The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?
Objectives The present study aimed to compare the diagnostic performance of a machine learning (ML)–based FFR CT algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR ICA . Methods...
Gespeichert in:
| Veröffentlicht in: | European radiology 2019-07, Vol.29 (7), p.3647-3657 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3657 |
|---|---|
| container_issue | 7 |
| container_start_page | 3647 |
| container_title | European radiology |
| container_volume | 29 |
| creator | Yu, Mengmeng Lu, Zhigang Shen, Chengxing Yan, Jing Wang, Yining Lu, Bin Zhang, Jiayin |
| description | Objectives
The present study aimed to compare the diagnostic performance of a machine learning (ML)–based FFR
CT
algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR
ICA
.
Methods
Patients who underwent both CCTA and FFR
ICA
measurement within 2 weeks were retrospectively included. ML-based FFR
CT
, volume of subtended myocardium (V
sub
), percentage of subtended myocardium volume versus total myocardium volume (V
ratio
), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFR
ICA
≤ 0.8 were considered to be functionally significant.
Results
One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, V
sub
, V
ratio
, V
ratio
/MLD, V
ratio
/MLA, and LL/MLD
4
were all significantly longer or larger in the group of FFR
ICA
≤ 0.8 while smaller minimal lumen area, MLD, and FFR
CT
value were noted. The AUC of FFR
CT
+ V
ratio
/MLD was significantly better than that of FFR
CT
alone (0.935 versus 0.873,
p
|
| doi_str_mv | 10.1007/s00330-019-06139-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2196528610</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2195743535</sourcerecordid><originalsourceid>FETCH-LOGICAL-c418t-e477f0937706aed0b2a686c5a27e0ed1ab89ab20b8b49925f4e6be3a146197193</originalsourceid><addsrcrecordid>eNp9kc-KFDEQxoMoOI6-gKeAFw-btfKnOx0vIoOzCguCjOeQTldPZ-3ujEn3wt58AG--oU9i1hEED1KHqsPv-6iqj5DnHC45gH6VAaQEBtwwqLk0TDwgG66kYBwa9ZBswMiGaWPUY_Ik5xsAMFzpDfl-GJC2mBd6StgFv8REY09D9gNOwVMfU5xduqN5wTnmkF_TvLZl7rCj0130LnXBjfQ2juuEF3Ryfggz0hFdmsN8_PntR-tyYff7T7vDBS32QzgOLIX8hZ5G93VF2qNb1oT5zVPyqHdjxmd_-pZ83r877N6z649XH3Zvr5lXvFkYKq37cpDWUDvsoBWubmpfOaERsOOubYxrBbRNq4wRVa-wblE6rmpuNDdyS16efU8plgXyYqdyMI6jmzGu2Qpu6ko0NYeCvvgHvYlrmst291SllaxKbYk4Uz7FnBP29pTCVN5mOdj7gOw5IFsCsr8DsqKI5FmUCzwfMf21_o_qF7vilXA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2195743535</pqid></control><display><type>article</type><title>The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?</title><source>SpringerLink Journals</source><creator>Yu, Mengmeng ; Lu, Zhigang ; Shen, Chengxing ; Yan, Jing ; Wang, Yining ; Lu, Bin ; Zhang, Jiayin</creator><creatorcontrib>Yu, Mengmeng ; Lu, Zhigang ; Shen, Chengxing ; Yan, Jing ; Wang, Yining ; Lu, Bin ; Zhang, Jiayin</creatorcontrib><description>Objectives
The present study aimed to compare the diagnostic performance of a machine learning (ML)–based FFR
CT
algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR
ICA
.
Methods
Patients who underwent both CCTA and FFR
ICA
measurement within 2 weeks were retrospectively included. ML-based FFR
CT
, volume of subtended myocardium (V
sub
), percentage of subtended myocardium volume versus total myocardium volume (V
ratio
), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFR
ICA
≤ 0.8 were considered to be functionally significant.
Results
One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, V
sub
, V
ratio
, V
ratio
/MLD, V
ratio
/MLA, and LL/MLD
4
were all significantly longer or larger in the group of FFR
ICA
≤ 0.8 while smaller minimal lumen area, MLD, and FFR
CT
value were noted. The AUC of FFR
CT
+ V
ratio
/MLD was significantly better than that of FFR
CT
alone (0.935 versus 0.873,
p
< 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. V
ratio
/MLD-complemented ML-based FFR
CT
for “gray zone” lesions with FFR
CT
value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208).
Conclusions
ML-based FFR
CT
simulation and V
ratio
/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. V
ratio
/MLD is more accurate than ML-based FFR
CT
for lesions with simulated FFR
CT
value from 0.7 to 0.8.
