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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Research ArticleARTIFICIAL INTELLIGENCE

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke

Haoyue Zhang, Jennifer S. Polson, Zichen Wang, Kambiz Nael, Neal M. Rao, William F. Speier and Corey W. Arnold
American Journal of Neuroradiology June 2024, DOI: https://doi.org/10.3174/ajnr.A8272
Haoyue Zhang
aFrom the Computational Diagnostics Lab (H.Z., J.S.P., Z.W., W.F.S., C.W.A.), University of California, Los Angeles, California
bDepartment of Bioengineering (H.Z., J.S.P., Z.W., C.W.A.), University of California, Los Angeles, California
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Jennifer S. Polson
aFrom the Computational Diagnostics Lab (H.Z., J.S.P., Z.W., W.F.S., C.W.A.), University of California, Los Angeles, California
bDepartment of Bioengineering (H.Z., J.S.P., Z.W., C.W.A.), University of California, Los Angeles, California
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Zichen Wang
aFrom the Computational Diagnostics Lab (H.Z., J.S.P., Z.W., W.F.S., C.W.A.), University of California, Los Angeles, California
bDepartment of Bioengineering (H.Z., J.S.P., Z.W., C.W.A.), University of California, Los Angeles, California
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Kambiz Nael
cDepartment of Radiology (K.N., W.F.S., C.W.A.), University of California, Los Angeles, California
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  • ORCID record for Kambiz Nael
Neal M. Rao
dDepartment of Neurology (N.M.R.), University of California, Los Angeles, California
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William F. Speier
aFrom the Computational Diagnostics Lab (H.Z., J.S.P., Z.W., W.F.S., C.W.A.), University of California, Los Angeles, California
cDepartment of Radiology (K.N., W.F.S., C.W.A.), University of California, Los Angeles, California
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Corey W. Arnold
aFrom the Computational Diagnostics Lab (H.Z., J.S.P., Z.W., W.F.S., C.W.A.), University of California, Los Angeles, California
bDepartment of Bioengineering (H.Z., J.S.P., Z.W., C.W.A.), University of California, Los Angeles, California
cDepartment of Radiology (K.N., W.F.S., C.W.A.), University of California, Los Angeles, California
eDepartment of Pathology (C.W.A.), University of California, Los Angeles, California
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References

  1. 1.↵
    1. Albers GW,
    2. Marks MP,
    3. Kemp S, et al
    ; DEFUSE 3 Investigators, Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med 2018;378:708–18 doi:10.1056/NEJMoa1713973 pmid:29364767
    CrossRefPubMed
  2. 2.↵
    1. Benjamin EJ,
    2. Muntner P,
    3. Alonso A, et al
    ; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2019 update: a report from the American Heart Association. Circulation 2019;139:e56–528 doi:10.1161/CIR.0000000000000659 pmid:30700139
    CrossRefPubMed
  3. 3.↵
    1. Leibeskind DS,
    2. Jovin TG,
    3. Majoie CB, et al
    . TICI reperfusion in HERMES: Success in endovascular stroke therapy. In: Proceedings of the International Stroke Conference, Feb 21–24, 2017. Houston, Texas. doi:10.1161/str.48.suppl_1.128Stroke
    CrossRef
  4. 4.↵
    1. Dargazanli C,
    2. Consoli A,
    3. Barral M, et al
    . Impact of modified TICI 3 versus modified TICI 2b reperfusion score to predict good outcome following endovascular therapy. AJNR Am J Neuroradiol 2017;38:90–96 doi:10.3174/ajnr.A4968 pmid:27811134
    Abstract/FREE Full Text
  5. 5.↵
    1. Fugate JE,
    2. Klunder AM,
    3. Kallmes DF
    . What is meant by “TICI”? AJNR Am J Neuroradiol 2013;34:1792–97 doi:10.3174/ajnr.A3496 pmid:23578670
    Abstract/FREE Full Text
  6. 6.↵
    1. Leischner H,
    2. Flottmann F,
    3. Hanning U, et al
    . Reasons for failed endovascular recanalization attempts in patients with stroke. J Neurointerv Surg 2019;11:439–42 doi:10.1136/neurintsurg-2018-014060 pmid:30472671
    Abstract/FREE Full Text
