Abstract
PURPOSE: Postoperative prognosis prediction models for patients with stage Ⅰ and Ⅱ hepatocellular carcinoma (HCC) according to the 8th edition of the Tumor-Node-Metastasis staging system after surgery are rare. This study aimed to build a prognostic score to predict survival outcomes and stratify these patients into different prognostic strata. PATIENTS AND METHODS: We developed a web-based nomogram that incorporated four selected risk factors based on the multivariate Cox regression, using a training set (n=3567) from the Surveillance, Epidemiology, and End Results (SEER) database. It was validated with an independent internal set from the SEER database (n=1783) and an external validation set of 516 Chinese patients. The predictive performance and discrimination ability of our model were further evaluated and compared with those of the conventional HCC staging systems. RESULTS: Our nomogram consistently outperformed the conventional staging systems in the training, internal validation set, and external validation set. We quantified the nomogram model into a numerical SNIG (an abbreviation of the incorporated variables - size, number, MVI, and grade) score by summing the points assigned to each incorporated variable, leading to the optimal cut-off values of 6 and 10, which could stratify patients into 3 categories (SNIG score <6, 6-10, ≥10). This yielded significantly different median overall survivals (interquartile ranges) of 42.0 (20.0-72.0) and 37.0 (17.0-67.0); 28.0 (12.0-60.0) and 42.0 (21.75-82.0); 40.0 (18.0-70.0) and 29.0 (11.5-61.0) months for the 3 categories in the entire SEER and external validation sets, respectively. CONCLUSION: We developed a web-based SNIG model to graphically and numerically predict the overall survival of stage Ⅰ and Ⅱ HCC. This scoring system may shed light on risk stratification for these patients in clinical practice and clinical trials.