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

Autoři	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
Rok publikování	2023
Druh	Recenzovaný odborný článek
Časopis / Zdroj	Stroke: Vascular and Interventional Neurology
Fakulta / Pracoviště MU	Lékařská fakulta
Citace
www	https://www.ahajournals.org/doi/10.1161/SVIN.122.000525
Doi	https://doi.org/10.1161/SVIN.122.000525
Klíčová slova	deep learning; endovascular therapy; ischemia; machine learning; stroke; thrombus
Popis	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.