Lee Jie, Lin Jhen-Bin, Lin Wan-Chun, Jan Ya-Ting, Leu Yi-Shing, Chen Yu-Jen, Wu Kun-Pin
Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan.
Department of Medicine, MacKay Medical College, New Taipei City, Taiwan.
Eur Radiol. 2024 Dec 20. doi: 10.1007/s00330-024-11303-4.
Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC.
We enrolled 1087 patients with OCSCC treated with surgery and adjuvant radiotherapy at two tertiary centers (660 in the derivation cohort and 427 in the external validation cohort). Skeletal muscle index (SMI) was measured using pre- and post-radiotherapy computed tomography (CT) at the C3 vertebral level. Random forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were developed to predict all-cause mortality, and their performances were evaluated using the area under the curve (AUC). Muscle loss threshold was identified using the SHapley Additive exPlanations (SHAP) method and validated using Cox regression analysis.
In the external validation cohort, the RF, XGBoost, and CatBoost models achieved favorable performance in predicting all-cause mortality (AUC: 0.898, 0.859, and 0.842). The SHAP method demonstrated that SMI change after radiotherapy was the most important feature for predicting all-cause mortality and consistently identified SMI loss ≥ 4.2% as the threshold in all three models. In multivariable analysis, SMI loss ≥ 4.2% was independently associated with increased all-cause mortality risk in both cohorts (derivation cohort: hazard ratio: 6.66, p < 0.001; external validation cohort: hazard ratio: 8.46, p < 0.001).
This study can assist clinicians in identifying patients with considerable muscle loss after treatment and guide interventions to improve muscle mass.
Question Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity cancer; however, the threshold of muscle loss remains unclear. Findings Explainable artificial intelligence identified muscle loss ≥ 4.2% as the threshold of increased all-cause mortality risk in both derivation and external validation cohorts. Clinical Relevance Muscle loss ≥ 4.2% may be the optimal threshold for survival in patients who receive adjuvant radiotherapy for oral cavity cancer. This threshold can guide clinicians in improving muscle mass after radiotherapy.
口腔鳞状细胞癌（OCSCC）患者放疗后肌肉流失与较差的生存率相关。然而，肌肉流失的阈值仍不清楚。本研究旨在利用可解释的人工智能来确定OCSCC患者中与生存相关的肌肉流失阈值。
我们纳入了在两个三级中心接受手术和辅助放疗的1087例OCSCC患者（推导队列660例，外部验证队列427例）。使用放疗前后C3椎体水平的计算机断层扫描（CT）测量骨骼肌指数（SMI）。开发了随机森林（RF）、极端梯度提升（XGBoost）和分类提升（CatBoost）模型来预测全因死亡率，并使用曲线下面积（AUC）评估其性能。使用SHapley加性解释（SHAP）方法确定肌肉流失阈值，并使用Cox回归分析进行验证。
在外部验证队列中，RF、XGBoost和CatBoost模型在预测全因死亡率方面表现良好（AUC：0.898、0.859和0.842）。SHAP方法表明，放疗后SMI变化是预测全因死亡率的最重要特征，并在所有三个模型中一致确定SMI损失≥4.2%为阈值。在多变量分析中，SMI损失≥4.2%在两个队列中均与全因死亡风险增加独立相关（推导队列：风险比：6.66，p＜0.001；外部验证队列：风险比：8.46，p＜0.001）。
本研究可帮助临床医生识别治疗后肌肉流失严重的患者，并指导干预措施以增加肌肉量。
问题放疗后肌肉流失与口腔癌患者较差的生存率相关；然而，肌肉流失的阈值仍不清楚。发现可解释的人工智能确定肌肉流失≥4.2%为推导队列和外部验证队列中全因死亡风险增加的阈值。临床意义肌肉流失≥4.2%可能是接受口腔癌辅助放疗患者生存的最佳阈值。该阈值可指导临床医生在放疗后增加肌肉量。
