Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA
Objectives A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA. Methods In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assig...
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| Veröffentlicht in: | European radiology 2021-03, Vol.31 (3), p.1216-1226 |
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| creator | Yu, Yiming Wang, Yubo Yang, Maoqing Huang, Meiping Li, Jun Jia, Qianjun Zhuang, Jian Huang, Liyu |
| description | Objectives
A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA.
Methods
In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression.
Results
Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (
p
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| doi_str_mv | 10.1007/s00330-020-07238-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2440473461</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2440473461</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-f1c997e6faedf59c68b4d842104ead2a190f6d1310107927b367d778596451003</originalsourceid><addsrcrecordid>eNp9kMtKxDAUhoMoOo6-gAsJuHFTPbm0SZbDMF5AcKPrmGkTrUwbTdKBeXtTxwu4EHJIOPnOn_AhdELgggCIywjAGBRAcwnKZEF20IRwRgsCku-iCajcFErxA3QY4ysAKMLFPjpgVMqSKj5BT4u1WQ0mtf0zTi8WR7u2oU0b7B02PqS2xrU3oU4Z8T1ux-VMnyIe4jhjepN8lylnTRqCjbizJuZDgzM-f5gdoT1nVtEef-1T9Hi1eJjfFHf317fz2V1RM1GmwpFaKWErZ2zjSlVXcskbySkBbk1DDVHgqoYwAgSEomLJKtEIIUtV8TLbYFN0vs19C_59sDHpro21Xa1Mb_0QNeUcuGC8Ihk9-4O--iH0-XeZklIITvgYSLdUHXyMwTr9FtrOhI0moEf_eutfZ__6078eo0-_oodlZ5ufkW_hGWBbIOar_tmG37f_if0ACuWPQg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2488774140</pqid></control><display><type>article</type><title>Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Yu, Yiming ; Wang, Yubo ; Yang, Maoqing ; Huang, Meiping ; Li, Jun ; Jia, Qianjun ; Zhuang, Jian ; Huang, Liyu</creator><creatorcontrib>Yu, Yiming ; Wang, Yubo ; Yang, Maoqing ; Huang, Meiping ; Li, Jun ; Jia, Qianjun ; Zhuang, Jian ; Huang, Liyu</creatorcontrib><description>Objectives
A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA.
Methods
In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression.
Results
Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (
p
< 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (
p
< 0.001), the diameter of the descending aorta at the diaphragmatic level (
p
< 0.001) and weight (
p
= 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation.
Conclusions
CoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered.
Key Points
• CTA is routinely ordered for infants with coarctation of the aorta; however, whether anatomical variations observed with CTA could be used to assess the severity of CoA remains unknown.
• Using the diameter and area of the coarctation site adjusted to body growth as features, the LDA model achieved an accuracy of 88.6% and 90.2% in differentiating between the mild and severe CoA patients in the non-PDA group and PDA group, respectively.
• The narrowest aortic diameter (CoA diameter) indexed to weight has a hazard ratio of 10.29 for re-coarctation.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-020-07238-1</identifier><identifier>PMID: 32885294</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Alternations ; Aorta ; Aorta - diagnostic imaging ; Aortic Coarctation - diagnostic imaging ; Area ; Cardiac ; Congenital diseases ; Coronary vessels ; Diagnostic Radiology ; Diameters ; Discriminant analysis ; Echocardiography ; Evaluation ; Humans ; Image reconstruction ; Imaging ; Infant ; Infants ; Internal Medicine ; Interventional Radiology ; Learning algorithms ; Machine learning ; Medicine ; Medicine & Public Health ; Model accuracy ; Neuroradiology ; Proportional Hazards Models ; Radiology ; Regression analysis ; Retrospective Studies ; Thorax ; Ultrasound ; Weight</subject><ispartof>European radiology, 2021-03, Vol.31 (3), p.1216-1226</ispartof><rights>European Society of Radiology 2020</rights><rights>European Society of Radiology 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-f1c997e6faedf59c68b4d842104ead2a190f6d1310107927b367d778596451003</citedby><cites>FETCH-LOGICAL-c375t-f1c997e6faedf59c68b4d842104ead2a190f6d1310107927b367d778596451003</cites><orcidid>0000-0001-6534-2712</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/s00330-020-07238-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-020-07238-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32885294$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yu, Yiming</creatorcontrib><creatorcontrib>Wang, Yubo</creatorcontrib><creatorcontrib>Yang, Maoqing</creatorcontrib><creatorcontrib>Huang, Meiping</creatorcontrib><creatorcontrib>Li, Jun</creatorcontrib><creatorcontrib>Jia, Qianjun</creatorcontrib><creatorcontrib>Zhuang, Jian</creatorcontrib><creatorcontrib>Huang, Liyu</creatorcontrib><title>Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA.
Methods
In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression.
Results
Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (
p
< 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (
p
< 0.001), the diameter of the descending aorta at the diaphragmatic level (
p
< 0.001) and weight (
p
= 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation.
Conclusions
CoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered.
Key Points
• CTA is routinely ordered for infants with coarctation of the aorta; however, whether anatomical variations observed with CTA could be used to assess the severity of CoA remains unknown.
• Using the diameter and area of the coarctation site adjusted to body growth as features, the LDA model achieved an accuracy of 88.6% and 90.2% in differentiating between the mild and severe CoA patients in the non-PDA group and PDA group, respectively.