  7. 7.↵
    1. Blanc R,
    2. Redjem H,
    3. Ciccio G, et al
    . Predictors of the aspiration component success of a Direct Aspiration First Pass Technique (ADAPT) for the endovascular treatment of stroke reperfusion strategy in anterior circulation acute stroke. Stroke 2017;48:1588–93 doi:10.1161/STROKEAHA.116.016149 pmid:28428348
    Abstract/FREE Full Text
  8. 8.↵
    1. Ducroux C,
    2. Piotin M,
    3. Gory B, et al
    ; ASTER Trial Investigators. First pass effect with contact aspiration and stent retrievers in the Aspiration versus Stent Retriever (ASTER) trial. J Neurointerv Surg 2020;12:386–91 doi:10.1136/neurintsurg-2019-015215 pmid:31471527
    Abstract/FREE Full Text
  9. 9.↵
    1. Flottmann F,
    2. Brekenfeld C,
    3. Broocks G, et al
    ; GSR Investigators. Good clinical outcome decreases with number of retrieval attempts in stroke thrombectomy: beyond the first-pass effect. Stroke 2021;52:482–90 doi:10.1161/STROKEAHA.120.029830 pmid:33467875
    CrossRefPubMed
  10. 10.↵
    1. Shahid AH,
    2. Abbasi M,
    3. Larco JLA, et al
    . Risk factors of futile recanalization following endovascular treatment in patients with large‐vessel occlusion: systematic review and meta‐analysis. Stroke Vascular Interventional Radiology 2022;2:e000257. Accessed January 4, 2023
  11. 11.↵
    1. Goda T,
    2. Oyama N,
    3. Kitano T, et al
    . Factors associated with unsuccessful recanalization in mechanical thrombectomy for acute ischemic stroke. Cerebrovasc Dis Extra 2019;9:107–13 doi:10.1159/000503001 pmid:31563915
    CrossRefPubMed
  12. 12.↵
    1. Heider DM,
    2. Simgen A,
    3. Wagenpfeil G, et al
    . Why we fail: mechanisms and co-factors of unsuccessful thrombectomy in acute ischemic stroke. Neurol Sci 2020;41:1547–55 doi:10.1007/s10072-020-04244-5 pmid:31974796
    CrossRefPubMed
  13. 13.↵
    1. Bang OY,
    2. Saver JL,
    3. Kim SJ, et al
    . Collateral flow predicts response to endovascular therapy for acute ischemic stroke. Stroke 2011;42:693–99 doi:10.1161/STROKEAHA.110.595256 pmid:21233472
    Abstract/FREE Full Text
  14. 14.↵
    1. Mulder ML,
    2. Jansen IG,
    3. Goldhoorn RJ, et al
    ; MR CLEAN Registry Investigators. Time to endovascular treatment and outcome in acute ischemic stroke: MR CLEAN registry results. Circulation 2018;138:232–40 doi:10.1161/CIRCULATIONAHA.117.032600 pmid:29581124
    Abstract/FREE Full Text
  15. 15.↵
    1. Piedade GS,
    2. Schirmer CM,
    3. Goren O, et al
    . Cerebral collateral circulation: a review in the context of ischemic stroke and mechanical thrombectomy. World Neurosurg 2019;122:33–42 doi:10.1016/j.wneu.2018.10.066 pmid:30342266
    CrossRefPubMed
  16. 16.↵
    1. Shuaib A,
    2. Butcher K,
    3. Mohammad AA, et al
    . Collateral blood vessels in acute ischaemic stroke: a potential therapeutic target. Lancet Neurol 2011;10:909–21 doi:10.1016/S1474-4422(11)70195-8 pmid:21939900
    CrossRefPubMedWeb of Science
  17. 17.↵
    1. Powers WJ,
    2. Rabinstein AA,
    3. Ackerson T, et al
    . Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke a guideline for healthcare professionals from the American Heart Association/American Stroke Assoiation. Stroke 2019;50:E344–418 doi:10.1161/STR.0000000000000211 pmid:31662037
    CrossRefPubMed
  18. 18.↵
    1. Velagapudi L,
    2. Mouchtouris N,
    3. Schmidt RF, et al
    . A machine learning approach to first pass reperfusion in mechanical thrombectomy: prediction and feature analysis. J Stroke Cerebrovasc Dis 2021;30:105796 doi:10.1016/j.jstrokecerebrovasdis.2021.105796 pmid:33887664
    CrossRefPubMed
  19. 19.↵
    1. Srivatsa S,
    2. Duan Y,
    3. Sheppard JP, et al
    . Cerebral vessel anatomy as a predictor of first-pass effect in mechanical thrombectomy for emergent large-vessel occlusion. J Neurosurg 2020;134:576–84 doi:10.3171/2019.11.JNS192673 pmid:31978878
    CrossRefPubMed
  20. 20.↵
    1. Velasco Gonzalez A,
    2. Görlich D,
    3. Buerke B, et al
