Background/Objectives:Cancer-associated cachexia in head and neck squamous cell carcinoma (HNSCC) is challenging to diagnose due to its complex pathophysiology. This study aimed to identify metabolic biomarkers linked to cachexia and survival in HNSCC patients using [18F]FDG-PET/CT imaging and machine learning (ML) techniques.Methods:We retrospectively analyzed 253 HNSCC patients from Vienna General Hospital and the MD Anderson Cancer Center. Automated organ segmentation was employed to quantify metabolic and volumetric data from [18F]FDG-PET/CT scans across 29 tissues and organs. Patients were categorized into low weight loss (LoWL; grades 0–2) and high weight loss (HiWL; grades 3–4) groups, according to the weight loss grading system (WLGS). Machine learning models, combined with Cox regression, were used to identify survival predictors. Shapley additive explanation (SHAP) analysis was conducted to determine the significance of individual features.Results:The HiWL group exhibited increased glucose metabolism in skeletal muscle and adipose tissue (p= 0.01), while the LoWL group showed higher lung metabolism. The one-year survival rate was 84.1% in the LoWL group compared to 69.2% in the HiWL group (p< 0.01). Pancreatic volume emerged as a key biomarker associated with cachexia, with the ML model achieving an AUC of 0.79 (95% CI: 0.77–0.80) and an accuracy of 0.82 (95% CI: 0.81–0.83). Multivariate Cox regression confirmed pancreatic volume as an independent prognostic factor (HR: 0.66, 95% CI: 0.46–0.95;p< 0.05).Conclusions:The integration of metabolic and volumetric data provided a strong predictive model, highlighting pancreatic volume as a key imaging biomarker in the metabolic assessment of cachexia in HNSCC. This finding enhances our understanding and may improve prognostic evaluations and therapeutic strategies.
**背景/目的：** 头颈部鳞状细胞癌相关的癌性恶病质因其复杂的病理生理机制而难以诊断。本研究旨在利用[18F]FDG-PET/CT成像和机器学习技术，识别与HNSCC患者恶病质及生存相关的代谢生物标志物。 **方法：** 我们回顾性分析了来自维也纳总医院和MD安德森癌症中心的253例HNSCC患者。采用自动化器官分割技术，对29个组织和器官的[18F]FDG-PET/CT扫描数据进行代谢和体积量化。根据体重减轻分级系统，将患者分为低体重减轻组（LoWL；0-2级）和高体重减轻组（HiWL；3-4级）。结合Cox回归的机器学习模型用于识别生存预测因子，并进行了SHAP分析以确定各特征的重要性。 **结果：** HiWL组在骨骼肌和脂肪组织中表现出葡萄糖代谢增高（p=0.01），而LoWL组则显示肺部代谢较高。LoWL组的一年生存率为84.1%，而HiWL组为69.2%（p<0.01）。胰腺体积成为与恶病质相关的关键生物标志物，ML模型的AUC为0.79（95% CI：0.77-0.80），准确率为0.82（95% CI：0.81-0.83）。多变量Cox回归证实胰腺体积是一个独立的预后因素（HR：0.66，95% CI：0.46-0.95；p<0.05）。 **结论：** 代谢与体积数据的整合提供了一个强大的预测模型，凸显了胰腺体积作为HNSCC恶病质代谢评估中的一个关键影像学生物标志物。这一发现增进了我们的理解，并可能改善预后评估和治疗策略。
肿瘤(癌症)患者之家