Key Points
• Machine learning–based FFR
CT
and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis.
• Subtended myocardium volume was more accurate than machine learning–based FFR
CT
for “gray zone” lesions with simulated FFR value from 0.7 to 0.8.
• CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-019-06139-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial intelligence ; Cardiac ; Computer simulation ; Diagnostic Radiology ; Diagnostic systems ; Imaging ; Internal Medicine ; Interventional Radiology ; Ischemia ; Learning algorithms ; Lesions ; Machine learning ; Medicine ; Medicine & Public Health ; Myocardium ; Neuroradiology ; Parameters ; Patients ; Performance prediction ; Radiology ; Risk ; Stenosis ; Ultrasound</subject><ispartof>European radiology, 2019-07, Vol.29 (7), p.3647-3657</ispartof><rights>European Society of Radiology 2019</rights><rights>European Radiology is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-e477f0937706aed0b2a686c5a27e0ed1ab89ab20b8b49925f4e6be3a146197193</citedby><cites>FETCH-LOGICAL-c418t-e477f0937706aed0b2a686c5a27e0ed1ab89ab20b8b49925f4e6be3a146197193</cites><orcidid>0000-0001-7383-7571</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/s00330-019-06139-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-019-06139-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Yu, Mengmeng</creatorcontrib><creatorcontrib>Lu, Zhigang</creatorcontrib><creatorcontrib>Shen, Chengxing</creatorcontrib><creatorcontrib>Yan, Jing</creatorcontrib><creatorcontrib>Wang, Yining</creatorcontrib><creatorcontrib>Lu, Bin</creatorcontrib><creatorcontrib>Zhang, Jiayin</creatorcontrib><title>The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><description>Objectives
The present study aimed to compare the diagnostic performance of a machine learning (ML)–based FFR
CT
algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR
ICA
.
Methods
Patients who underwent both CCTA and FFR
ICA
measurement within 2 weeks were retrospectively included. ML-based FFR
CT
, volume of subtended myocardium (V
sub
), percentage of subtended myocardium volume versus total myocardium volume (V
ratio
), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFR
ICA
≤ 0.8 were considered to be functionally significant.
Results
One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, V
sub
, V
ratio
, V
ratio
/MLD, V
ratio
/MLA, and LL/MLD
4
were all significantly longer or larger in the group of FFR
ICA
≤ 0.8 while smaller minimal lumen area, MLD, and FFR
CT
value were noted. The AUC of FFR
CT
+ V
ratio
/MLD was significantly better than that of FFR
CT
alone (0.935 versus 0.873,
p
< 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. V
ratio
/MLD-complemented ML-based FFR
CT
for “gray zone” lesions with FFR
CT
value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208).
Conclusions
ML-based FFR
CT
simulation and V
ratio
/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. V
ratio
/MLD is more accurate than ML-based FFR
CT
for lesions with simulated FFR
CT
value from 0.7 to 0.8.
Key Points
• Machine learning–based FFR
CT
and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis.
• Subtended myocardium volume was more accurate than machine learning–based FFR
CT
for “gray zone” lesions with simulated FFR value from 0.7 to 0.8.
• CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Cardiac</subject><subject>Computer simulation</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Ischemia</subject><subject>Learning algorithms</subject><subject>Lesions</subject><subject>Machine learning</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Myocardium</subject><subject>Neuroradiology</subject><subject>Parameters</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Radiology</subject><subject>Risk</subject><subject>Stenosis</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kc-KFDEQxoMoOI6-gKeAFw-btfKnOx0vIoOzCguCjOeQTldPZ-3ujEn3wt58AG--oU9i1hEED1KHqsPv-6iqj5DnHC45gH6VAaQEBtwwqLk0TDwgG66kYBwa9ZBswMiGaWPUY_Ik5xsAMFzpDfl-GJC2mBd6StgFv8REY09D9gNOwVMfU5xduqN5wTnmkF_TvLZl7rCj0130LnXBjfQ2juuEF3Ryfggz0hFdmsN8_PntR-tyYff7T7vDBS32QzgOLIX8hZ5G93VF2qNb1oT5zVPyqHdjxmd_-pZ83r877N6z649XH3Zvr5lXvFkYKq37cpDWUDvsoBWubmpfOaERsOOubYxrBbRNq4wRVa-wblE6rmpuNDdyS16efU8plgXyYqdyMI6jmzGu2Qpu6ko0NYeCvvgHvYlrmst291SllaxKbYk4Uz7FnBP29pTCVN5mOdj7gOw5IFsCsr8DsqKI5FmUCzwfMf21_o_qF7vilXA</recordid><startdate>20190701</startdate><enddate>20190701</enddate><creator>Yu, Mengmeng</creator><creator>Lu, Zhigang</creator><creator>Shen, Chengxing</creator><creator>Yan, Jing</creator><creator>Wang, Yining</creator><creator>Lu, Bin</creator><creator>Zhang, Jiayin</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><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/0000-0001-7383-7571</orcidid></search><sort><creationdate>20190701</creationdate><title>The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?