Ability of Radiomics Versus Humans in Predicting First-Pass
Effect After Endovascular Treatment in the ESCAPE-NA1 Trial

J 2023

Ability of Radiomics Versus Humans in Predicting First-Pass Effect After Endovascular Treatment in the ESCAPE-NA1 Trial

BALA, Fouzi, Wu QIU, Kairan ZHU, Manon KAPPELHOF, Petra CIMFLOVÁ et. al.

Basic information

Original name

Ability of Radiomics Versus Humans in Predicting First-Pass Effect After Endovascular Treatment in the ESCAPE-NA1 Trial

Authors

BALA, Fouzi, Wu QIU, Kairan ZHU, Manon KAPPELHOF, Petra CIMFLOVÁ (203 Czech Republic, belonging to the institution), Beom Joon KIM, Rosalie MCDONOUGH, Nishita SINGH, Nima KASHANI, Jianhai ZHANG, Mohamed NAJM, Johanna M OSPEL, Ankur WADHWA, Raul G NOGUEIRA, Ryan A MCTAGGART, Andrew M DEMCHUK, Alexandre Y POPPE, Charlotte ZERNA, Manish JOSHI, Mohammed A ALMEKHLAFI, Mayank GOYAL, Michael D HILL and Bijoy K MENON

Edition

Stroke: Vascular and Interventional Neurology, Hoboken, Wiley, 2023, 2694-5746

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30210 Clinical neurology

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

RIV identification code

RIV/00216224:14110/23:00133719

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1161/SVIN.122.000525

UT WoS

001162312700004

Keywords in English

deep learning; endovascular therapy; ischemia; machine learning; stroke; thrombus

Abstract

V originále

BACKGROUND: First-pass effect (FPE), that is, achieving reperfusion with a single thrombectomy device pass, is associated with better clinical outcomes in patients with acute stroke. FPE is therefore increasingly used as a marker of device and procedural efficacy. We aimed to evaluate the ability of thrombus-based radiomics models to predict FPE in patients undergoing endovascular thrombectomy and compare performance with experts and nonradiomics thrombus characteristics.

Citovat

BALA, Fouzi, Wu QIU, Kairan ZHU, Manon KAPPELHOF, Petra CIMFLOVÁ, Beom Joon KIM, Rosalie MCDONOUGH, Nishita SINGH, Nima KASHANI, Jianhai ZHANG, Mohamed NAJM, Johanna M OSPEL, Ankur WADHWA, Raul G NOGUEIRA, Ryan A MCTAGGART, Andrew M DEMCHUK, Alexandre Y POPPE, Charlotte ZERNA, Manish JOSHI, Mohammed A ALMEKHLAFI, Mayank GOYAL, Michael D HILL and Bijoy K MENON. Ability of Radiomics Versus Humans in Predicting First-Pass Effect After Endovascular Treatment in the ESCAPE-NA1 Trial. Stroke: Vascular and Interventional Neurology. Hoboken: Wiley, 2023, vol. 3, No 3, p. 1-10. ISSN 2694-5746. Available from: https://dx.doi.org/10.1161/SVIN.122.000525.

@article{2381017,
   author = {Bala, Fouzi and Qiu, Wu and Zhu, Kairan and Kappelhof, Manon and Cimflová, Petra and Kim, Beom Joon and McDonough, Rosalie and Singh, Nishita and Kashani, Nima and Zhang, Jianhai and Najm, Mohamed and Ospel, Johanna M and Wadhwa, Ankur and Nogueira, Raul G and McTaggart, Ryan A and Demchuk, Andrew M and Poppe, Alexandre Y and Zerna, Charlotte and Joshi, Manish and Almekhlafi, Mohammed A and Goyal, Mayank and Hill, Michael D and Menon, Bijoy K},
   article_location = {Hoboken},
   article_number = {3},
   doi = {http://dx.doi.org/10.1161/SVIN.122.000525},
   keywords = {deep learning; endovascular therapy; ischemia; machine learning; stroke; thrombus},
   language = {eng},
   issn = {2694-5746},
   journal = {Stroke: Vascular and Interventional Neurology},
   title = {Ability of Radiomics Versus Humans in Predicting First-Pass Effect After Endovascular Treatment in the ESCAPE-NA1 Trial},
   url = {https://www.ahajournals.org/doi/10.1161/SVIN.122.000525},
   volume = {3},
   year = {2023}
}

TY  - JOUR
ID  - 2381017
AU  - Bala, Fouzi - Qiu, Wu - Zhu, Kairan - Kappelhof, Manon - Cimflová, Petra - Kim, Beom Joon - McDonough, Rosalie - Singh, Nishita - Kashani, Nima - Zhang, Jianhai - Najm, Mohamed - Ospel, Johanna M - Wadhwa, Ankur - Nogueira, Raul G - McTaggart, Ryan A - Demchuk, Andrew M - Poppe, Alexandre Y - Zerna, Charlotte - Joshi, Manish - Almekhlafi, Mohammed A - Goyal, Mayank - Hill, Michael D - Menon, Bijoy K
PY  - 2023
TI  - Ability of Radiomics Versus Humans in Predicting First-Pass Effect After Endovascular Treatment in the ESCAPE-NA1 Trial
JF  - Stroke: Vascular and Interventional Neurology
VL  - 3
IS  - 3
SP  - 1-10
EP  - 1-10
PB  - Wiley
SN  - 26945746
KW  - deep learning
KW  - endovascular therapy
KW  - ischemia
KW  - machine learning
KW  - stroke
KW  - thrombus
UR  - https://www.ahajournals.org/doi/10.1161/SVIN.122.000525
N2  - BACKGROUND: First-pass effect (FPE), that is, achieving reperfusion with a single thrombectomy device pass, is associated with better clinical outcomes in patients with acute stroke. FPE is therefore increasingly used as a marker of device and procedural efficacy. We aimed to evaluate the ability of thrombus-based radiomics models to predict FPE in patients undergoing endovascular thrombectomy and compare performance with experts and nonradiomics thrombus characteristics.
ER  -

BALA, Fouzi, Wu QIU, Kairan ZHU, Manon KAPPELHOF, Petra CIMFLOVÁ, Beom Joon KIM, Rosalie MCDONOUGH, Nishita SINGH, Nima KASHANI, Jianhai ZHANG, Mohamed NAJM, Johanna M OSPEL, Ankur WADHWA, Raul G NOGUEIRA, Ryan A MCTAGGART, Andrew M DEMCHUK, Alexandre Y POPPE, Charlotte ZERNA, Manish JOSHI, Mohammed A ALMEKHLAFI, Mayank GOYAL, Michael D HILL and Bijoy K MENON. Ability of Radiomics Versus Humans in Predicting First-Pass Effect After Endovascular Treatment in the ESCAPE-NA1 Trial. \textit{Stroke: Vascular and Interventional Neurology}. Hoboken: Wiley, 2023, vol.~3, No~3, p.~1-10. ISSN~2694-5746. Available from: https://dx.doi.org/10.1161/SVIN.122.000525.

Detailed Information on Publication Record