Background: An accurate prognostic assessment is essential to optimize treatment strategies in head and neck cancer (HNC). This study aimed to develop and internally evaluate an AI-assisted survival risk score derived from automatically quantified cervical muscle parameters on routine radiotherapy-planning CT scans. Methods: Pretreatment CT images were processed in a single-center cohort of 65 HNC patients, using AI-assisted automated segmentation to obtain the cervical skeletal muscle index (SMI), intramuscular adipose tissue area (IMAT), and mean muscle attenuation (HU). A multivariable Cox regression model was used to generate the continuous FUNC-RISK score, and model performance was assessed using time-dependent ROC curves at 36 and 60 months. Results: Patient-, tumor-, and treatment-related characteristics were not predictive of survival. SMI (p= 0.006) and IMAT (p= 0.047) were significantly associated with overall survival in a univariable analysis, while HU showed a borderline association (p= 0.087). All three parameters were included in the multivariable model, yielding the following equation: FUNC-RISK = (−0.364 × SMI) + (−0.087 × IMAT) + (0.011 × HU). The model demonstrated moderate discrimination (AUC = 0.734 at 36 months; 95% CI 0.604–0.863;p= 0.002, and AUC = 0.689 at 60 months; 95% CI 0.558–0.819;p= 0.009). Based on the median score (−3.18), patients were stratified into low- and high-risk groups. Five-year overall survival was 71.9% ± 7.9% for the low-risk group versus 39.4% ± 8.5% for the high-risk group (p= 0.006). Conclusions: FUNC-RISK provides preliminary evidence of clinically meaningful prognostic stratification based on AI-derived cervical muscle quantity and quality metrics obtained from routine radiotherapy-planning CT scans. These exploratory results support the potential role of automated body-composition analysis in personalized risk assessment for HNC, although external multicenter validation is required before clinical implementation.
背景：准确评估预后对于优化头颈癌（HNC）的治疗策略至关重要。本研究旨在基于常规放疗计划CT扫描中自动量化的颈部肌肉参数，开发并内部评估一种人工智能辅助的生存风险评分。方法：对65例HNC患者的单中心队列治疗前CT图像进行处理，采用人工智能辅助自动分割技术获取颈部骨骼肌指数（SMI）、肌内脂肪组织面积（IMAT）及平均肌肉衰减值（HU）。通过多变量Cox回归模型生成连续的FUNC-RISK评分，并采用36个月和60个月的时间依赖性ROC曲线评估模型性能。结果：患者特征、肿瘤特征及治疗相关特征均未显示出对生存的预测能力。单变量分析显示，SMI（p=0.006）和IMAT（p=0.047）与总生存期显著相关，而HU呈临界关联（p=0.087）。所有三个参数均纳入多变量模型，得出以下方程：FUNC-RISK = (−0.364 × SMI) + (−0.087 × IMAT) + (0.011 × HU)。该模型展现出中等区分度（36个月AUC=0.734；95% CI 0.604–0.863；p=0.002；60个月AUC=0.689；95% CI 0.558–0.819；p=0.009）。根据评分中位数（−3.18）将患者分为低风险组与高风险组，低风险组五年总生存率为71.9% ± 7.9%，高风险组为39.4% ± 8.5%（p=0.006）。结论：FUNC-RISK评分基于常规放疗计划CT扫描中人工智能提取的颈部肌肉数量与质量指标，为具有临床意义的预后分层提供了初步证据。这些探索性结果支持自动化身体成分分析在头颈癌个体化风险评估中的潜在作用，但临床应用前仍需进行外部多中心验证。
肿瘤(癌症)患者之家