• The narrowest aortic diameter (CoA diameter) indexed to weight has a hazard ratio of 10.29 for re-coarctation.</description><subject>Algorithms</subject><subject>Alternations</subject><subject>Aorta</subject><subject>Aorta - diagnostic imaging</subject><subject>Aortic Coarctation - diagnostic imaging</subject><subject>Area</subject><subject>Cardiac</subject><subject>Congenital diseases</subject><subject>Coronary vessels</subject><subject>Diagnostic Radiology</subject><subject>Diameters</subject><subject>Discriminant analysis</subject><subject>Echocardiography</subject><subject>Evaluation</subject><subject>Humans</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Infant</subject><subject>Infants</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Model accuracy</subject><subject>Neuroradiology</subject><subject>Proportional Hazards Models</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Thorax</subject><subject>Ultrasound</subject><subject>Weight</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMtKxDAUhoMoOo6-gAsJuHFTPbm0SZbDMF5AcKPrmGkTrUwbTdKBeXtTxwu4EHJIOPnOn_AhdELgggCIywjAGBRAcwnKZEF20IRwRgsCku-iCajcFErxA3QY4ysAKMLFPjpgVMqSKj5BT4u1WQ0mtf0zTi8WR7u2oU0b7B02PqS2xrU3oU4Z8T1ux-VMnyIe4jhjepN8lylnTRqCjbizJuZDgzM-f5gdoT1nVtEef-1T9Hi1eJjfFHf317fz2V1RM1GmwpFaKWErZ2zjSlVXcskbySkBbk1DDVHgqoYwAgSEomLJKtEIIUtV8TLbYFN0vs19C_59sDHpro21Xa1Mb_0QNeUcuGC8Ihk9-4O--iH0-XeZklIITvgYSLdUHXyMwTr9FtrOhI0moEf_eutfZ__6078eo0-_oodlZ5ufkW_hGWBbIOar_tmG37f_if0ACuWPQg</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Yu, Yiming</creator><creator>Wang, Yubo</creator><creator>Yang, Maoqing</creator><creator>Huang, Meiping</creator><creator>Li, Jun</creator><creator>Jia, Qianjun</creator><creator>Zhuang, Jian</creator><creator>Huang, Liyu</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>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</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>FR3</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>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6534-2712</orcidid></search><sort><creationdate>20210301</creationdate><title>Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA</title><author>Yu, Yiming ; Wang, Yubo ; Yang, Maoqing ; Huang, Meiping ; Li, Jun ; Jia, Qianjun ; Zhuang, Jian ; Huang, Liyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-f1c997e6faedf59c68b4d842104ead2a190f6d1310107927b367d778596451003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Alternations</topic><topic>Aorta</topic><topic>Aorta - diagnostic imaging</topic><topic>Aortic Coarctation - diagnostic imaging</topic><topic>Area</topic><topic>Cardiac</topic><topic>Congenital diseases</topic><topic>Coronary vessels</topic><topic>Diagnostic Radiology</topic><topic>Diameters</topic><topic>Discriminant analysis</topic><topic>Echocardiography</topic><topic>Evaluation</topic><topic>Humans</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Infant</topic><topic>Infants</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Model accuracy</topic><topic>Neuroradiology</topic><topic>Proportional Hazards Models</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Thorax</topic><topic>Ultrasound</topic><topic>Weight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Yiming</creatorcontrib><creatorcontrib>Wang, Yubo</creatorcontrib><creatorcontrib>Yang, Maoqing</creatorcontrib><creatorcontrib>Huang, Meiping</creatorcontrib><creatorcontrib>Li, Jun</creatorcontrib><creatorcontrib>Jia, Qianjun</creatorcontrib><creatorcontrib>Zhuang, Jian</creatorcontrib><creatorcontrib>Huang, Liyu</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>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</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>Technology Research Database</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>Engineering Research Database</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>Biotechnology and BioEngineering Abstracts</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 radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Yiming</au><au>Wang, Yubo</au><au>Yang, Maoqing</au><au>Huang, Meiping</au><au>Li, Jun</au><au>Jia, Qianjun</au><au>Zhuang, Jian</au><au>Huang, Liyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2021-03-01</date><risdate>2021</risdate><volume>31</volume><issue>3</issue><spage>1216</spage><epage>1226</epage><pages>1216-1226</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA.
Methods
In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression.
Results
Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (
p
< 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (
p
< 0.001), the diameter of the descending aorta at the diaphragmatic level (
p
< 0.001) and weight (
p
= 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation.
Conclusions
CoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered.
Key Points
• CTA is routinely ordered for infants with coarctation of the aorta; however, whether anatomical variations observed with CTA could be used to assess the severity of CoA remains unknown.
• Using the diameter and area of the coarctation site adjusted to body growth as features, the LDA model achieved an accuracy of 88.6% and 90.2% in differentiating between the mild and severe CoA patients in the non-PDA group and PDA group, respectively.
• The narrowest aortic diameter (CoA diameter) indexed to weight has a hazard ratio of 10.29 for re-coarctation.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32885294</pmid><doi>10.1007/s00330-020-07238-1</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6534-2712</orcidid></addata></record> |
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| ispartof | European radiology, 2021-03, Vol.31 (3), p.1216-1226 |
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| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Alternations Aorta Aorta - diagnostic imaging Aortic Coarctation - diagnostic imaging Area Cardiac Congenital diseases Coronary vessels Diagnostic Radiology Diameters Discriminant analysis Echocardiography Evaluation Humans Image reconstruction Imaging Infant Infants Internal Medicine Interventional Radiology Learning algorithms Machine learning Medicine Medicine & Public Health Model accuracy Neuroradiology Proportional Hazards Models Radiology Regression analysis Retrospective Studies Thorax Ultrasound Weight |
| title | Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA |
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