Construction of diagnostic models with machine-learning algorithms for colorectal cancer based on clinical laboratory parameters.

ABSTRACT:

Background

Colonoscopy remains the predominant diagnostic modality for colorectal cancer (CRC), as the diagnostic performance of tumor markers in alone, particularly in the early stages of the disease, is limited. This study sought to develop a diagnostic model for CRC that integrated various laboratory parameters.

Methods

One hundred patients with CRC were assigned to an experimental group while 114 with benign colorectal diseases and 101 healthy individuals were assigned to a control group. The clinical and laboratory data, including the tumor markers such as carcinoembryonic antigen (CEA), glycan carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 242 (CA242), blood count parameters, blood biochemical parameters, and coagulation parameters, were collected for each pa

SUBMITTER: Si D 

PROVIDER: S-EPMC11565110 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature