Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance
Purpose To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical a...
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Veröffentlicht in: | European journal of nuclear medicine and molecular imaging 2021-11, Vol.48 (12), p.4002-4015 |
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container_title | European journal of nuclear medicine and molecular imaging |
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creator | Bezzi, C. Mapelli, P. Presotto, L. Neri, I. Scifo, P. Savi, A. Bettinardi, V. Partelli, S. Gianolli, L. Falconi, M. Picchio, M. |
description | Purpose
To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided.
Methods
Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction.
Results
Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response.
Conclusions
Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients. |
doi_str_mv | 10.1007/s00259-021-05338-8 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2511241014</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2511241014</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-e1c742cbf12f014567f55eaa0837a2c9bf507cdb5df69dea02cb2bb4cd462b953</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouq7-AQ9S8OKlOkmbpvEmi1-wIIheDWmSrpE2WZP24L837q4rePA0w-SZN8OD0AmGCwzALiMAoTwHgnOgRVHn9Q6a4ArznEHNd7c9gwN0GOM7AK5JzffRQYILSghM0OuT1Nb3VsXMumwpnQpGDlZlzozBG6e9CtaZbBh7H-JV1pvhzWvf-YVVsstsjKOJmXQ6U511q1m0C2fb1DpljtBeK7tojjd1il5ub55n9_n88e5hdj3PVcHokBusWElU02LSAi5pxVpKjZRQF0wSxZuWAlO6obqtuDYSEkuaplS6rEjDaTFF5-vcZfAf6aJB9DYq03XSGT9GQSjGpMQpO6Fnf9B3PwaXrksUYxzzEleJImtKBR9jMK1YBtvL8CkwiG_7Ym1fJPtiZV_Uael0Ez02vdHblR_dCSjWQExPbmHC79__xH4BjiaRQg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2577919416</pqid></control><display><type>article</type><title>Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Bezzi, C. ; Mapelli, P. ; Presotto, L. ; Neri, I. ; Scifo, P. ; Savi, A. ; Bettinardi, V. ; Partelli, S. ; Gianolli, L. ; Falconi, M. ; Picchio, M.</creator><creatorcontrib>Bezzi, C. ; Mapelli, P. ; Presotto, L. ; Neri, I. ; Scifo, P. ; Savi, A. ; Bettinardi, V. ; Partelli, S. ; Gianolli, L. ; Falconi, M. ; Picchio, M.</creatorcontrib><description>Purpose
To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided.
Methods
Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction.
Results
Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response.
Conclusions
Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.</description><identifier>ISSN: 1619-7070</identifier><identifier>EISSN: 1619-7089</identifier><identifier>DOI: 10.1007/s00259-021-05338-8</identifier><identifier>PMID: 33835220</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Advanced Image Analyses (Radiomics and Artificial Intelligence) ; Cardiology ; Clinical significance ; Diagnosis ; Diagnosis, Differential ; Differential diagnosis ; Humans ; Imaging ; Medicine ; Medicine & Public Health ; Neoplasms ; Neuroendocrine tumors ; Neuroendocrine Tumors - diagnostic imaging ; Nuclear Medicine ; Oncology ; Optimization ; Orthopedics ; Pancreas ; Pancreatic cancer ; Pancreatic Neoplasms - diagnostic imaging ; Predictions ; Radiology ; Radiomics ; Reproducibility of Results ; Review Article ; Therapeutic applications ; Tumors ; Workflow</subject><ispartof>European journal of nuclear medicine and molecular imaging, 2021-11, Vol.48 (12), p.4002-4015</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-e1c742cbf12f014567f55eaa0837a2c9bf507cdb5df69dea02cb2bb4cd462b953</citedby><cites>FETCH-LOGICAL-c375t-e1c742cbf12f014567f55eaa0837a2c9bf507cdb5df69dea02cb2bb4cd462b953</cites><orcidid>0000-0002-7532-6211</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00259-021-05338-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00259-021-05338-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33835220$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bezzi, C.</creatorcontrib><creatorcontrib>Mapelli, P.</creatorcontrib><creatorcontrib>Presotto, L.</creatorcontrib><creatorcontrib>Neri, I.</creatorcontrib><creatorcontrib>Scifo, P.</creatorcontrib><creatorcontrib>Savi, A.</creatorcontrib><creatorcontrib>Bettinardi, V.</creatorcontrib><creatorcontrib>Partelli, S.</creatorcontrib><creatorcontrib>Gianolli, L.</creatorcontrib><creatorcontrib>Falconi, M.</creatorcontrib><creatorcontrib>Picchio, M.</creatorcontrib><title>Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance</title><title>European journal of nuclear medicine and molecular imaging</title><addtitle>Eur J Nucl Med Mol Imaging</addtitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><description>Purpose
To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided.
Methods
Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction.
Results
Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response.
