Two novel online nomograms for predicting the survival of individual patients undergoing partial hepatectomy for huge hepatocellular carcinoma

A method for predicting prognosis of patients who undergo partial hepatectomy for huge hepatocellular carcinoma (HHCC, diameter ≥10 cm) is currently lacking. This study aimed to establish two online nomograms to predict the overall survival (OS) and disease-free survival (DFS) for patients undergoing resection for HHCC. The clinicopathologic characteristics and follow-up information of patients who underwent partial hepatectomy for HHCC at two medical centers were reviewed. Using a training cohort, a Cox model was used to identify the predictors of survival. Two dynamic nomograms for OS and DFS were developed and validated based on the data. Eight and nine independent factors derived from the multivariate analysis of the training cohort were screened and incorporated into the nomograms for OS and DFS, respectively. In the training cohort, the nomogram achieved concordance indices (C-indices) of 0.745 and 0.738 in predicting the OS and DFS, respectively. These results were supported by external validation (C-indices: 0.822 for OS and 0.827 for DFS). Further, the calibration curves of the endpoints showed a favorable agreement between the nomograms’ assessments and actual observations. The two web-based nomograms demonstrated optimal predictive performance for patients undergoing partial hepatectomy for HHCC. This provides a practical method for a personalized prognosis based on an individual's underlying risk factors.