Establishment of Nomogram Model for Minimally Invasive Treatment of Small Hepatocellular Carcinoma Based on CD8(+)T Cell Counts

PURPOSE: Minimally invasive treatment of small hepatocellular carcinoma (HCC) is the main way of treatment, which can cause the change of HCC immune microenvironment. T lymphocytes are an important part of the immune microenvironment and may be powerful predictors of prognosis. The purpose of this study was to explore the effect of T lymphocytes on the prognosis of HCC and establish a prognostic model. PATIENTS AND METHODS: We conducted a retrospective study of 300 patients with small HCC and developed a clinical prediction model. The selection of modeling variables was performed by combining backward stepwise Cox regression using Akaike’s Information Criteria (AIC) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Establish a dynamic nomogram model to predict 1-, 2-, and 3-year overall survival (OS). Receiver operating characteristic curve (ROC curve) was used to verify the model discriminative ability, calibration curve was used to examine the model calibration ability, and decision curve analysis (DCA) was used to evaluate the clinical value. RESULTS: The nomogram to predict the OS of small HCC includes the following four variables: aspartate aminotransferase (AST), alpha fetoprotein (AFP), C-reactive protein (CRP) and CD8(+)T cell counts, represented liver function index, tumor-related index, Inflammatory index and immune-related index, respectively. The area under the receiver operating characteristic curves (AUC) of predicting 1-, 2-, and 3-year overall survival were 0.846, 0.824 and 0.812, and the model was excellent in discrimination, calibration and clinical applicability. CONCLUSION: Our study provides a nomogram based on CD8(+)T cell counts that can help predict the prognosis of small HCC after minimally invasive treatment, which suggests that T lymphocytes can be used as a prognostic factor for HCC. Larger trials are needed to verify our results.