Development and validation of a nomogram for survival prediction in hepatocellular carcinoma after partial hepatectomy

Construction and validation of a preoperative prognostic model integrating the novel aspartate aminotransferase-albumin score for hepatocellular carcinoma patients undergoing liver resection

Backgrounds/Aims

Patients undergoing liver resection for hepatocellular carcinoma (HCC) often possess good liver reserve, which may limit the prognostic effectiveness of existing liver function scores. This study aimed to develop a novel liver function score and a preoperative prognostic model specifically for HCC resection patients.

Methods

Eight hundred twenty-seven HCC patients undergoing initial liver resection were segregated into training and validation cohorts in a 6:4 ratio. Cox regression analysis was employed to identify significant parameters influencing overall survival. The efficacy of the liver function score and prognostic model was evaluated using metrics such as the area under the receiver operating characteristic curve.

Results

Aspartate aminotransferase (AST) and albumin emerged as significant prognostic indicators. The AST-albumin (ASAL) score, calculated as exp [AST (IU/L) × 0.005 - albumin (g/dL) × 1.043] × 100, outperformed existing scores such as Child-Turcotte-Pugh, albumin-bilirubin, platelet-albumin, and AST-platelet ratio index in both training and validation cohorts. Additionally, a scoring model that combined the ASAL score with alpha-fetoprotein and the up-to-seven criterion exhibited superior discriminatory capabilities compared to the American Joint Committee on Cancer tumor, node, metastasis stage, and Barcelona Clinic Liver Cancer stage.

Conclusions

The proposed prognostic model that integrates the novel ASAL score offers promising prognostic potential for HCC patients undergoing liver resection.

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.