Abstract:
Objective To explore the predictive value of immune inflammation combined with liver function hematological indicators for the metastasis of colorectal cancer.
Methods A retrospective analysis of clinical data of 133 patients with colorectal cancer was conducted. The patients were divided into three groups based on disease progression after 24 months of postoperative follow-up: non-metastasis group (n=38), liver metastasis group (n=43), and non-liver distant metastasis group (n=52). The immune inflammatory markers and liver function hematological indicators of progression-free survival were analyzed. Nomogram prediction models were constructed using univariate and multivariate logistic regression analyses to identify risk factors for metastasis of colorectal cancer. The accuracy of the nomogram was validated using receiver operating characteristic (ROC) curve and calibration curve, and the clinical predictive efficacy was evaluated through decision curve and clinical impact curve.
Results Univariate and multivariate logistic regression analyses showed that pan-immune-inflammatory value (PIV), prognostic nutritional index (PNI), and bile acid (BA) were independent predictors of colorectal cancer metastasis. The area under the ROC curve of the combined prediction of metastasis was 0.84; neutrophil/lymphocyte ratio (NLR) and BA were independent predictors of liver metastasis from colorectal cancer. The area under the ROC curve of the combined prediction of liver metastasis was 0.83; PIV and PNI were independent predictive factors for the occurrence of non-liver distant metastasis from colorectal cancer. The area under the ROC curve for the combined prediction of non-liver distant metastasis was 0.83. The calibration curve, decision curve, and clinical impact curve showed that the three models had good accuracy and net benefit value.
Conclusion The nomogram constructed based on immune inflammation and liver function hematological indicators can predict the metastasis of patients with colorectal cancer and has high predictive efficacy and clinical application prospects.
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