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

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

BackgroundAccurate 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) algorithms...

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Bibliographic Details
Main Authors: Luojie Liu, Rufa Zhang, Dongtao Shi, Rui Li, Qinghua Wang, Yunfu Feng, Fenying Lu, Yang Zong, Xiaodan Xu
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-05-01
Series:Frontiers in Oncology
Subjects:
automated machine learning
predictive models
endoscopic resection
gastrointestinal stromal tumors
difficulty
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2023.1190987/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2023.1190987/full

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