Prediction of survival and analysis of prognostic factors for patients with AFP negative hepatocellular carcinoma: a population-based study

Author:

Liu Chengyu,Li Zikang,Zhang Zhilei,Li Jinlong,Xu Congxi,Jia Yuming,Zhang Chong,Yang Wuhan,Wang Wenchuan,Wang Xiaojuan,Liang Kuopeng,Peng Li,Wang Jitao

Abstract

Abstract Purpose Hepatocellular carcinoma (HCC) has a poor prognosis, and alpha-fetoprotein (AFP) is widely used to evaluate HCC. However, the proportion of AFP-negative individuals cannot be disregarded. This study aimed to establish a nomogram of risk factors affecting the prognosis of patients with AFP-negative HCC and to evaluate its diagnostic efficiency. Patients and methods Data from patients with AFP-negative initial diagnosis of HCC (ANHC) between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and validation. We randomly divided overall cohort into the training or validation cohort (7:3). Univariate and multivariate Cox regression analysis were used to identify the risk factors. We constructed nomograms with overall survival (OS) and cancer-specific survival (CSS) as clinical endpoint events and constructed survival analysis by using Kaplan-Meier curve. Also, we conducted internal validation with Receiver Operating Characteristic (ROC) analysis and Decision curve analysis (DCA) to validate the clinical value of the model. Results This study included 1811 patients (1409 men; 64.7% were Caucasian; the average age was 64 years; 60.7% were married). In the multivariate analysis, the independent risk factors affecting prognosis were age, ethnicity, year of diagnosis, tumor size, tumor grade, surgery, chemotherapy, and radiotherapy. The nomogram-based model related C-indexes were 0.762 (95% confidence interval (CI): 0.752–0.772) and 0.752 (95% CI: 0.740–0.769) for predicting OS, and 0.785 (95% CI: 0.774–0.795) and 0.779 (95% CI: 0.762–0.795) for predicting CSS. The nomogram model showed that the predicted death was consistent with the actual value. The ROC analysis and DCA showed that the nomogram had good clinical value compared with TNM staging. Conclusion The age(HR:1.012, 95% CI: 1.006–1.018, P-value < 0.001), ethnicity(African-American: HR:0.946, 95% CI: 0.783–1.212, P-value: 0.66; Others: HR:0.737, 95% CI: 0.613–0.887, P-value: 0.001), tumor diameter(HR:1.006, 95% CI: 1.004–1.008, P-value < 0.001), year of diagnosis (HR:0.852, 95% CI: 0.729–0.997, P-value: 0.046), tumor grade(Grade 2: HR:1.124, 95% CI: 0.953–1.326, P-value: 0.164; Grade 3: HR:1.984, 95% CI: 1.574–2.501, P-value < 0.001; Grade 4: HR:2.119, 95% CI: 1.115–4.027, P-value: 0.022), surgery(Liver Resection: HR:0.193, 95% CI: 0.160–0.234, P-value < 0.001; Liver Transplant: HR:0.102, 95% CI: 0.072–0.145, P-value < 0.001), chemotherapy(HR:0.561, 95% CI: 0.471–0.668, P-value < 0.001), and radiotherapy(HR:0.641, 95% CI: 0.463–0.887, P-value:0.007) were independent prognostic factors for patients with ANHC. We developed a nomogram model for predicting the OS and CSS of patients with ANHC, with a good predictive performance.

Publisher

Springer Science and Business Media LLC

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