CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture
Background Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. The goal was to assess whether aortic calcification distribution could better predict AAA rupture through machine learning and LASSO regression. Methodology In this retrospective study, 80 pati...
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| Veröffentlicht in: | European radiology 2024-06, Vol.34 (6), p.3903-3911 |
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| creator | Mansouri, Mohamed Therasse, Eric Montagnon, Emmanuel Zhan, Ying Olivier Lessard, Simon Roy, Aubert Boucher, Louis-Martin Steinmetz, Oren Aslan, Emre Tang, An Chartrand-Lefebvre, Carl Soulez, Gilles |
| description | Background
Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. The goal was to assess whether aortic calcification distribution could better predict AAA rupture through machine learning and LASSO regression.
Methodology
In this retrospective study, 80 patients treated for a ruptured AAA between January 2001 and August 2018 were matched with 80 non-ruptured patients based on maximal AAA diameter, age, and sex. Calcification volume and dispersion, morphologic, and clinical variables were compared between both groups using a univariable analysis with
p
= 0.05 and multivariable analysis through machine learning and LASSO regression. We used AUC for machine learning and odds ratios for regression to measure performance.
Results
Mean age of patients was 74.0 ± 8.4 years and 89% were men. AAA diameters were equivalent in both groups (80.9 ± 17.5 vs 79.0 ± 17.3 mm,
p
= 0.505). Ruptured aneurysms contained a smaller number of calcification aggregates (18.0 ± 17.9 vs 25.6 ± 18.9,
p
= 0.010) and were less likely to have a proximal neck (45.0% vs 76.3%,
p
|
| doi_str_mv | 10.1007/s00330-023-10429-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2893840627</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3066597636</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-fb78e25175b7f73bf5263b40eaa0fdb23d1a9650bed49db1c0cd9a12cf654d93</originalsourceid><addsrcrecordid>eNp9kEtLAzEYRYMotlb_gAsZcONm9EsyM5kspfiCggjdhzwlZaapycyi_95o6wMXrpKQc--XHITOMVxjAHaTACiFEggtMVSEl_gATXFFST621eGv_QSdpLQCAI4rdowmlHHOGWmn6GW-LORadtvkUxFcIUMcvC607LR3XsvBh3UqhlBsojVeD4VUJvQ-J75QubZj3Ka-iONmGKM9RUdOdsme7dcZWt7fLeeP5eL54Wl-uyg1ZfVQOsVaS2rMasUco8rVpKGqAislOKMINVjypgZlTcWNwhq04RIT7Zq6MpzO0NWudhPD22jTIHqftO26_J4wJkFaTtsKGsIyevkHXYUx5i8kQaFpas4a2mSK7CgdQ0rROrGJvpdxKzCID99i51tk3-LTt8A5dLGvHlVvzXfkS3AG6A5I-Wr9auPP7H9q3wFeUot3</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3066597636</pqid></control><display><type>article</type><title>CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Mansouri, Mohamed ; Therasse, Eric ; Montagnon, Emmanuel ; Zhan, Ying Olivier ; Lessard, Simon ; Roy, Aubert ; Boucher, Louis-Martin ; Steinmetz, Oren ; Aslan, Emre ; Tang, An ; Chartrand-Lefebvre, Carl ; Soulez, Gilles</creator><creatorcontrib>Mansouri, Mohamed ; Therasse, Eric ; Montagnon, Emmanuel ; Zhan, Ying Olivier ; Lessard, Simon ; Roy, Aubert ; Boucher, Louis-Martin ; Steinmetz, Oren ; Aslan, Emre ; Tang, An ; Chartrand-Lefebvre, Carl ; Soulez, Gilles</creatorcontrib><description>Background
Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. The goal was to assess whether aortic calcification distribution could better predict AAA rupture through machine learning and LASSO regression.
Methodology
In this retrospective study, 80 patients treated for a ruptured AAA between January 2001 and August 2018 were matched with 80 non-ruptured patients based on maximal AAA diameter, age, and sex. Calcification volume and dispersion, morphologic, and clinical variables were compared between both groups using a univariable analysis with
p
= 0.05 and multivariable analysis through machine learning and LASSO regression. We used AUC for machine learning and odds ratios for regression to measure performance.
