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

Abstract

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, there is a correlation between Body Mass Index (BMI), red blood cell count (RBC), anemia, Mean Corpuscular Volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), platelet count (PLT), Red Cell Distribution Width-Standard Deviation (RDW-SD), and the incidence of colorectal cancer. Based on the findings of multivariate logistic regression analysis, the variables of BMI and RBC exhibit a decrease, while anemia and PLT demonstrate an increase, all of which are identified as risk factors for the occurrence of colorectal cancer. LASSO regression selected BMI, RBC, anemia, and PLT as prediction factors. LASSO regression and multivariate logistic regression analysis yielded the same results. A nomogram was constructed based on the 4 prediction factors identified by LASSO regression analysis to predict the risk of colorectal cancer. The AUC of the nomogram was 0.751 (95% CI, OR: 0.708-0.793). The calibration curves in the validation and training sets showed good performance, indicating that the constructed nomogram model has good predictive ability. Additionally, the DCA demonstrated that the nomogram model has diagnostic accuracy.

Conclusion: The Nomogram Model offers precise prognostications regarding the likelihood of Colorectal Cancer in patients, thereby helping healthcare professionals in their decision-making processes and promoting the rational categorization of patients as well as the allocation of medical resources.

Keywords: anemia; colorectal cancer; machine learning; nomogram; risk prediction.