Development and validation of web-based prognostic nomograms for massive hepatocellular carcinoma (≥10 cm): A retrospective study based on the SEER database.

BACKGROUND AND AIMS: Massive hepatocellular carcinoma (MHCC, a maximum tumor size of at least 10 cm) tends to have a poor prognosis. Therefore, this study aims to construct and validate prognostic nomograms for MHCC. METHODS: Clinic data of 1292 MHCC patients between 2010 and 2015 were got from the surveillance, epidemiology, and end results (SEER) cancer registration database. The whole set was separated into the training and validation sets at a ratio of 2:1 randomly. Variables, significantly associated with cancer-specific (CSS) and overall survival (OS) of MHCC were figured out by multivariate Cox regression analysis and were taken to develop nomograms. The concordance index (C-index), calibration curve, and decision curve analysis (DCA) were taken to validate the predictive abilities and accuracy of the nomograms. RESULTS: Race, alpha-fetoprotein (AFP), grade, combined summary stage, and surgery were identified as independent factors of CSS, and fibrosis score, AFP, grade, combined summary stage, and surgery significantly correlated with OS in the training cohort. They then were taken to construct prognostic nomograms. The constructed model for predicting CSS exhibited satisfactory performance with a C-index of 0.727 (95% CI: 0.746-0.708) in the training group and 0. 672 (95% CI: 0.703-0.641) in the validation group. Besides, the model for predicting OS of MHCC also showed strong performance both in the training group (C-index: 0.722, 95% CI: 0.741-0.704) and the validation (C-index: 0.667, 95% CI: 0.696-0.638) group. All calibration curves and decision curves performed satisfactory predictive accuracy and clinic application values of the nomograms. CONCLUSION: The web-based nomograms for CSS and OS of MHCC were developed and validated in this study, which prospectively could be tested and may serve as additional tools to assess patient's individualized prognosis and make precise therapeutic selection to improve the poor outcome of MHCC.