Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor

BACKGROUND: Accurate preoperative assessment of surgical difficulty is crucial to the success of the surgery and patient safety. This study aimed to evaluate the difficulty for endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) using multiple machine learning (ML) algorith...

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Detalles Bibliográficos
Autores principales: Liu, Luojie, Zhang, Rufa, Shi, Dongtao, Li, Rui, Wang, Qinghua, Feng, Yunfu, Lu, Fenying, Zong, Yang, Xu, Xiaodan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206233/
https://www.ncbi.nlm.nih.gov/pubmed/37234977
http://dx.doi.org/10.3389/fonc.2023.1190987

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206233/
https://www.ncbi.nlm.nih.gov/pubmed/37234977
http://dx.doi.org/10.3389/fonc.2023.1190987

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