Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity
Background Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity. Purpose To develop a contrast‐enhanced (CE) MRI‐based...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2020-02, Vol.51 (2), p.397-406 |
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| creator | Lin, Qiao JI, Yi‐fan Chen, Yong Sun, Huan Yang, Dan‐dan Chen, Ai‐li Chen, Tian‐wu Zhang, Xiao Ming |
| description | Background
Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity.
Purpose
To develop a contrast‐enhanced (CE) MRI‐based radiomics model for the early prediction of AP severity.
Study Type
Retrospective.
Subjects
A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe).
Field Strength/Sequence
3.0T, T1‐weighted CE‐MRI.
Assessment
Radiomics features were extracted from the portal venous‐phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP.
Statistical Tests
Independent t‐test, Mann–Whitney U‐test, chi‐square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test.
Results
Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI).
Data Conclusion
The radiomics model had good performance in the early prediction of AP severity.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2020;51:397–406. |
| doi_str_mv | 10.1002/jmri.26798 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2232107785</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2232107785</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4238-92c1a2e67a6e948a327c77f17162af75eed7d6faf01e2dc653b59ddee21d87013</originalsourceid><addsrcrecordid>eNp90M1KxDAUhuEgiv8bL0ACbkSoJidt0y5l8BdFGHRriMkpZmibMWmV2XkJXqNXYscZXbhwlSwePg4vIXucHXPG4GTSBHcMuSyLFbLJM4AEsiJfHf4sEwkvmNwgWzFOGGNlmWbrZENwLgCY3CSPY22db5yJtPEWa-oranzbBR27z_cPbJ91a9DS2_EVrXygqEM9o9OA1pnO-Xbutek7pNMBBtSd61ykEV8xuG62Q9YqXUfcXb7b5OH87H50mdzcXVyNTm8Sk4IokhIM14C51DmWaaEFSCNlxSXPQVcyQ7TS5pWuGEewJs_EU1ZaiwjcFpJxsU0OF7vT4F96jJ1qXDRY17pF30cFIIAzKYtsoAd_6MT3oR2uUyBEWhZlKtNBHS2UCT7GgJWaBtfoMFOcqXl1Na-uvqsPeH852T81aH_pT-YB8AV4czXO_plS10PoxegX-rGONQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2334989474</pqid></control><display><type>article</type><title>Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity</title><source>MEDLINE</source><source>Wiley Online Library All Journals</source><creator>Lin, Qiao ; JI, Yi‐fan ; Chen, Yong ; Sun, Huan ; Yang, Dan‐dan ; Chen, Ai‐li ; Chen, Tian‐wu ; Zhang, Xiao Ming</creator><creatorcontrib>Lin, Qiao ; JI, Yi‐fan ; Chen, Yong ; Sun, Huan ; Yang, Dan‐dan ; Chen, Ai‐li ; Chen, Tian‐wu ; Zhang, Xiao Ming</creatorcontrib><description>Background
Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity.
Purpose
To develop a contrast‐enhanced (CE) MRI‐based radiomics model for the early prediction of AP severity.
Study Type
Retrospective.
Subjects
A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe).
Field Strength/Sequence
3.0T, T1‐weighted CE‐MRI.
Assessment
Radiomics features were extracted from the portal venous‐phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP.
Statistical Tests
Independent t‐test, Mann–Whitney U‐test, chi‐square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test.
Results
Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI).
Data Conclusion
The radiomics model had good performance in the early prediction of AP severity.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2020;51:397–406.</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.26798</identifier><identifier>PMID: 31132207</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Acute Disease ; acute pancreatitis ; Algorithms ; Computed tomography ; Feature extraction ; Field strength ; Humans ; Image analysis ; Image processing ; Magnetic Resonance Imaging ; Medical imaging ; Pancreatitis ; Pancreatitis - diagnostic imaging ; Predictive Value of Tests ; Radiomics ; Retrospective Studies ; severity ; Statistical analysis ; Statistical tests ; Support vector machines ; Training</subject><ispartof>Journal of magnetic resonance imaging, 2020-02, Vol.51 (2), p.397-406</ispartof><rights>2019 International Society for Magnetic Resonance in Medicine</rights><rights>2019 International Society for Magnetic Resonance in Medicine.</rights><rights>2020 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4238-92c1a2e67a6e948a327c77f17162af75eed7d6faf01e2dc653b59ddee21d87013</citedby><cites>FETCH-LOGICAL-c4238-92c1a2e67a6e948a327c77f17162af75eed7d6faf01e2dc653b59ddee21d87013</cites><orcidid>0000-0001-5327-8506</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.26798$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.26798$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31132207$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Qiao</creatorcontrib><creatorcontrib>JI, Yi‐fan</creatorcontrib><creatorcontrib>Chen, Yong</creatorcontrib><creatorcontrib>Sun, Huan</creatorcontrib><creatorcontrib>Yang, Dan‐dan</creatorcontrib><creatorcontrib>Chen, Ai‐li</creatorcontrib><creatorcontrib>Chen, Tian‐wu</creatorcontrib><creatorcontrib>Zhang, Xiao Ming</creatorcontrib><title>Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity.
Purpose
To develop a contrast‐enhanced (CE) MRI‐based radiomics model for the early prediction of AP severity.
Study Type
Retrospective.
Subjects
A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe).
