Development and validation of a prognostic nomogram in gastric cancer with hepatitis B virus infection

The retrospectively study included 319 patients with histologically diagnosed GC with hepatitis B viral infection from 2009 to 2017 in Sun Yat-sen University Cancer Center (SYSUCC, Guangdong, China). All the patients were classified as the first record of hospitalizations and the clinical information were extracted from Electronic Medical Record (EMR) system. The levels of LFT factors were investigated before treatment Laboratory Information System (LIS). The inclusion criteria were as follows: (1) patients with a confirmed histologically diagnosed of GC; (2) patients with HBsAg-positive, but without other types of hepatitis viruses (i.e. hepatitis A viral, hepatitis C viral); (3) patients without second tumor, or indefinite diagnoses; (4) patients with complete clinical data; (5) patients without diseases influenced LFT (i.e. acute hepatitis, liver cirrhosis); (6) patients without any treatment. We divided patients into two cohorts by the time sequence. The primary cohort comprised 235 patients from August 2008 to September 2015. The validation cohort was contained 84 GC patients from September 2015 to January 2017 with age and sex match to the primary cohort. All patients provided written informed consent. The Institute Research Ethics Committee of the Sun Yat-Sen University Cancer Center, Guangzhou, China approved this study. The authenticity of this article has been validated by uploading the key raw data onto the Research Data Deposit public platform (http://​www.​researchdata.​org.​cn), with the approval RDD number as RDDA2019001020.

All the patients received routine tests at the first visit in our hospital. Blood samples were collected at room temperature, then centrifuged at 3500 r/min for 10 min, which could be used to estimate the level of LFT biomarkers, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transpeptidase (GGT), total bile acid (TBA), alkaline phosphatase (ALP), albumin (ALB), total bilirubin (TBIL), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), prothrombin time (PT), fibrinogen (Fbg). HBV infection markers including HbsAg, hepatitis B surface antibody (HbsAb), hepatitis B e antigen (HbeAg) hepatitis B e antibody (HbeAb) and hepatitis B core antibody (HbcAb) were recorded.

All GC patients were advised to receive regular follow-ups after completion of the primary therapy according to clinical guidelines. Patients were generally followed up every 3 months in the first 2 years and annually thereafter for patients without evidence of recurrence in the following 3 to 5 years. Patients who did not visit our hospital as scheduled were telephoned for follow-ups to obtain the treatment information and living status (performed by The Medical Information Unit in our Cancer Center). The last follow-up occurred in September 2018. The outcome of our study was overall survival (OS). OS was defined as the time from the diagnosis of HCC to the date of the last follow-up or death.

Statistical analyses were performed using SPSS 16.0 (IBM, Chicago, IL, USA) and R for Windows (version 3.4.2, http://​www.​r-project.​org/​). The optimal cut-off points in our study were evaluated by minimum P value from log-rank × 2 statistics using the X-tile program [14] and continuous variables were transformed to categorical variables, while the categorical variables were classified based on clinical findings. Univariate and multivariate regression analysis was used to analyze the risk factors in the primary cohort, A nomogram was formulated based on the results of multivariate analysis by the package of rms. We tested the accuracy of the nomograms by discrimination and calibration both in primary and externa validation cohort. The discrimination of the nomogram was measured by Harrell’s C-index (C-index). The value of the C-index ranges from 0.5 to 1.0, with 0.5 indicating random chance and 1.0 indicating a perfect ability to correctly discriminate the outcome with the model. Then, the calibration curve of the nomogram model for the OS and decision curve analyses were performed. The total points of each patient were calculated according to the established Cox regression model, 3 groups of patients with different risk of prognosis (based on the total points) were delineated using the X-tile program. Survival curves were depicted by the Kaplan–Meier method, and using the dichotomized risk group as a factor, finally, compared using the log-rank test. All statistical tests were two-sided, and P values of less than 0.05 were considered to be statistically significant.

The clinicopathologic characteristics of the training and validation sets were evaluated. The characteristics of the 235 consecutive AFP-negative HCC patients in the primary cohort and 84 patients in the validation cohort are showed in Table 1. There were 68 (28.94%) patients died, while 22 (26.19%) patients died respectively.

All the available informations, including clinicopathologic characteristics and biomarkers, were included for univariate and multivariate analysis (Table 2). In univariate analyse, Age, TNM stage, tumor stage, node stage, distant metastases, LDH, GGT, TBA, ALP, ALB, ApoB, Fbg were related to OS. All of the potentially important biomarkers identified in univariate analysis were further included in the multivariate analysis. Based on 235 HBsAg-positive GC patients with complete information, age, tumor stage, distant metastases, GGT and ALP were significant predictors of OS.

A nomogram is a graphic representation of the solution of an equation that provides a reasonable approximation of the probability of a particular outcome. The model explanatory covariables consisted of age, tumor stage, distant metastases, GGT and ALP. A nomogram was constructed to predict 1-, 3- and 5-year OS (Fig. 1). The validation of nomogram was consisted of discrimination and calibration Discrimination was performed by using a concordance index (C-index) Calibration was evaluated by comparing the means of predicted survival with estimating of predicted with observed Kaplan–Meier survival, with the x-axes are actual survival estimated by the nomogram, the y-axes are observed survival calculated by the Kaplan–Meier method. In the primary cohort, the C-index for OS prediction was 0.812 (95% CI 0.762–0.862). The calibration plot for the probability of OS at 1, 3 or 5 year after therapy showed an optimal agreement between the prediction by nomogram and actual observation (Fig. 2).

We then applied the nomogram to the validation cohort of 84 HBsAg-positive GC patients.

The C-index for OS prediction was up to 0.821 (95% CI 0.723–0.919). The calibration plot for the probability of OS at 3-year after therapy showed an optimal agreement between the prediction by nomogram and actual observation (Fig. 2).

Furthermore, the discrimination of the nomogram and that of the AJCC TNM Stage have been compared. In the development cohort, the C-index of nomogram was 0.812 (95% CI 0.762–0.862), which was superior than the C-index of AJCC TNM Stage 0.755 (95% CI 0.702–0.808).

The decision curve analysis for the nomogram and TNM staging systems is showed in Fig. 3. The decision curve presented that if the threshold probability of a patient is > 10%, the developed nomogram and TNM staging system in predicting OS is more benefit than all patients dead scheme or none patients dead scheme. Furthermore, the net benefit was comparable, the nomogram in predicting OS is more benefit than that of TNM staging system in this range.

Based on the nomogram, patients were divided into three groups: low-risk group, middle-risk group and a high-risk group, which showed good prognostic classification for HBsAg-positive GC both in development cohort and validation cohort. In the development cohort, there were 114 patients in the low-risk group, 49 patients in the middle-risk group, while 71 patients in the high-risk group. The OS between the three risk groups were (37.07 ± 16.80) months, (24.68 ± 18.31) months, (8.38 ± 6.33) months (P < 0.001). Also, in the validation cohort, there were 22 patients in the low-risk group, 49 patients in the middle-risk group, while 13 patients in the high-risk group. The mean OS between the three risk groups were (19.82 ± 10.07) months, (14.08 ± 9.76) months and (13.92 ± 7.15) months (P < 0.001) (Fig. 4).