    . Predictors of successful first-pass thrombectomy with a balloon guide catheter: results of a decision tree analysis. Transl Stroke Res 2020;11:900–09 doi:10.1007/s12975-020-00784-2 pmid:32447614
    CrossRefPubMed
  21. 21.↵
    1. Zhang H,
    2. Polson JS,
    3. Nael K, et al
    . A machine learning approach to predict acute ischemic stroke thrombectomy reperfusion using discriminative MR image features, BHI 2021. In: Proeedings of the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Sept 21–24, 2021. Athens, Greece doi:10.1109/BHI50953.2021.9508597
    CrossRef
  22. 22.↵
    1. van Os HJA,
    2. Ramos LA,
    3. Hilbert A, et al
    ; MR CLEAN Registry Investigators. Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Front Neurol 2018;9:784 doi:10.3389/fneur.2018.00784 pmid:30319525
    CrossRefPubMed
  23. 23.↵
    1. Waqas M,
    2. Li W,
    3. Patel TR, et al
    . Clot imaging characteristics predict first-pass effect of aspiration-first approach to thrombectomy. Interv Neuroradiol 2022;28:152–59 doi:10.1177/15910199211019174 pmid:34000868
    CrossRefPubMed
  24. 24.↵
    1. Bala F,
    2. Qiu W,
    3. Zhu K, et al
    ; ESCAPE‐NA1 Investigators. Ability of radiomics versus humans in predicting first‐pass effect after endovascular treatment in the ESCAPE‐NA1 trial. Stroke Vascular and Interventional Neurology 2023;3:e000525 doi:10.1161/SVIN.122.000525
    CrossRef
  25. 25.↵
    1. Hofmeister J,
    2. Bernava G,
    3. Rosi A, et al
    . Clot-based radiomics predict a mechanical thrombectomy strategy for successful recanalization in acute ischemic stroke. Stroke 2020;51:2488–94 doi:10.1161/STROKEAHA.120.030334 pmid:32684141
    CrossRefPubMed
  26. 26.
    1. Qiu W,
    2. Kuang H,
    3. Nair J, et al
    . Radiomics-based intracranial thrombus features on CT and CTA predict recanalization with intravenous alteplase in patients with acute ischemic stroke. AJNR Am J Neuroradiol 2019;40:39–44 doi:10.3174/ajnr.A5918 pmid:30573458
    Abstract/FREE Full Text
  27. 27.↵
    1. Hilbert A,
    2. Ramos LA,
    3. van Os HJ, et al
    . Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke. Comput Biol Med 2019;115:103516 doi:10.1016/j.compbiomed.2019.103516 pmid:31707199
    CrossRefPubMed
  28. 28.↵
    1. Xian Y,
    2. Xu H,
    3. Smith EE, et al
    . Achieving more rapid door-to-needle times and improved outcomes in acute ischemic stroke in a nationwide quality improvement intervention. Stroke 2022;53:1328–38 doi:10.1161/STROKEAHA.121.035853 pmid:34802250
    CrossRefPubMed
  29. 29.↵
    1. Fonarow GC,
    2. Smith EE,
    3. Saver JL, et al
    . Improving door-to-needle times in acute ischemic stroke: the design and rationale for the American Heart Association/American Stroke Association’s Target–Stroke initiative. Stroke 2011;42:2983–89 doi:10.1161/STROKEAHA.111.621342 pmid:21885841
    Abstract/FREE Full Text
  30. 30.↵
    1. Zhang H,
    2. Polson JS,
    3. Nael K, et al
    . Intra-domain task-adaptive transfer learning to determine acute ischemic onset time. Comput Med Imaging Graph 2021;90:101926 doi:10.1016/j.compmedimag.2021.101926 pmid:33934065
    CrossRefPubMed
  31. 31.↵
    1. Avants BB,
    2. Tustison N,
    3. Song G
    . Advanced normalization tools (ANTS). Insight J 2009;2:1–35
  32. 32.↵
    1. Chen X,
    2. He K
    . Exploring simple Siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 20-25, 2021; Nashville, Tennessee
  33. 33.↵
    1. Loshchilov I,
    2. Hutter F
    . Decoupled weight decay regularization 1711.05101. arXiv 2017 https://arxiv.org/abs/1711.05101. Accessed December 10, 2022
  34. 34.↵
    1. Youden WJ
    . Index for rating diagnostic tests. Cancer 1950;3:32–35 doi:10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3 pmid:15405679
    CrossRefPubMedWeb of Science
  35. 35.↵
    1. Goyal N,
    2. Tsivgoulis G,
    3. Frei D, et al
    . Comparative safety and efficacy of modified TICI 2b and TICI 3 reperfusion in acute ischemic strokes treated with mechanical thrombectomy. Clin Neurosurg 2019;84:680–86 doi:10.1093/neuros/nyy097 pmid:29618102