Results
Mean age of patients was 74.0 ± 8.4 years and 89% were men. AAA diameters were equivalent in both groups (80.9 ± 17.5 vs 79.0 ± 17.3 mm,
p
= 0.505). Ruptured aneurysms contained a smaller number of calcification aggregates (18.0 ± 17.9 vs 25.6 ± 18.9,
p
= 0.010) and were less likely to have a proximal neck (45.0% vs 76.3%,
p
< 0.001). In the machine learning analysis, 5 variables were associated to AAA rupture: proximal neck, antiplatelet use, calcification number, Euclidian distance between calcifications, and standard deviation of the Euclidian distance. A follow-up LASSO regression was concomitant with the findings of the machine learning analysis regarding calcification dispersion but discordant on calcification number.
Conclusion
There might be more to AAA calcifications that what is known in the present literature. We need larger prospective studies to investigate if indeed, calcification dispersion affects rupture risk.
Clinical relevance statement
Ruptured aneurysms are possibly more likely to have their calcification volume concentrated in a smaller geographical area.
Key Points
• Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved.
• For a given calcification volume, AAAs with well-distributed calcification clusters could be less likely to rupture.
• A machine learning model including AAA calcifications better predicts rupture compared to a model based solely on maximal diameter and sex alone, although it might be prone to overfitting.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-023-10429-1</identifier><identifier>PMID: 37999728</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Abdomen ; Aged ; Aged, 80 and over ; Aorta ; Aorta, Abdominal - diagnostic imaging ; Aorta, Abdominal - pathology ; Aortic Aneurysm, Abdominal - complications ; Aortic Aneurysm, Abdominal - diagnostic imaging ; Aortic aneurysms ; Aortic Rupture - diagnostic imaging ; Aortic Rupture - etiology ; Calcification ; Calcification (ectopic) ; Diagnostic Radiology ; Diameters ; Female ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Learning algorithms ; Machine Learning ; Male ; Medicine ; Medicine & Public Health ; Neuroradiology ; Predictive Value of Tests ; Radiology ; Regression ; Retrospective Studies ; Rupture ; Rupturing ; Sex ; Tomography, X-Ray Computed - methods ; Ultrasound ; Vascular Calcification - complications ; Vascular Calcification - diagnostic imaging ; Vascular-Interventional</subject><ispartof>European radiology, 2024-06, Vol.34 (6), p.3903-3911</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to European Society of Radiology.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-fb78e25175b7f73bf5263b40eaa0fdb23d1a9650bed49db1c0cd9a12cf654d93</citedby><cites>FETCH-LOGICAL-c375t-fb78e25175b7f73bf5263b40eaa0fdb23d1a9650bed49db1c0cd9a12cf654d93</cites><orcidid>0000-0002-5566-3552</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/s00330-023-10429-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-023-10429-1$$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/37999728$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mansouri, Mohamed</creatorcontrib><creatorcontrib>Therasse, Eric</creatorcontrib><creatorcontrib>Montagnon, Emmanuel</creatorcontrib><creatorcontrib>Zhan, Ying Olivier</creatorcontrib><creatorcontrib>Lessard, Simon</creatorcontrib><creatorcontrib>Roy, Aubert</creatorcontrib><creatorcontrib>Boucher, Louis-Martin</creatorcontrib><creatorcontrib>Steinmetz, Oren</creatorcontrib><creatorcontrib>Aslan, Emre</creatorcontrib><creatorcontrib>Tang, An</creatorcontrib><creatorcontrib>Chartrand-Lefebvre, Carl</creatorcontrib><creatorcontrib>Soulez, Gilles</creatorcontrib><title>CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Background
Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. The goal was to assess whether aortic calcification distribution could better predict AAA rupture through machine learning and LASSO regression.
Methodology
In this retrospective study, 80 patients treated for a ruptured AAA between January 2001 and August 2018 were matched with 80 non-ruptured patients based on maximal AAA diameter, age, and sex. Calcification volume and dispersion, morphologic, and clinical variables were compared between both groups using a univariable analysis with
p
= 0.05 and multivariable analysis through machine learning and LASSO regression. We used AUC for machine learning and odds ratios for regression to measure performance.
Results
Mean age of patients was 74.0 ± 8.4 years and 89% were men. AAA diameters were equivalent in both groups (80.9 ± 17.5 vs 79.0 ± 17.3 mm,
p
= 0.505). Ruptured aneurysms contained a smaller number of calcification aggregates (18.0 ± 17.9 vs 25.6 ± 18.9,
p
= 0.010) and were less likely to have a proximal neck (45.0% vs 76.3%,
p
< 0.001). In the machine learning analysis, 5 variables were associated to AAA rupture: proximal neck, antiplatelet use, calcification number, Euclidian distance between calcifications, and standard deviation of the Euclidian distance. A follow-up LASSO regression was concomitant with the findings of the machine learning analysis regarding calcification dispersion but discordant on calcification number.
