Abstract
BACKGROUND: An individual prognostic model that includes inflammation caused by the delayed recovery of liver function after surgery for the early recurrence of hepatocellular carcinoma (HCC) following liver transplantation (LT) has not been well determined. Our aim was to develop a nomogram model for predicting individual survival and early recurrence following LT for patients. METHODS: Retrospective data, including clinical pathology and follow-up data, on HCC patients were collected between October 2016 and October 2019 at Huashan Hospital Affiliated to Fudan University. A nomogram estimating recurrence post-transplantation was constructed using multivariate Cox regression analysis. RESULTS: A total of 210 patients were included in the present study. The multivariate estimators of recurrence consisted of age, maximum tumor diameter, tumor thrombus, microvascular invasion (MVI), alanine aminotransferase and alpha-fetoprotein on postoperative day 7. Nomogram of recurrence-free survival was developed. The calibration and discrimination of the novel model were assessed with the calibration curves and concordance index (C-index). Its reliability and advantages were evaluated by comparing it with the conventional American Joint Committee on Cancer (AJCC) 8th edition staging system using integrated discrimination improvement (IDI) and net reclassification improvement (NRI). In comparison to the AJCC 8th edition staging system, the C-index (development set: 0.796 vs. 0.643, validation set: 0.741 vs. 0.563), the area under the receiver operating characteristic curve (AUC) of the validation set (1-year AUC: 0.732 vs. 0.586, 2-year AUC: 0.705 vs. 0.504), the development set (1-year AUC: 0.799 vs. 0.551, 2-year AUC: 0.801 vs. 0.512), and this model's calibration plots all showed improved performance. In addition, NRI and IDI verified that the nomogram is an accurate prognostic tool. Subsequently, a web calculator was generated to assess the risk of tumor recurrence post-LT. CONCLUSIONS: The nomogram, based on clinical and pathological factors, showed good accuracy in estimating prognostic recurrence and can be used to guide individual patient follow-up and treatment.