Xie Jiageng, Xue Beihui, Bian Shuying, Ji Xiaowei, Lin Jie, Zheng Xiangwu, Tang Kun
Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Departments of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Nucl Med Commun. 2023 Nov 1;44(11):977-987. doi: 10.1097/MNM.0000000000001742. Epub 2023 Aug 14.
Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC.
In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram.
Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively.
The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.
腹膜转移（PM）通常被认为是胃癌（GC）的一个不可治愈因素，不适合手术。本研究的目的是开发并验证一种基于18F-FDG PET/CT的影像组学模型，并结合临床危险因素来预测GC的PM。
在这项回顾性研究中，分析了2015年1月至2021年10月期间接受术前18F-FDG PET/CT检查的410例GC患者（PM- = 281例，PM+ = 129例）。患者被随机分为训练队列（n = 288）和验证队列（n = 122）。应用最大相关最小冗余（mRMR）和最小绝对收缩和选择算子方法来选择特征。进行多变量逻辑回归分析以建立预测模型。使用辨别力、校准和临床实用性来评估列线图的性能。
选择了14个影像组学特征参数来构建影像组学模型。影像组学模型在训练队列中的曲线下面积（AUC）为0.86 [95%置信区间（CI），0.81 - 0.90]，在验证队列中为0.85（95% CI，0.78 - 0.92）。经过多变量逻辑回归分析，不同PM状态患者之间的腹腔积液、平均标准化摄取值（SUVmean）、糖类抗原125（CA125）和影像组学特征显示出统计学显著差异（P < 0.05）。选择它们构建综合预测模型，该模型在训练队列中的AUC为0.92（95% CI，0.89 - 0.95），在验证队列中为0.92（95% CI，0.86 - 0.98）。
基于18F-FDG PET/CT影像组学特征和临床危险因素的列线图可能应用于GC患者术前个体化治疗策略的制定。