Conclusions
Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.</description><subject>Advanced Image Analyses (Radiomics and Artificial Intelligence)</subject><subject>Cardiology</subject><subject>Clinical significance</subject><subject>Diagnosis</subject><subject>Diagnosis, Differential</subject><subject>Differential diagnosis</subject><subject>Humans</subject><subject>Imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neoplasms</subject><subject>Neuroendocrine tumors</subject><subject>Neuroendocrine Tumors - diagnostic imaging</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Optimization</subject><subject>Orthopedics</subject><subject>Pancreas</subject><subject>Pancreatic cancer</subject><subject>Pancreatic Neoplasms - diagnostic imaging</subject><subject>Predictions</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Reproducibility of Results</subject><subject>Review Article</subject><subject>Therapeutic applications</subject><subject>Tumors</subject><subject>Workflow</subject><issn>1619-7070</issn><issn>1619-7089</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kE1LxDAQhoMouq7-AQ9S8OKlOkmbpvEmi1-wIIheDWmSrpE2WZP24L837q4rePA0w-SZN8OD0AmGCwzALiMAoTwHgnOgRVHn9Q6a4ArznEHNd7c9gwN0GOM7AK5JzffRQYILSghM0OuT1Nb3VsXMumwpnQpGDlZlzozBG6e9CtaZbBh7H-JV1pvhzWvf-YVVsstsjKOJmXQ6U511q1m0C2fb1DpljtBeK7tojjd1il5ub55n9_n88e5hdj3PVcHokBusWElU02LSAi5pxVpKjZRQF0wSxZuWAlO6obqtuDYSEkuaplS6rEjDaTFF5-vcZfAf6aJB9DYq03XSGT9GQSjGpMQpO6Fnf9B3PwaXrksUYxzzEleJImtKBR9jMK1YBtvL8CkwiG_7Ym1fJPtiZV_Uael0Ez02vdHblR_dCSjWQExPbmHC79__xH4BjiaRQg</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Bezzi, C.</creator><creator>Mapelli, P.</creator><creator>Presotto, L.</creator><creator>Neri, I.</creator><creator>Scifo, P.</creator><creator>Savi, A.</creator><creator>Bettinardi, V.</creator><creator>Partelli, S.</creator><creator>Gianolli, L.</creator><creator>Falconi, M.</creator><creator>Picchio, M.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</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>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7532-6211</orcidid></search><sort><creationdate>20211101</creationdate><title>Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance</title><author>Bezzi, C. ; Mapelli, P. ; Presotto, L. ; Neri, I. ; Scifo, P. ; Savi, A. ; Bettinardi, V. ; Partelli, S. ; Gianolli, L. ; Falconi, M. ; Picchio, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-e1c742cbf12f014567f55eaa0837a2c9bf507cdb5df69dea02cb2bb4cd462b953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Advanced Image Analyses (Radiomics and Artificial Intelligence)</topic><topic>Cardiology</topic><topic>Clinical significance</topic><topic>Diagnosis</topic><topic>Diagnosis, Differential</topic><topic>Differential diagnosis</topic><topic>Humans</topic><topic>Imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neoplasms</topic><topic>Neuroendocrine tumors</topic><topic>Neuroendocrine Tumors - diagnostic imaging</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Optimization</topic><topic>Orthopedics</topic><topic>Pancreas</topic><topic>Pancreatic cancer</topic><topic>Pancreatic Neoplasms - diagnostic imaging</topic><topic>Predictions</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Reproducibility of Results</topic><topic>Review Article</topic><topic>Therapeutic applications</topic><topic>Tumors</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bezzi, C.</creatorcontrib><creatorcontrib>Mapelli, P.</creatorcontrib><creatorcontrib>Presotto, L.</creatorcontrib><creatorcontrib>Neri, I.</creatorcontrib><creatorcontrib>Scifo, P.</creatorcontrib><creatorcontrib>Savi, A.</creatorcontrib><creatorcontrib>Bettinardi, V.</creatorcontrib><creatorcontrib>Partelli, S.</creatorcontrib><creatorcontrib>Gianolli, L.</creatorcontrib><creatorcontrib>Falconi, M.</creatorcontrib><creatorcontrib>Picchio, M.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of nuclear medicine and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bezzi, C.</au><au>Mapelli, P.</au><au>Presotto, L.</au><au>Neri, I.</au><au>Scifo, P.</au><au>Savi, A.</au><au>Bettinardi, V.</au><au>Partelli, S.</au><au>Gianolli, L.</au><au>Falconi, M.</au><au>Picchio, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance</atitle><jtitle>European journal of nuclear medicine and molecular imaging</jtitle><stitle>Eur J Nucl Med Mol Imaging</stitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><date>2021-11-01</date><risdate>2021</risdate><volume>48</volume><issue>12</issue><spage>4002</spage><epage>4015</epage><pages>4002-4015</pages><issn>1619-7070</issn><eissn>1619-7089</eissn><abstract>Purpose
To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided.
Methods
Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction.
Results
Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response.
Conclusions
Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33835220</pmid><doi>10.1007/s00259-021-05338-8</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-7532-6211</orcidid></addata></record> |
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subjects | Advanced Image Analyses (Radiomics and Artificial Intelligence) Cardiology Clinical significance Diagnosis Diagnosis, Differential Differential diagnosis Humans Imaging Medicine Medicine & Public Health Neoplasms Neuroendocrine tumors Neuroendocrine Tumors - diagnostic imaging Nuclear Medicine Oncology Optimization Orthopedics Pancreas Pancreatic cancer Pancreatic Neoplasms - diagnostic imaging Predictions Radiology Radiomics Reproducibility of Results Review Article Therapeutic applications Tumors Workflow |
title | Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance |
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