Conclusion
There might be more to AAA calcifications that what is known in the present literature. We need larger prospective studies to investigate if indeed, calcification dispersion affects rupture risk.
Clinical relevance statement
Ruptured aneurysms are possibly more likely to have their calcification volume concentrated in a smaller geographical area.
Key Points
• Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved.
• For a given calcification volume, AAAs with well-distributed calcification clusters could be less likely to rupture.
• A machine learning model including AAA calcifications better predicts rupture compared to a model based solely on maximal diameter and sex alone, although it might be prone to overfitting.</description><subject>Abdomen</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aorta</subject><subject>Aorta, Abdominal - diagnostic imaging</subject><subject>Aorta, Abdominal - pathology</subject><subject>Aortic Aneurysm, Abdominal - complications</subject><subject>Aortic Aneurysm, Abdominal - diagnostic imaging</subject><subject>Aortic aneurysms</subject><subject>Aortic Rupture - diagnostic imaging</subject><subject>Aortic Rupture - etiology</subject><subject>Calcification</subject><subject>Calcification (ectopic)</subject><subject>Diagnostic Radiology</subject><subject>Diameters</subject><subject>Female</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Predictive Value of Tests</subject><subject>Radiology</subject><subject>Regression</subject><subject>Retrospective Studies</subject><subject>Rupture</subject><subject>Rupturing</subject><subject>Sex</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Ultrasound</subject><subject>Vascular Calcification - complications</subject><subject>Vascular Calcification - diagnostic imaging</subject><subject>Vascular-Interventional</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEtLAzEYRYMotlb_gAsZcONm9EsyM5kspfiCggjdhzwlZaapycyi_95o6wMXrpKQc--XHITOMVxjAHaTACiFEggtMVSEl_gATXFFST621eGv_QSdpLQCAI4rdowmlHHOGWmn6GW-LORadtvkUxFcIUMcvC607LR3XsvBh3UqhlBsojVeD4VUJvQ-J75QubZj3Ka-iONmGKM9RUdOdsme7dcZWt7fLeeP5eL54Wl-uyg1ZfVQOsVaS2rMasUco8rVpKGqAislOKMINVjypgZlTcWNwhq04RIT7Zq6MpzO0NWudhPD22jTIHqftO26_J4wJkFaTtsKGsIyevkHXYUx5i8kQaFpas4a2mSK7CgdQ0rROrGJvpdxKzCID99i51tk3-LTt8A5dLGvHlVvzXfkS3AG6A5I-Wr9auPP7H9q3wFeUot3</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Mansouri, Mohamed</creator><creator>Therasse, Eric</creator><creator>Montagnon, Emmanuel</creator><creator>Zhan, Ying Olivier</creator><creator>Lessard, Simon</creator><creator>Roy, Aubert</creator><creator>Boucher, Louis-Martin</creator><creator>Steinmetz, Oren</creator><creator>Aslan, Emre</creator><creator>Tang, An</creator><creator>Chartrand-Lefebvre, Carl</creator><creator>Soulez, Gilles</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>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5566-3552</orcidid></search><sort><creationdate>20240601</creationdate><title>CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture</title><author>Mansouri, Mohamed ; Therasse, Eric ; Montagnon, Emmanuel ; Zhan, Ying Olivier ; Lessard, Simon ; Roy, Aubert ; Boucher, Louis-Martin ; Steinmetz, Oren ; Aslan, Emre ; Tang, An ; Chartrand-Lefebvre, Carl ; Soulez, Gilles</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-fb78e25175b7f73bf5263b40eaa0fdb23d1a9650bed49db1c0cd9a12cf654d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Abdomen</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aorta</topic><topic>Aorta, Abdominal - diagnostic imaging</topic><topic>Aorta, Abdominal - pathology</topic><topic>Aortic Aneurysm, Abdominal - complications</topic><topic>Aortic Aneurysm, Abdominal - diagnostic imaging</topic><topic>Aortic aneurysms</topic><topic>Aortic Rupture - diagnostic imaging</topic><topic>Aortic Rupture - etiology</topic><topic>Calcification</topic><topic>Calcification (ectopic)</topic><topic>Diagnostic Radiology</topic><topic>Diameters</topic><topic>Female</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Predictive Value of Tests</topic><topic>Radiology</topic><topic>Regression</topic><topic>Retrospective Studies</topic><topic>Rupture</topic><topic>Rupturing</topic><topic>Sex</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>Ultrasound</topic><topic>Vascular Calcification - complications</topic><topic>Vascular Calcification - diagnostic imaging</topic><topic>Vascular-Interventional</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mansouri, Mohamed</creatorcontrib><creatorcontrib>Therasse, Eric</creatorcontrib><creatorcontrib>Montagnon, Emmanuel</creatorcontrib><creatorcontrib>Zhan, Ying