标题： 基于SEER数据库预测肝细胞癌的肺转移 显示英文标题

标题： Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database

摘要： 肝细胞癌（HCC）是导致癌症相关死亡的主要原因，肺转移是最常见的远端扩散部位，并显著恶化预后。 尽管临床和人口统计数据的可用性不断增加，但针对HCC肺转移的预测模型在范围和临床适用性方面仍有限。 在本研究中，我们使用来自监测、流行病学和最终结果（SEER）数据库的数据开发并验证了一个端到端的机器学习流程。 我们评估了三种机器学习模型（随机森林、XGBoost和逻辑回归）以及一个多层感知器（MLP）神经网络。 我们的模型实现了高AUROC值和召回率，其中随机森林和MLP模型表现出最佳的整体性能（AUROC = 0.82）。 然而，各模型的低精确度突显了准确预测阳性病例的挑战。 为解决这些限制，我们开发了一个自定义损失函数，结合召回优化，使MLP模型达到最高的灵敏度。 集成方法进一步通过利用单个模型的优势提高了整体召回率。 特征重要性分析揭示了关键预测因素，如手术状态、肿瘤分期和随访时间，强调了临床干预和疾病进展在转移预测中的相关性。 虽然本研究展示了机器学习在识别高风险患者方面的潜力，但局限性包括依赖于不平衡数据集、特征注释不完整以及预测的低精确度。 未来的工作应利用不断扩展的SEER数据集，改进数据填补技术，并探索先进的预训练模型以提高预测准确性和临床实用性。 显示英文摘要

摘要： Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with lung metastases being the most common site of distant spread and significantly worsening prognosis. Despite the growing availability of clinical and demographic data, predictive models for lung metastasis in HCC remain limited in scope and clinical applicability. In this study, we develop and validate an end-to-end machine learning pipeline using data from the Surveillance, Epidemiology, and End Results (SEER) database. We evaluated three machine learning models (Random Forest, XGBoost, and Logistic Regression) alongside a multilayer perceptron (MLP) neural network. Our models achieved high AUROC values and recall, with the Random Forest and MLP models demonstrating the best overall performance (AUROC = 0.82). However, the low precision across models highlights the challenges of accurately predicting positive cases. To address these limitations, we developed a custom loss function incorporating recall optimization, enabling the MLP model to achieve the highest sensitivity. An ensemble approach further improved overall recall by leveraging the strengths of individual models. Feature importance analysis revealed key predictors such as surgery status, tumor staging, and follow up duration, emphasizing the relevance of clinical interventions and disease progression in metastasis prediction. While this study demonstrates the potential of machine learning for identifying high-risk patients, limitations include reliance on imbalanced datasets, incomplete feature annotations, and the low precision of predictions. Future work should leverage the expanding SEER dataset, improve data imputation techniques, and explore advanced pre-trained models to enhance predictive accuracy and clinical utility.