Shan Yuying, Yu Xi, Yang Yong, Sun Jiannan, Wu Shengdong, Mao Shuqi, Lu Caide
Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, Ningbo, Zhejiang, 315041, People's Republic of China.
J Hepatocell Carcinoma. 2022 Aug 10;9:717-728. doi: 10.2147/JHC.S373960. eCollection 2022.
The macrotrabecular-massive subtype of hepatocellular carcinoma (MTM-HCC) is an aggressive histological type and results in poor prognosis. We developed a nomogram model based on laboratory results to predict the presence of MTM-HCC.
A total of 357 HCC patients who underwent radical surgery between January 2015 and December 2020 at Ningbo Medical Center Lihuili Hospital were grouped according to histological type. After propensity score matching (PSM), 267 patients were divided into MTM-HCC (n = 76) and non-MTM-HCC (n = 191) groups. A LASSO regression analysis model was used to select predictive factors. Finally, a nomogram for predicting the presence of MTM-HCC was established. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram model by quantifying the net benefits along with the increase in threshold probabilities.
The 1-, 3-, and 5-year disease-free survival (DFS) and overall survival (OS) rates for MTM-HCC were 60.0%, 36.0%, 32.4% and 92.1%, 68.7%, 52.2%, respectively. Survival analysis indicated that the probabilities of achieving DFS and OS were significantly worse in the MTM-HCC group than in the non-MTM-HCC group ( < 0.05). The nomogram model that included AST levels, PT and AFP levels achieved a better C-index of 0.723 (95% CI: 0.659-0.787). DCA revealed that the nomogram model could lead to net benefits and exhibited a wider range of threshold probabilities in the prediction of MTM-HCC.
The nomogram model included AST, PT and AFP could achieve an optimal performance in the preoperative prediction of MTM-HCC.
肝细胞癌的大小梁-实体亚型（MTM-HCC）是一种侵袭性组织学类型，预后较差。我们基于实验室检查结果开发了一种列线图模型，以预测MTM-HCC的存在。
对2015年1月至2020年12月在宁波市医疗中心李惠利医院接受根治性手术的357例肝癌患者按组织学类型进行分组。经过倾向评分匹配（PSM）后，将267例患者分为MTM-HCC组（n = 76）和非MTM-HCC组（n = 191）。采用LASSO回归分析模型选择预测因素。最后，建立了预测MTM-HCC存在的列线图。进行决策曲线分析（DCA），通过量化净效益以及阈值概率的增加来确定列线图模型的临床实用性。
MTM-HCC的1年、3年和5年无病生存率（DFS）及总生存率（OS）分别为60.0%、36.0%、32.4%和92.1%、68.7%、52.2%。生存分析表明，MTM-HCC组实现DFS和OS的概率显著低于非MTM-HCC组（P < 0.05）。包含谷草转氨酶（AST）水平、凝血酶原时间（PT）和甲胎蛋白（AFP）水平的列线图模型获得了更好的C指数，为0.723（95%可信区间：0.659 - 0.787）。DCA显示，列线图模型可带来净效益，并且在预测MTM-HCC时表现出更广泛的阈值概率范围。
包含AST、PT和AFP的列线图模型在术前预测MTM-HCC方面可实现最佳性能。