Olivier</creatorcontrib><creatorcontrib>Lessard, Simon</creatorcontrib><creatorcontrib>Roy, Aubert</creatorcontrib><creatorcontrib>Boucher, Louis-Martin</creatorcontrib><creatorcontrib>Steinmetz, Oren</creatorcontrib><creatorcontrib>Aslan, Emre</creatorcontrib><creatorcontrib>Tang, An</creatorcontrib><creatorcontrib>Chartrand-Lefebvre, Carl</creatorcontrib><creatorcontrib>Soulez, Gilles</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>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mansouri, Mohamed</au><au>Therasse, Eric</au><au>Montagnon, Emmanuel</au><au>Zhan, Ying Olivier</au><au>Lessard, Simon</au><au>Roy, Aubert</au><au>Boucher, Louis-Martin</au><au>Steinmetz, Oren</au><au>Aslan, Emre</au><au>Tang, An</au><au>Chartrand-Lefebvre, Carl</au><au>Soulez, Gilles</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>34</volume><issue>6</issue><spage>3903</spage><epage>3911</epage><pages>3903-3911</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Background
Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. The goal was to assess whether aortic calcification distribution could better predict AAA rupture through machine learning and LASSO regression.
Methodology
In this retrospective study, 80 patients treated for a ruptured AAA between January 2001 and August 2018 were matched with 80 non-ruptured patients based on maximal AAA diameter, age, and sex. Calcification volume and dispersion, morphologic, and clinical variables were compared between both groups using a univariable analysis with
p
= 0.05 and multivariable analysis through machine learning and LASSO regression. We used AUC for machine learning and odds ratios for regression to measure performance.
Results
Mean age of patients was 74.0 ± 8.4 years and 89% were men. AAA diameters were equivalent in both groups (80.9 ± 17.5 vs 79.0 ± 17.3 mm,
p
= 0.505). Ruptured aneurysms contained a smaller number of calcification aggregates (18.0 ± 17.9 vs 25.6 ± 18.9,
p
= 0.010) and were less likely to have a proximal neck (45.0% vs 76.3%,
p
< 0.001). In the machine learning analysis, 5 variables were associated to AAA rupture: proximal neck, antiplatelet use, calcification number, Euclidian distance between calcifications, and standard deviation of the Euclidian distance. A follow-up LASSO regression was concomitant with the findings of the machine learning analysis regarding calcification dispersion but discordant on calcification number.
Conclusion
There might be more to AAA calcifications that what is known in the present literature. We need larger prospective studies to investigate if indeed, calcification dispersion affects rupture risk.
Clinical relevance statement
Ruptured aneurysms are possibly more likely to have their calcification volume concentrated in a smaller geographical area.
Key Points
• Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved.
• For a given calcification volume, AAAs with well-distributed calcification clusters could be less likely to rupture.
• A machine learning model including AAA calcifications better predicts rupture compared to a model based solely on maximal diameter and sex alone, although it might be prone to overfitting.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37999728</pmid><doi>10.1007/s00330-023-10429-1</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-5566-3552</orcidid></addata></record> |
| fulltext | fulltext |
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| language | eng |
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| subjects | Abdomen Aged Aged, 80 and over Aorta Aorta, Abdominal - diagnostic imaging Aorta, Abdominal - pathology Aortic Aneurysm, Abdominal - complications Aortic Aneurysm, Abdominal - diagnostic imaging Aortic aneurysms Aortic Rupture - diagnostic imaging Aortic Rupture - etiology Calcification Calcification (ectopic) Diagnostic Radiology Diameters Female Humans Imaging Internal Medicine Interventional Radiology Learning algorithms Machine Learning Male Medicine Medicine & Public Health Neuroradiology Predictive Value of Tests Radiology Regression Retrospective Studies Rupture Rupturing Sex Tomography, X-Ray Computed - methods Ultrasound Vascular Calcification - complications Vascular Calcification - diagnostic imaging Vascular-Interventional |
| title | CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture |
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