    CrossRefPubMed
  36. 36.↵
    1. Alexandre AM,
    2. Valente I,
    3. Consoli A, et al
    . Posterior circulation endovascular thrombectomy for large-vessel occlusion: predictors of favorable clinical outcome and analysis of first-pass effect. AJNR Am J Neuroradiol 2021;42:896–903 doi:10.3174/ajnr.A7023 pmid:33664106
    Abstract/FREE Full Text
  37. 37.↵
    1. Di Maria F,
    2. Kyheng M,
    3. Consoli A, et al
    ; ETIS Investigators. Identifying the predictors of first-pass effect and its influence on clinical outcome in the setting of endovascular thrombectomy for acute ischemic stroke: results from a multicentric prospective registry. Int J Stroke 2021;16:20–28 doi:10.1177/1747493020923051 pmid:32380902
    CrossRefPubMed
  38. 38.↵
    1. Nikoubashman O,
    2. Dekeyzer S,
    3. Riabikin A, et al
    . True first-pass effect: first-pass complete reperfusion improves clinical outcome in thrombectomy patients with stroke. Stroke 2019;50:2140–46 doi:10.1161/STROKEAHA.119.025148 pmid:31216965
    CrossRefPubMed
  39. 39.↵
    1. Jang KM,
    2. Choi HH,
    3. Nam TK, et al
    . Clinical outcomes of first-pass effect after mechanical thrombectomy for acute ischemic stroke: a systematic review and meta-analysis. Clin Neurol Neurosurg 2021;211:107030 doi:10.1016/j.clineuro.2021.107030 pmid:34823155
    CrossRefPubMed
  40. 40.↵
    1. Jadhav AP,
    2. Desai SM,
    3. Zaidat OO, et al
    . First pass effect with neurothrombectomy for acute ischemic stroke: analysis of the systematic evaluation of patients treated with stroke devices for acute ischemic stroke registry. Stroke 2022;53:e30–32 doi:10.1161/STROKEAHA.121.035457 pmid:34784741
    CrossRefPubMed
  41. 41.↵
    1. Bai X,
    2. Zhang X,
    3. Wang J, et al
    . Factors influencing recanalization after mechanical thrombectomy with first-pass effect for acute ischemic stroke: a systematic review and meta-analysis. Front Neurol 2021;12:628523 doi:10.3389/fneur.2021.628523 pmid:33897591
    CrossRefPubMed
  42. 42.↵
    1. Rohan V,
    2. Baxa J,
    3. Tupy R, et al
    . Length of occlusion predicts recanalization and outcome after intravenous thrombolysis in middle cerebral artery stroke. Stroke 2014;45:2010–17 doi:10.1161/STROKEAHA.114.005731 pmid:24916912
    Abstract/FREE Full Text
  43. 43.↵
    1. Zhang H,
    2. Polson J,
    3. Yang EJ, et al
    . Predicting thrombectomy recanalization from CT imaging using deep learning models, arXiv Feb 8, 2023. https://arxiv.org/abs/2302.04143. Accessed March 15, 2023
  44. 44.↵
    1. Volny O,
    2. Cimflova P,
    3. Szeder V
    . Inter-rater reliability for thrombolysis in cerebral infarction with TICI 2c category. J Stroke Cerebrovasc Dis 2017;26:992–94 doi:10.1016/j.jstrokecerebrovasdis.2016.11.008 pmid:27919793
    CrossRefPubMed
  45. 45.↵
    1. Liebeskind DS,
    2. Bracard S,
    3. Guillemin F, et al
    ; HERMES Collaborators. eTICI reperfusion: defining success in endovascular stroke therapy. J Neurointerv Surg 2019;11:433–38 doi:10.1136/neurintsurg-2018-014127 pmid:30194109
    Abstract/FREE Full Text
  46. 46.↵
    1. Behme D,
    2. Tsogkas I,
    3. Colla R, et al
    . Validation of the extended thrombolysis in cerebral infarction score in a real-world cohort. PLoS One 2019;14:e0210334 doi:10.1371/journal.pone.0210334 pmid:30629664
    CrossRefPubMed
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Haoyue Zhang, Jennifer S. Polson, Zichen Wang, Kambiz Nael, Neal M. Rao, William F. Speier, Corey W. Arnold
A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke
American Journal of Neuroradiology Jun 2024, DOI: 10.3174/ajnr.A8272

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A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke
Haoyue Zhang, Jennifer S. Polson, Zichen Wang, Kambiz Nael, Neal M. Rao, William F. Speier, Corey W. Arnold
American Journal of Neuroradiology Jun 2024, DOI: 10.3174/ajnr.A8272
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