Retrospective cohort study develops nomograms predicting prognosis in colorectal cancer, suggesting clinical utility.
Objective Inflammation and immunohematological biomarkers are recognized as contributing factors in the progression of solid tumors. This study aimed to develop nomograms incorporating inflammatory and immunohematological biomarkers to predict the prognosis of colorectal cancer. Methods We retrospectively enrolled 432 patients with colorectal cancer at Yunnan Cancer Hospital from January 2019 to December 2022. Clinicopathologic data and blood samples were obtained at initial diagnosis before any treatment, and therapy was initiated within 3 days. Patients with prior therapy, secondary cancers, or systemic conditions affecting hematology were excluded. Metastases were confirmed by pathology or imaging, and overall survival and progression-free survival were recorded through follow-up. Multivariate logistic and Cox regression analyses, nomogram construction, and internal validation with 1000-bootstrap resampling were performed using R, with risk groups defined by receiver operating characteristic curve–derived cutoffs. Results In the training cohort, the model accuracy for predicting distant metastasis was 66.25%. The concordance index values for the three nomograms were 0.791 (95% confidence interval, 0.745–0.838), 0.752 (95% confidence interval, 0.699–0.806), and 0.687 (95% confidence interval, 0.647–0.726), respectively. Calibration plots showed strong agreement between predicted and observed outcomes, while decision curve analysis indicated potential clinical utility. Subgroup analyses confirmed that the nomograms were effective prognostic indicators across stage I–IV colorectal cancer patients (P < 0.05). Conclusion Single or combined hematological indicators are readily obtainable and feasible and possess high prognostic value. Nomograms based on cancer-associated inflammatory factors and immunohematological biomarkers demonstrated robust accuracy in predicting distant metastasis, overall survival, and progression-free survival in patients with colorectal cancer. These tools may assist clinicians in risk stratification and treatment decision-making, ultimately supporting improved oncological outcomes.
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Tang et al. (2026) studied this question.
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