Informace o publikaci

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

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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 Článek v odborném periodiku
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 http://dx.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.

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