一项基于计算机断层扫描影像组学的胃癌微卫星不稳定状态术前预测的多中心研究。

A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics.

作者信息

Liang Xiuqun, Wu Yinbo, Liu Ying, Yu Danping, Huang Chencui, Li Zhi

机构信息

Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China.

Department of Radiology, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.

出版信息

Abdom Radiol (NY). 2022 Jun;47(6):2036-2045. doi: 10.1007/s00261-022-03507-3. Epub 2022 Apr 7.

DOI:10.1007/s00261-022-03507-3

PMID:35391567

Abstract

PURPOSE

To construct and validate a radiomics feature model based on computed tomography (CT) images and clinical characteristics to predict the microsatellite instability (MSI) status of gastric cancer patients before surgery.

METHODS

We retrospectively collected the upper abdominal or the entire abdominal-enhanced CT scans of 189 gastric cancer patients before surgery. The patients underwent postoperative gastric cancer MSI status testing, and the dates of their radiologic images and clinicopathological data were from January 2015 to August 2021. These 189 patients were divided into a training set (n = 90) and an external validation set (n = 99). The patients were divided by MSI status into the MSI-high (H) arm (30 and 33 patients in the training set and external validation set, respectively) and MSI-low/stable (L/S) arm (60 and 66 patients in the training set and external validation set, respectively). In the training set, the clinical characteristics and tumor radiologic characteristics of the patients were extracted, and the tenfold cross-validation method was used for internal validation of the training set. The external validation set was used to assess its generalized performance. A receiver-operating characteristic (ROC) curve was plotted to assess the model performance, and the area under the curve (AUC) was calculated.

RESULTS

The AUC of the radiomics model in the training set and external validation set was 0.8228 [95% confidence interval (CI) 0.7355-0.9101] and 0.7603 [95% CI 0.6625-0.8581], respectively, showing that the constructed radiomics model exhibited satisfactory generalization capabilities. The accuracy, sensitivity, and specificity of the training dataset were 0.72, 0.63, and 0.77, respectively. The accuracy, sensitivity, and specificity of the external validation dataset were 0.67, 0.79, and 0.60, respectively. Statistical analysis was carried out on the clinical data, and there was statistical significance for the tumor site and age (p < 0.05). MSI-H gastric cancer was mostly seen in the gastric antrum and older patients.

CONCLUSIONS

Radiomics markers based on CT images and clinical characteristics have the potential to be a non-invasive auxiliary diagnostic tool for preoperative assessment of gastric cancer MSI status, and they can aid in clinical decision-making and improve patient outcomes.

摘要

目的

构建并验证基于计算机断层扫描（CT）图像和临床特征的放射组学特征模型，以预测胃癌患者术前的微卫星不稳定性（MSI）状态。

方法

我们回顾性收集了189例胃癌患者术前的上腹部或全腹部增强CT扫描图像。这些患者均接受了术后胃癌MSI状态检测，其影像学图像和临床病理数据的日期范围为2015年1月至2021年8月。这189例患者被分为训练集（n = 90）和外部验证集（n = 99）。根据MSI状态将患者分为MSI高（H）组（训练集和外部验证集分别为30例和33例）和MSI低/稳定（L/S）组（训练集和外部验证集分别为60例和66例）。在训练集中，提取患者的临床特征和肿瘤影像学特征，并采用十折交叉验证法对训练集进行内部验证。外部验证集用于评估其泛化性能。绘制受试者操作特征（ROC）曲线以评估模型性能，并计算曲线下面积（AUC）。

结果

放射组学模型在训练集和外部验证集中的AUC分别为0.8228 [95%置信区间（CI）0.7355 - 0.9101]和0.7603 [95% CI 0.6625 - 0.8581]，表明构建的放射组学模型具有令人满意的泛化能力。训练数据集的准确性、敏感性和特异性分别为0.72、0.63和0.77。外部验证数据集的准确性、敏感性和特异性分别为0.67、0.79和0.60。对临床数据进行统计分析，发现肿瘤部位和年龄具有统计学意义（p < 0.05）。MSI-H胃癌多见于胃窦部和老年患者。