic carcinoma</subject><subject>Pancreatic Neoplasms</subject><subject>Pancreatic Neoplasms - pathology</subject><subject>Radiomics</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Tumors</subject><issn>0846-5371</issn><issn>1488-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10M9LwzAUB_Agis7pH-BFCl68VJOXNGm8jflrMFDEnUuWvoyONZ1NK_jfm7GpoHjK4X2-3zweIWeMXjGm1DXNhcy4YgCMgdCg9siAiTxPgUu2TwabeboBR-Q4hCWlVHClD8kRl1RmmYIBmUxqs6j8Imlc8my8bdF0lU1ue9uZVTIq0TfWtLbyTW1ukpFPZuvSdJg0PnlBi76L5j3mMJyQA2dWAU9375DM7u9ex4_p9OlhMh5NUyu46NK4eCkVOIG6BDTSUpRMutxq7Up0gNpBNqclCoOKA6fCoQSATDM3nwvkQ3K57V23zVuPoSvqKlhcrYzHpg8FKEa1AqAq0otfdNn0rY_bRaUlBSZ5HhXbKts2IbToinVb1ab9KBgtNncu_tw5Zs53zf28xvI78XXYCK62IJgF_nz7f-MnieiDKQ</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Barat, Maxime</creator><creator>Marchese, Ugo</creator><creator>Pellat, Anna</creator><creator>Dohan, Anthony</creator><creator>Coriat, Romain</creator><creator>Hoeffel, Christine</creator><creator>Fishman, Elliot K.</creator><creator>Cassinotto, Christophe</creator><creator>Chu, Linda</creator><creator>Soyer, Philippe</creator><general>SAGE Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>L7M</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5055-1682</orcidid></search><sort><creationdate>20230501</creationdate><title>Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances</title><author>Barat, Maxime ; 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Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. 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| subjects | Adenocarcinoma Artificial Intelligence Cancer Carcinoma, Pancreatic Ductal Computed tomography Data acquisition Humans Image acquisition Lesions Magnetic resonance imaging Medical imaging Pancreas Pancreatic cancer Pancreatic carcinoma Pancreatic Neoplasms Pancreatic Neoplasms - pathology Radiomics Tomography, X-Ray Computed - methods Tumors |
| title | Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances |
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