Predicting colorectal cancer risk: a novel approach using anemia and blood test markers

作者：Zhongqi Zhang, Tianmiao Zhang, Rongcheng Zhang, Xiaonian Zhu, Xiaoyan Wu, Shengkui Tan, Zhiyuan Jian · 发表于：Frontiers in Oncology · 年份：2024 · DOI：10.3389/fonc.2024.1347058 · 被引用次数：8 · 研究领域：Inflammatory Biomarkers in Disease Prognosis、Colorectal Cancer Screening and Detection、Biomarkers in Disease Mechanisms

Background and objectives: Colorectal cancer remains an important public health problem in the context of the COVID-19 (Corona virus disease 2019) pandemic. The decline in detection rates and delayed diagnosis of the disease necessitate the exploration of novel approaches to identify individuals with a heightened risk of developing colorectal cancer. The study aids clinicians in the rational allocation and utilization of healthcare resources, thereby benefiting patients, physicians, and the healthcare system. Methods: The present study retrospectively analyzed the clinical data of colorectal cancer cases diagnosed at the Affiliated Hospital of Guilin Medical University from September 2022 to September 2023, along with a control group. The study employed univariate and multivariate logistic regression as well as LASSO (Least absolute shrinkage and selection operator) regression to screen for predictors of colorectal cancer risk. The optimal predictors were selected based on the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. These predictors were then utilized in constructing a Nomogram Model for predicting colorectal cancer risk. The accuracy of the risk prediction Nomogram Model was assessed through calibration curves, ROC curves, and decision curve analysis (DCA) curves. Results: Clinical data of 719 patients (302 in the case group and 417 in the control group) were included in this study. Based on univariate logistic regression analysis, th...