Field Strength/Sequence
3.0T, T1‐weighted CE‐MRI.
Assessment
Radiomics features were extracted from the portal venous‐phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP.
Statistical Tests
Independent t‐test, Mann–Whitney U‐test, chi‐square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test.
Results
Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI).
Data Conclusion
The radiomics model had good performance in the early prediction of AP severity.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2020;51:397–406.</description><subject>Acute Disease</subject><subject>acute pancreatitis</subject><subject>Algorithms</subject><subject>Computed tomography</subject><subject>Feature extraction</subject><subject>Field strength</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image processing</subject><subject>Magnetic Resonance Imaging</subject><subject>Medical imaging</subject><subject>Pancreatitis</subject><subject>Pancreatitis - diagnostic imaging</subject><subject>Predictive Value of Tests</subject><subject>Radiomics</subject><subject>Retrospective Studies</subject><subject>severity</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Support vector machines</subject><subject>Training</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90M1KxDAUhuEgiv8bL0ACbkSoJidt0y5l8BdFGHRriMkpZmibMWmV2XkJXqNXYscZXbhwlSwePg4vIXucHXPG4GTSBHcMuSyLFbLJM4AEsiJfHf4sEwkvmNwgWzFOGGNlmWbrZENwLgCY3CSPY22db5yJtPEWa-oranzbBR27z_cPbJ91a9DS2_EVrXygqEM9o9OA1pnO-Xbutek7pNMBBtSd61ykEV8xuG62Q9YqXUfcXb7b5OH87H50mdzcXVyNTm8Sk4IokhIM14C51DmWaaEFSCNlxSXPQVcyQ7TS5pWuGEewJs_EU1ZaiwjcFpJxsU0OF7vT4F96jJ1qXDRY17pF30cFIIAzKYtsoAd_6MT3oR2uUyBEWhZlKtNBHS2UCT7GgJWaBtfoMFOcqXl1Na-uvqsPeH852T81aH_pT-YB8AV4czXO_plS10PoxegX-rGONQ</recordid><startdate>202002</startdate><enddate>202002</enddate><creator>Lin, Qiao</creator><creator>JI, Yi‐fan</creator><creator>Chen, Yong</creator><creator>Sun, Huan</creator><creator>Yang, Dan‐dan</creator><creator>Chen, Ai‐li</creator><creator>Chen, Tian‐wu</creator><creator>Zhang, Xiao Ming</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, 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>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-5327-8506</orcidid></search><sort><creationdate>202002</creationdate><title>Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity</title><author>Lin, Qiao ; JI, Yi‐fan ; Chen, Yong ; Sun, Huan ; Yang, Dan‐dan ; Chen, Ai‐li ; Chen, Tian‐wu ; Zhang, Xiao Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4238-92c1a2e67a6e948a327c77f17162af75eed7d6faf01e2dc653b59ddee21d87013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Acute Disease</topic><topic>acute pancreatitis</topic><topic>Algorithms</topic><topic>Computed tomography</topic><topic>Feature extraction</topic><topic>Field strength</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image processing</topic><topic>Magnetic Resonance Imaging</topic><topic>Medical imaging</topic><topic>Pancreatitis</topic><topic>Pancreatitis - diagnostic imaging</topic><topic>Predictive Value of Tests</topic><topic>Radiomics</topic><topic>Retrospective Studies</topic><topic>severity</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Support vector machines</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Qiao</creatorcontrib><creatorcontrib>JI, Yi‐fan</creatorcontrib><creatorcontrib>Chen, Yong</creatorcontrib><creatorcontrib>Sun, Huan</creatorcontrib><creatorcontrib>Yang, Dan‐dan</creatorcontrib><creatorcontrib>Chen, Ai‐li</creatorcontrib><creatorcontrib>Chen, Tian‐wu</creatorcontrib><creatorcontrib>Zhang, Xiao Ming</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Qiao</au><au>JI, Yi‐fan</au><au>Chen, Yong</au><au>Sun, Huan</au><au>Yang, Dan‐dan</au><au>Chen, Ai‐li</au><au>Chen, Tian‐wu</au><au>Zhang, Xiao Ming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2020-02</date><risdate>2020</risdate><volume>51</volume><issue>2</issue><spage>397</spage><epage>406</epage><pages>397-406</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Background
Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity.
Purpose
To develop a contrast‐enhanced (CE) MRI‐based radiomics model for the early prediction of AP severity.
Study Type
Retrospective.
Subjects
A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe).
Field Strength/Sequence
3.0T, T1‐weighted CE‐MRI.
Assessment
Radiomics features were extracted from the portal venous‐phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP.
Statistical Tests
Independent t‐test, Mann–Whitney U‐test, chi‐square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test.
Results
Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI).
Data Conclusion
The radiomics model had good performance in the early prediction of AP severity.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2020;51:397–406.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>31132207</pmid><doi>10.1002/jmri.26798</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5327-8506</orcidid></addata></record> |
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| subjects | Acute Disease acute pancreatitis Algorithms Computed tomography Feature extraction Field strength Humans Image analysis Image processing Magnetic Resonance Imaging Medical imaging Pancreatitis Pancreatitis - diagnostic imaging Predictive Value of Tests Radiomics Retrospective Studies severity Statistical analysis Statistical tests Support vector machines Training |
| title | Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity |
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