Daye Dania, Parker Regina, Tripathi Satvik, Cox Meredith, Brito Orama Sebastian, Valentin Leonardo, Bridge Christopher P, Uppot Raul N
Massachusetts General Hospital, Boston, MA 02114, USA.
Harvard Medical School, Boston, MA 02115, USA.
Cancers (Basel). 2024 May 23;16(11):1975. doi: 10.3390/cancers16111975.
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。通过对多学科肿瘤委员会讨论的患者进行一项经机构审查委员会批准的回顾性研究，提取了临床和影像变量，并将其用于梯度提升机器学习算法XGBoost。使用混淆矩阵指标和受试者操作特征（ROC）曲线下面积评估该算法的性能。该研究纳入了140名患者（平均年龄67.7±8.9岁），发现该算法能够预测委员会提出的所有八项治疗建议。该模型的预测比基于欧洲肿瘤内科学会（ESMO）和美国国立综合癌症网络（NCCN）发布的治疗指南的预测更准确。该研究得出结论，纳入临床和影像变量的机器学习模型可以预测专家多学科肿瘤委员会提出的治疗建议，这可能有助于在缺乏专科专业知识的情况下进行临床决策。
