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register hereBackground: Most patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients.Methods: Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms.

背景：大多数多发性肝细胞癌（MHCC）患者一旦被诊断出就处于晚期，因此临床治疗和决策相当棘手。AJCC-TNM系统无法准确确定预后，我们的研究旨在确定MHCC的预后因素，并开发预后模型来量化患者的风险和生存概率。方法：从监测，流行病学和最终结果（SEER）数据库中获得符合条件的HCC患者，然后使用Cox回归，机器学习（ML）和深度学习（DL）算法建立预后模型。

the model's performance was evaluated using Cindex, receiver operating characteristic curve, Brier score and decision curve analysis respectively and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique.Results: A total of eight variables were included in the follow-up study, our analysis identified that the gradient boosted machine (GBM) model was the best prognostic model for advanced MHCC.

分别使用Cindex，接受者工作特征曲线，Brier评分和决策曲线分析评估模型的性能，并使用SHapley加法解释（SHAP）可解释性技术解释最佳模型。结果：随访研究共纳入8个变量，我们的分析发现梯度增强机（GBM）模型是晚期MHCC的最佳预后模型。

In particular, the GBM model in the training cohort had a C-index of 0.73, a Brier score of 0.124, with area under the curve (AUC) values above 0.78 at the first, third, and fifth year. Importantly, the model also performed well in test cohort. The Kaplan-Meier (K-M) survival analysis demonstrated that the newly developed risk stratification system could well differentiate the prognosis of patients.Conclusions: Of the ML models, GBM model could predict the prognosis of advanced MHCC patients most accurately.

特别是，训练队列中的GBM模型的C指数为0.73，Brier得分为0.124，第一年，第三年和第五年的曲线下面积（AUC）值高于0.78。重要的是，该模型在测试队列中也表现良好。Kaplan-Meier（K-M）生存分析表明，新开发的风险分层系统可以很好地区分患者的预后。。

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