Division of Gastroenterology, Peking Union Medical College Hospital, 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Division of Pathology, Peking Union Medical College Hospital, Beijing, China.
Surg Endosc. 2018 Feb;32(2):855-863. doi: 10.1007/s00464-017-5754-z. Epub 2017 Jul 21.
Conventional endoscopy and endoscopic ultrasonography (EUS) are used to estimate the invasion depth of early-stage gastric cancers (EGCs), but estimates made by either technique are often inaccurate. We developed a model to determine the invasion depth of EGCs using conventional endoscopy and EUS findings, with pathology results as the reference.
We performed a retrospective study of 195 patients (205 lesions) diagnosed with gastric cancers who underwent endoscopy and EUS followed by resection. Based on pathology analyses, lesions (n = 205) were assigned to categories of: mucosa invasion or minute invasion into the submucosal layer less than 500 μm from the muscularis mucosae (M-SM1) or penetration of 500 μm or more (≥SM2). The lesions were randomly assigned to derivation (138 lesions) and validation sets (67 lesions). A depth predictive model was proposed in the derivation set using multivariate logistic regression analyses. The discriminative power of this model was assessed in both sets.
Remarkable redness (OR 5.42; 95% CI 1.32-22.29), abrupt cutting of converging folds (OR 8.58; 95% CI 1.65-44.72), lesions location in the upper third of the stomach (OR 10.26; 95% CI 2.19-48.09), and deep invasion based on EUS findings (OR 16.53; 95% CI 4.48-61.15) significantly associated with ≥SM2 invasion. A model that incorporated these 4 variables discriminated between M-SM1 and ≥SM2 lesions with the area under the ROC curve of 0.865 in the derivation set and 0.797 in the validation set. In the derivation set, a cut-off score of 8 identified lesions as ≥SM2 with 54% sensitivity and 97% specificity. The model correctly predicted the invasion depth 89.86% of lesions; it overestimated the depth of 2.17% of lesions.
We developed a model to identify EGCs with invasion depth ≥SM2 based on endoscopy and EUS findings. This model might reduce overestimation of gastric tumor depth and prevent unnecessary gastrectomy.
传统内镜和内镜超声(EUS)用于估计早期胃癌(EGC)的浸润深度,但两种技术的估计往往不准确。我们开发了一种使用内镜和 EUS 检查结果来确定 EGC 浸润深度的模型,以病理结果为参考。
我们对 195 例(205 处病灶)接受内镜和 EUS 检查后行切除术的胃癌患者进行了回顾性研究。根据病理分析,病灶(n=205)分为黏膜浸润或黏膜下浅层浸润小于 500μm(M-SM1)或穿透 500μm 或以上(≥SM2)。病灶随机分为推导组(138 个病灶)和验证组(67 个病灶)。在推导组中使用多变量逻辑回归分析提出了一个深度预测模型。在两个组中评估该模型的判别能力。
显著发红(OR 5.42;95%CI 1.32-22.29)、突然变细的汇聚褶皱(OR 8.58;95%CI 1.65-44.72)、病灶位于胃上三分之一(OR 10.26;95%CI 2.19-48.09)和 EUS 检查发现深部浸润(OR 16.53;95%CI 4.48-61.15)与≥SM2 浸润显著相关。纳入这 4 个变量的模型在推导组中区分 M-SM1 和≥SM2 病变的 AUC 为 0.865,在验证组中为 0.797。在推导组中,8 分的截断评分将病变识别为≥SM2,敏感性为 54%,特异性为 97%。该模型正确预测了 89.86%的病变浸润深度;它高估了 2.17%的病变深度。
我们开发了一种基于内镜和 EUS 检查结果识别浸润深度≥SM2 的 EGC 的模型。该模型可能会减少对胃肿瘤深度的高估,并防止不必要的胃切除术。