</title><author>Yu, Mengmeng ; Lu, Zhigang ; Shen, Chengxing ; Yan, Jing ; Wang, Yining ; Lu, Bin ; Zhang, Jiayin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-e477f0937706aed0b2a686c5a27e0ed1ab89ab20b8b49925f4e6be3a146197193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Cardiac</topic><topic>Computer simulation</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Ischemia</topic><topic>Learning algorithms</topic><topic>Lesions</topic><topic>Machine learning</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Myocardium</topic><topic>Neuroradiology</topic><topic>Parameters</topic><topic>Patients</topic><topic>Performance prediction</topic><topic>Radiology</topic><topic>Risk</topic><topic>Stenosis</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Mengmeng</creatorcontrib><creatorcontrib>Lu, Zhigang</creatorcontrib><creatorcontrib>Shen, Chengxing</creatorcontrib><creatorcontrib>Yan, Jing</creatorcontrib><creatorcontrib>Wang, Yining</creatorcontrib><creatorcontrib>Lu, Bin</creatorcontrib><creatorcontrib>Zhang, Jiayin</creatorcontrib><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, Mengmeng</au><au>Lu, Zhigang</au><au>Shen, Chengxing</au><au>Yan, Jing</au><au>Wang, Yining</au><au>Lu, Bin</au><au>Zhang, Jiayin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features?</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><date>2019-07-01</date><risdate>2019</risdate><volume>29</volume><issue>7</issue><spage>3647</spage><epage>3657</epage><pages>3647-3657</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
The present study aimed to compare the diagnostic performance of a machine learning (ML)–based FFR
CT
algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR
ICA
.
Methods
Patients who underwent both CCTA and FFR
ICA
measurement within 2 weeks were retrospectively included. ML-based FFR
CT
, volume of subtended myocardium (V
sub
), percentage of subtended myocardium volume versus total myocardium volume (V
ratio
), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFR
ICA
≤ 0.8 were considered to be functionally significant.
Results
One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, V
sub
, V
ratio
, V
ratio
/MLD, V
ratio
/MLA, and LL/MLD
4
were all significantly longer or larger in the group of FFR
ICA
≤ 0.8 while smaller minimal lumen area, MLD, and FFR
CT
value were noted. The AUC of FFR
CT
+ V
ratio
/MLD was significantly better than that of FFR
CT
alone (0.935 versus 0.873,
p
< 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. V
ratio
/MLD-complemented ML-based FFR
CT
for “gray zone” lesions with FFR
CT
value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208).
Conclusions
ML-based FFR
CT
simulation and V
ratio
/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. V
ratio
/MLD is more accurate than ML-based FFR
CT
for lesions with simulated FFR
CT
value from 0.7 to 0.8.
Key Points
• Machine learning–based FFR
CT
and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis.
• Subtended myocardium volume was more accurate than machine learning–based FFR
CT
for “gray zone” lesions with simulated FFR value from 0.7 to 0.8.
• CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00330-019-06139-2</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-7383-7571</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0938-7994 |
| ispartof | European radiology, 2019-07, Vol.29 (7), p.3647-3657 |
| issn | 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2196528610 |
| source | SpringerLink Journals |
| subjects | Algorithms Artificial intelligence Cardiac Computer simulation Diagnostic Radiology Diagnostic systems Imaging Internal Medicine Interventional Radiology Ischemia Learning algorithms Lesions Machine learning Medicine Medicine & Public Health Myocardium Neuroradiology Parameters Patients Performance prediction Radiology Risk Stenosis Ultrasound |
| title | The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning–based FFRCT, or high-risk plaque features? |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T07%3A46%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20best%20predictor%20of%20ischemic%20coronary%20stenosis:%20subtended%20myocardial%20volume,%20machine%20learning%E2%80%93based%20FFRCT,%20or%20high-risk%20plaque%20features?&rft.jtitle=European%20radiology&rft.au=Yu,%20Mengmeng&rft.date=2019-07-01&rft.volume=29&rft.issue=7&rft.spage=3647&rft.epage=3657&rft.pages=3647-3657&rft.issn=0938-7994&rft.eissn=1432-1084&rft_id=info:doi/10.1007/s00330-019-06139-2&rft_dat=%3Cproquest_cross%3E2195743535%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2195743535&rft_id=info:pmid/&rfr_iscdi=true |