Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China.
Acta Radiol. 2022 Aug;63(8):1005-1013. doi: 10.1177/02841851211029083. Epub 2021 Jul 7.
The relevance of Epstein-Barr virus (EBV) in gastric carcinoma has been represented by the existence of EBV-encoded small RNA (EBER) in the tumor cells and has prognostic significance in gastric cancer, while gastric adenocarcinoma represents the most frequently occurring gastric malignancy.
To observe the capacity of radiomic features extracted from contrast-enhanced computed tomography (CE-CT) images to differentiate EBER-positive gastric adenocarcinoma from EBER-negative ones.
A total of 54 patients with gastric adenocarcinoma (EBER-positive: 27, EBER-negative: 27) were retrospectively examined. Radiomic imaging features were extracted from all regions of interest (ROI) delineated by two experienced radiologists on late arterial phase CT images. We distinguished related radiomic features through the two-tailed t test and applied them to construct a decision tree model to evaluate whether EBER in situ hybridization positive had appeared.
Nine radiomics features were significantly related to EBER in situ hybridization status ( < 0.05), four of which were used to build the decision tree through backward elimination: Correlation_ AllDirection_offset7, Correlation_ angle135_offset7, RunLengthNonuniformity_ AllDirection_offset1_SD, and HighGreyLevelRunEmphasis_ AllDiretion_offset1_SD. The decision tree model consisted of seven decision nodes and six terminal nodes, three of which demonstrated positive EBER in situ hybridization. The specificity, sensitivity, and accuracy of the model were 84%, 80%, and 81.7%, respectively. The area under the curve of the decision tree model was 0.87.
Radiomics based on CE-CT could be applied to predict EBER in situ hybridization status preoperatively in patients with gastric adenocarcinoma.
EB 病毒（EBV）在胃癌中的相关性表现为肿瘤细胞中存在 EBV 编码的小 RNA（EBER），并且在胃癌中具有预后意义，而胃腺癌则代表最常见的胃恶性肿瘤。
观察从增强 CT（CE-CT）图像中提取的放射组学特征区分 EBER 阳性胃腺癌和 EBER 阴性胃腺癌的能力。
回顾性分析了 54 例胃腺癌患者（EBER 阳性：27 例，EBER 阴性：27 例）。由两位有经验的放射科医生在动脉晚期 CT 图像上勾画所有感兴趣区（ROI），提取放射组学特征。我们通过双尾 t 检验区分相关放射组学特征，并应用它们构建决策树模型，以评估 EBER 原位杂交是否呈阳性。
有 9 个放射组学特征与 EBER 原位杂交状态显著相关（ < 0.05），其中 4 个特征通过后向消除用于构建决策树：Correlation_ AllDirection_offset7、Correlation_ angle135_offset7、RunLengthNonuniformity_ AllDirection_offset1_SD 和 HighGreyLevelRunEmphasis_ AllDiretion_offset1_SD。决策树模型由七个决策节点和六个终端节点组成，其中三个节点表现为 EBER 原位杂交阳性。该模型的特异性、敏感性和准确性分别为 84%、80%和 81.7%，决策树模型的曲线下面积为 0.87。
基于 CE-CT 的放射组学可用于预测胃腺癌患者术前 EBER 原位杂交状态。
