胃癌（GC）患者的生存预后通常会影响医生对其后续治疗的选择。本研究旨在开发基于正电子发射断层扫描（PET）的放射组学模型结合临床肿瘤-淋巴结-转移（TNM）分期来预测GC患者的总生存期（OS）。我们回顾了总共327例患者的临床信息接受 18 F-氟脱氧葡萄糖 (18 F-FDG) PET 扫描并进行病理学确诊的 GC 患者。将患者随机分为训练组 (n = 229) 和验证组 (n = 98)。我们从 PET 图像中提取了 171 个 PET 放射组学特征，并使用最小绝对收缩和选择算子 (LASSO) 和随机生存森林 (RSF) 确定 PET 放射组学分数 (RS)。构建了包括 PET RS 和临床 TNM 分期在内的放射组学模型来预测 GC 患者的 OS。该模型进行了区分度、校准和临床实用性评估。在多变量COX回归分析中，GC患者的年龄、癌胚抗原(CEA)、临床TNM和PET RS之间的差异具有统计学意义(p<0.05)。基于 COX 回归的结果开发了放射组学模型。该模型的 Harrell 一致性指数（C 指数）在训练队列中为 0.817，在验证队列中为 0.707，表现优于单一临床模型和结合临床特征结合临床 TNM 分期的模型。进一步分析显示，年龄较大的患者 (p<0.001)、CEA 升高 (p<0.001) 和临床 TNM 较高的患者 (p<0.001) PET RS 较高。在不同的临床 TNM 阶段，较高的 PET RS 与较差的生存预后相关。基于 PET RS、临床 TNM 和临床特征的放射组学模型可能为预测 GC 患者的 OS 提供新工具。© 2024。作者）。

Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC.We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness.On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis.Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.© 2024. The Author(s).