This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm’s performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model’s predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.
本研究探讨了机器学习在预测肝细胞癌（HCC）患者治疗建议方面的潜力。通过一项经机构审查委员会批准的多学科肿瘤委员会讨论患者的回顾性研究，提取临床及影像学变量，并采用梯度提升机器学习算法XGBoost进行分析。通过混淆矩阵指标及受试者工作特征（ROC）曲线下面积评估算法性能。研究共纳入140例患者（平均年龄67.7±8.9岁），结果显示该算法能有效预测肿瘤委员会提出的全部八类治疗建议。模型预测准确性优于基于ESMO和NCCN已发布治疗指南的预测结果。研究表明，整合临床与影像学变量的机器学习模型能够预测多学科专家肿瘤委员会的治疗建议，在缺乏亚专科专业知识的医疗环境中可能为临床决策提供辅助。
肿瘤(癌症)患者之家