Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Department of Nuclear Medicine, Shuyang Hospital Affiliated to Medical College of Yangzhou University, Suqian, China.
BMC Cancer. 2024 Nov 14;24(1):1402. doi: 10.1186/s12885-024-13157-x.
This study aimed to develop a predictive model utilizing radiomics and body composition features derived from F-FDG PET/CT scans to forecast progression-free survival (PFS) and overall survival (OS) outcomes in patients with esophageal squamous cell carcinoma (ESCC).
We analyzed data from 91 patients who underwent baseline F-FDG PET/CT imaging. Radiomic features extracted from PET and CT images and subsequent radiomics scores (Rad-scores) were calculated. Body composition metrics were also quantified, including muscle and fat distribution at the L3 level from CT scans. Multiparametric survival models were constructed using Cox regression analysis, and their performance was assessed using the area under the time-dependent receiver operating characteristic (ROC) curve (AUC) and concordance index (C-index).
Multivariate analysis identified Rad-score (P = 0.003), sarcopenia (P < 0.001), and visceral adipose tissue index (VATI) (P < 0.001) as independent predictors of PFS. For OS, Rad-score (P = 0.001), sarcopenia (P = 0.002), VATI (P = 0.037), stage (P = 0.042), and body mass index (BMI) (P = 0.008) were confirmed as independent prognostic factors. Integration of the Rad-score with clinical variables and body composition parameters enhanced predictive accuracy, yielding C-indices of 0.810 (95% CI: 0.737-0.884) for PFS and 0.806 (95% CI: 0.720-0.891) for OS.
This study underscored the potential of combining Rad-score with clinical and body composition data to refine prognostic assessment in ESCC patients.
本研究旨在开发一种预测模型，利用来自 F-FDG PET/CT 扫描的影像组学和身体成分特征来预测食管鳞状细胞癌（ESCC）患者的无进展生存期（PFS）和总生存期（OS）结局。
我们分析了 91 例基线 F-FDG PET/CT 成像患者的数据。从 PET 和 CT 图像中提取影像组学特征并计算相应的影像组学评分（Rad-score）。还定量了身体成分指标，包括 CT 扫描 L3 水平的肌肉和脂肪分布。使用 Cox 回归分析构建多参数生存模型，并使用时间依赖性接收器操作特征（ROC）曲线下面积（AUC）和一致性指数（C-index）评估其性能。
多变量分析确定 Rad-score（P=0.003）、肌肉减少症（P<0.001）和内脏脂肪组织指数（VATI）（P<0.001）是 PFS 的独立预测因子。对于 OS，Rad-score（P=0.001）、肌肉减少症（P=0.002）、VATI（P=0.037）、分期（P=0.042）和体重指数（BMI）（P=0.008）被确认为独立的预后因素。Rad-score 与临床变量和身体成分参数的整合提高了预测准确性，PFS 的 C-index 为 0.810（95%CI：0.737-0.884），OS 的 C-index 为 0.806（95%CI：0.720-0.891）。
本研究强调了将 Rad-score 与临床和身体成分数据相结合以细化 ESCC 患者预后评估的潜力。
