Construction of a nomogram model based on biomarkers for liver metastasis in non-small cell lung cancer

BackgroundPatients with non-small cell lung cancer (NSCLC) with liver metastasis have a poor prognosis, and there are no reliable biomarkers for predicting disease progression. Currently, no recognized and reliable prediction model exists to anticipate liver metastasis in NSCLC, nor have the risk factors influencing its onset time been thoroughly explored.MethodsThis study conducted a retrospective analysis of 434 NSCLC patients from two hospitals to assess the association between the risk and timing of liver metastasis, as well as several variables.ResultsThe patients were divided into two groups: those without liver metastasis and those with liver metastasis. We constructed a nomogram model for predicting liver metastasis in NSCLC, incorporating elements such as T stage, N stage, M stage, lack of past radical lung cancer surgery, and programmed death ligand 1 (PD-L1) levels. Furthermore, NSCLC patients with wild-type EGFR, no prior therapy with tyrosine kinase inhibitors (TKIs), and no prior radical lung cancer surgery showed an elevated risk of early liver metastasis.ConclusionIn conclusion, the nomogram model developed in this study has the potential to become a simple, intuitive, and customizable clinical tool for assessing the risk of liver metastasis in NSCLC patients following validation. Furthermore, it provides a framework for investigating the timing of metachronous liver metastasis. In this study, we developed a visual graph model to predict the risk of liver metastasis in NSCLC patients, which produced good discrimination and calibration results. Following validation, this nomogram model has the potential to be a useful, intuitive, and individualized clinical tool.image