Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests

Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests

ObjectiveThe recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.MethodsWe employed f...

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Bibliographic Details
Main Authors: Huijie Wang, Xu Cao, Ping Meng, Caihua Zheng, Jinli Liu, Yong Liu, Tianpeng Zhang, Xiaofang Li, Xiaoyang Shi, Xiaoxing Sun, Teng Zhang, Haiying Zuo, Zhichao Wang, Xin Fu, Huan Li, Huanwei Zheng
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Oncology
Subjects:
advanced colorectal adenoma
machine learning
non-invasive test
risk assessment
adjustable thresholds
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2024.1325514/full

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