Impact of marital status on overall survival in patients with early-stage hepatocellular carcinoma

Nomogram predicting survival in patients with lymph node-negative hepatocellular carcinoma based on the SEER database and external validation

BACKGROUND

The relationship between lymph node (LN) status and survival outcome in hepatocellular carcinoma (HCC) is a highly controversial topic. The aim of this study was to investigate the prognostic factors in patients without LN metastasis (LNM) and to construct a nomogram to predict cancer-specific survival (CSS) in this group of patients.

METHODS

We screened 6840 eligible HCC patients in the Surveillance, Epidemiology and End Results（SEER）database between 2010 and 2019 and randomized them into a training cohort and an internal validation cohort, and recruited 160 patients from Zhongnan Hospital of Wuhan University as an external validation cohort. Independent prognostic factors obtained from univariate and multivariate analysis were used to construct a nomogram prediction model. The concordance index (C-index), area under curve (AUC), calibration plots and decision curve analysis (DCA) were used to assess the predictive power and clinical application of the model.

RESULTS

Univariate and multivariate analysis revealed age, gender, bone metastasis, lung metastasis, AFP, T stage, surgery and chemotherapy as independent prognostic factors. The C-index of the constructed nomogram for the training cohort, internal validation cohort and external validation cohort are 0.746, 0.740, and 0.777, respectively. In the training cohort, the AUC at 1-, 3-, and 5-year were 0.81, 0.800, and 0.800, respectively. Calibration curves showed great agreement between the actual observations and predictions for the three cohorts. The DCA results suggest that the nomogram model has more clinical application potential.

CONCLUSION

We constructed a nomogram to predict CSS in HCC patients without LNM. The model has been internally and externally validated to have excellent predictive performance and can help clinicians determine prognosis and make treatment decisions.

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

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.

The novel predictive nomograms for early death in metastatic hepatocellular carcinoma: A large cohort study

Metastatic hepatocellular carcinoma (HCC) is an aggressive disease which usually have a poor prognosis. Early mortality and risk factors in patients with metastatic HCC are poorly understood. Our study sought to identify associated risk factors and develop the nomograms for predicting early death in metastatic HCC patients. The patients diagnosed with metastatic HCC were chosen from the surveillance, epidemiology, and end results database between 2010 and 2015. To identify significant independent risk factors for early death, both univariate and multivariate logistic regression models were used. We constructed a pragmatic nomogram and then evaluated by using receiver operating characteristic curves, calibration plots, and decision curve analysis. The prediction model included 2587 patients with metastatic HCC. Among them, 1550 experienced early death (died within 3 months of initial diagnosis) and 1437 died from cancer-specific causes. Multivariate logistic regression analysis found that grade, surgery, radiation, chemotherapy, alpha-fetoprotein levels, and lung metastasis were independent risk factors for both all-cause early death and cancer-specific early death. In addition, bone metastasis were independent risk factors for all-cause early death, T-stage and brain metastasis were also independent risk factors for cancer-specific early death. Then we used the relevant risk factors to developed the practical nomograms of all-cause and cancer-specific early deaths. The nomograms demonstrated good predictive power and clinical utility under receiver operating characteristic curves and decision curve analysis. We developed 2 novel comprehensive nomograms to predict early death among metastatic HCC patients. Nomograms may help oncologists develop better treatment strategies and implementation of individualized treatment plans.