Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling
PurposeTo compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)—FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and FFR derived from coronary CT angiography based on...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
Apr 10 2018
|
| In: |
Radiology
Year: 2018, Jahrgang: 288, Heft: 1, Pages: 64-72 |
| ISSN: | 1527-1315 |
| DOI: | 10.1148/radiol.2018171291 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1148/radiol.2018171291 Verlag, Volltext: https://pubs.rsna.org/doi/10.1148/radiol.2018171291 
|
| Verfasserangaben: | Christian Tesche, Carlo N. De Cecco, Stefan Baumann, Matthias Renker, Tindal W. McLaurin, Taylor M. Duguay, Richard R. Bayer, Daniel H. Steinberg, Katharine L. Grant, Christian Canstein, Chris Schwemmer, Max Schoebinger, Lucian M. Itu, Saikiran Rapaka, Puneet Sharma, U. Joseph Schoepf |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1665360526 | ||
| 003 | DE-627 | ||
| 005 | 20220816151858.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 190514s2018 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1148/radiol.2018171291 |2 doi | |
| 035 | |a (DE-627)1665360526 | ||
| 035 | |a (DE-599)KXP1665360526 | ||
| 035 | |a (OCoLC)1341213026 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 33 |2 sdnb | ||
| 100 | 1 | |a Teschendorf, Christian |d 1966- |e VerfasserIn |0 (DE-588)172929806 |0 (DE-627)697857123 |0 (DE-576)133784150 |4 aut | |
| 245 | 1 | 0 | |a Coronary CT angiography-derived fractional flow reserve |b machine learning algorithm versus computational fluid dynamics modeling |c Christian Tesche, Carlo N. De Cecco, Stefan Baumann, Matthias Renker, Tindal W. McLaurin, Taylor M. Duguay, Richard R. Bayer, Daniel H. Steinberg, Katharine L. Grant, Christian Canstein, Chris Schwemmer, Max Schoebinger, Lucian M. Itu, Saikiran Rapaka, Puneet Sharma, U. Joseph Schoepf |
| 264 | 1 | |c Apr 10 2018 | |
| 300 | |a 9 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Gesehen am 14.05.2019 | ||
| 520 | |a PurposeTo compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)—FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFRML)—against coronary CT angiography and quantitative coronary angiography (QCA).Materials and MethodsA total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFRCFD and FFRML. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard.ResultsOn a per-lesion and per-patient level, FFRML showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFRCFD resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFRML and 0.89 for FFRCFD showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFRML (AUC, 0.91) and FFRCFD (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFRML was significantly shorter compared with that of FFRCFD (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042).ConclusionThe FFRML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFRCFD approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.© RSNA, 2018 | ||
| 700 | 1 | |a Baumann, Stefan |d 1983- |e VerfasserIn |0 (DE-588)1015785875 |0 (DE-627)669896462 |0 (DE-576)351096906 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Radiology |d Oak Brook, Ill. : Soc., 1923 |g 288(2018), 1, Seite 64-72 |h Online-Ressource |w (DE-627)320487253 |w (DE-600)2010588-5 |w (DE-576)094056706 |x 1527-1315 |7 nnas |a Coronary CT angiography-derived fractional flow reserve machine learning algorithm versus computational fluid dynamics modeling |
| 773 | 1 | 8 | |g volume:288 |g year:2018 |g number:1 |g pages:64-72 |g extent:9 |a Coronary CT angiography-derived fractional flow reserve machine learning algorithm versus computational fluid dynamics modeling |
| 856 | 4 | 0 | |u https://doi.org/10.1148/radiol.2018171291 |x Verlag |x Resolving-System |3 Volltext |
| 856 | 4 | 0 | |u https://pubs.rsna.org/doi/10.1148/radiol.2018171291 |x Verlag |3 Volltext |
| 951 | |a AR | ||
| 992 | |a 20190514 | ||
| 993 | |a Article | ||
| 994 | |a 2018 | ||
| 998 | |g 1015785875 |a Baumann, Stefan |m 1015785875:Baumann, Stefan |d 60000 |d 61000 |e 60000PB1015785875 |e 61000PB1015785875 |k 0/60000/ |k 1/60000/61000/ |p 3 | ||
| 999 | |a KXP-PPN1665360526 |e 3475715368 | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"recId":"1665360526","id":{"doi":["10.1148/radiol.2018171291"],"eki":["1665360526"]},"note":["Gesehen am 14.05.2019"],"physDesc":[{"extent":"9 S."}],"person":[{"given":"Christian","family":"Teschendorf","roleDisplay":"VerfasserIn","role":"aut","display":"Teschendorf, Christian"},{"role":"aut","display":"Baumann, Stefan","roleDisplay":"VerfasserIn","given":"Stefan","family":"Baumann"}],"name":{"displayForm":["Christian Tesche, Carlo N. De Cecco, Stefan Baumann, Matthias Renker, Tindal W. McLaurin, Taylor M. Duguay, Richard R. Bayer, Daniel H. Steinberg, Katharine L. Grant, Christian Canstein, Chris Schwemmer, Max Schoebinger, Lucian M. Itu, Saikiran Rapaka, Puneet Sharma, U. Joseph Schoepf"]},"origin":[{"dateIssuedDisp":"Apr 10 2018","dateIssuedKey":"2018"}],"relHost":[{"corporate":[{"role":"isb","display":"Radiological Society of North America","roleDisplay":"Herausgebendes Organ"}],"type":{"media":"Online-Ressource","bibl":"periodical"},"title":[{"title_sort":"Radiology","title":"Radiology"}],"origin":[{"dateIssuedDisp":"1923-","dateIssuedKey":"1923","publisherPlace":"Oak Brook, Ill.","publisher":"Soc."}],"part":{"year":"2018","issue":"1","text":"288(2018), 1, Seite 64-72","extent":"9","volume":"288","pages":"64-72"},"disp":"Coronary CT angiography-derived fractional flow reserve machine learning algorithm versus computational fluid dynamics modelingRadiology","language":["eng"],"physDesc":[{"extent":"Online-Ressource"}],"note":["Fortsetzung der Druck-Ausgabe","Gesehen 07.11.22"],"pubHistory":["1.1923 -"],"name":{"displayForm":["The Radiological Society of North America"]},"id":{"eki":["320487253"],"zdb":["2010588-5"],"issn":["1527-1315"]},"recId":"320487253"}],"title":[{"title_sort":"Coronary CT angiography-derived fractional flow reserve","title":"Coronary CT angiography-derived fractional flow reserve","subtitle":"machine learning algorithm versus computational fluid dynamics modeling"}],"language":["eng"],"type":{"bibl":"article-journal","media":"Online-Ressource"}} | ||
| SRT | |a TESCHENDORCORONARYCT1020 | ||