Construction of artificial neural network (ANN) based on predictive value of prognostic nutritional index (PNI) and neutrophil-to-lymphocyte ratio (NLR) in patients with cervical squamous cell carcinoma

To explore the analytical worth of prognostic nutritional index (PNI) and neutrophil-to-lymphocyte ratio (NLR) in patients with cervical squamous cell carcinoma. The clinical data of 539 patients with cervical cancer in the Affiliated Tumor Hospital of Nantong University from December 2007 to October 2016 were analyzed retrospectively. The ROC is used to select the best cutoff values of PNI and NLR, which are 48.95 and 2.4046. Cox regression analysis was used for univariate and multivariate analysis. Survival differences were assessed by Kaplan-Meier (KM) survival method. Finally, a 3-layer artificial neural network (ANN) model is established. In cervical squamous cell carcinoma, the KM survival curve showed that the overall survival (OS) rate of high-level PNI group was significantly higher than that of low-level PNI group (P < .001), while the OS rate of low-level NLR group was significantly higher than that of high-level NLR group (P = .002). In non-squamous cell carcinoma, there was no significant difference in OS between the 2 groups (P > .005). According to Cox multivariate analysis, preliminary diagnosed PNI and NLR were independent prognostic factors of cervical squamous cell carcinoma (P < .001, P = .008), and pathological type and International Federation of Gynecology and Obstetrics (FIGO) stage also had a certain impact on tumor progression (P = .042, P = .048). The increase of PNI and the decrease of NLR will help patients with cervical squamous cell carcinoma live longer. ANN showed that PNI and NLR were of great importance in predicting survival. Preoperative PNI and NLR are independent predictors of cervical squamous cell carcinoma patients related to clinicopathological features, and have particular value in judging prognosis.