Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Korean Advanced Institute of Science and Technology, Daejeon, South Korea.
JCO Clin Cancer Inform. 2022 Feb;6:e2100169. doi: 10.1200/CCI.21.00169.
To stratify patients and aid clinical decision making, we developed machine learning models to predict treatment failure and radiation-induced toxicities after radiotherapy (RT) in patients with hepatocellular carcinoma across institutions.
The models were developed using linear and nonlinear algorithms, predicting survival, nonlocal failure, radiation-induced liver disease, and lymphopenia from baseline patient and treatment parameters. The models were trained on 207 patients from Massachusetts General Hospital. Performance was quantified using Harrell's c-index, area under the curve (AUC), and accuracy in high-risk populations. Models' structures were optimized in a nested cross-validation approach to prevent overfitting. A study analysis plan was registered before external validation using 143 patients from MD Anderson Cancer Center. Clinical utility was assessed using net-benefit analysis.
The survival model stratified high-risk versus low-risk patients well in the external validation cohort (c-index = 0.75), better than existing risk scores. Predictions of 1-year survival and nonlocal failure were excellent (external AUC = 0.74 and 0.80, respectively), especially in the high-risk group (accuracy > 90%). Cause-of-death analysis showed differential modes of treatment failure in these cohorts and indicated that these models could be used to stratify RT patients for liver-sparing treatment regimen or combination approaches with systemic agents. Predictions of liver disease and lymphopenia were good but less robust (external AUC = 0.68 and 0.7, respectively), suggesting the need for more comprehensive consideration of dosimetry and better predictive biomarkers. The liver disease model showed excellent accuracy in the high-risk group (92%) and revealed possible interactions of platelet count with initial liver function.
Machine learning approaches can provide reliable outcome predictions in patients with hepatocellular carcinoma after RT in diverse cohorts across institutions. The excellent performance, particularly in high-risk patients, suggests novel strategies for patient stratification and treatment selection.
为了对患者进行分层并辅助临床决策，我们开发了机器学习模型，以预测跨机构接受肝癌放射治疗（RT）的患者的治疗失败和放射性肝损伤等毒性反应。
该模型使用线性和非线性算法开发，根据基线患者和治疗参数预测生存、非局部失败、放射性肝损伤和淋巴细胞减少。该模型在马萨诸塞州综合医院的 207 名患者中进行训练。使用哈雷尔（Harrell）c 指数、曲线下面积（AUC）和高危人群中的准确性来量化模型的性能。通过嵌套交叉验证方法优化模型结构，以防止过度拟合。在使用 MD 安德森癌症中心的 143 名患者进行外部验证之前，注册了研究分析计划。使用净效益分析评估临床实用性。
在外部验证队列中，生存模型很好地对高危与低危患者进行分层（c 指数=0.75），优于现有风险评分。1 年生存率和非局部失败的预测效果非常好（外部 AUC 分别为 0.74 和 0.80），尤其是在高危组（准确率>90%）。死因分析表明，这些队列中的治疗失败模式存在差异，表明这些模型可用于对 RT 患者进行分层，以选择肝脏保护治疗方案或与系统药物联合应用。肝脏疾病和淋巴细胞减少的预测效果良好，但不太稳健（外部 AUC 分别为 0.68 和 0.7），这表明需要更全面地考虑剂量学和更好的预测生物标志物。肝脏疾病模型在高危组中的准确率很高（92%），并揭示了血小板计数与初始肝功能之间可能存在的相互作用。
机器学习方法可以为跨机构不同队列的肝癌患者接受 RT 后的生存结果提供可靠的预测。特别是在高危患者中，该模型具有出色的性能，提示可以采用新的策略对患者进行分层并选择治疗方案。
