Model to identify early-stage gastric cancers with deep invasion of submucosa based on endoscopy and endoscopic ultrasonography findings

作者：Cheng, Jieyao; Wu, Xi^*; Yang, Aiming^*; Jiang, Qingwei; Yao, Fang; Feng, Yunlu; Guo, Tao; Zhou, Weixun; Wu, Dongsheng; Yan, Xuemin; Lai, Yamin; Qian, Jiaming; Lu, Xinghua; Fang, Weigang

来源：Surgical Endoscopy and Other Interventional Techniques, 2018, 32(2): 855-863.

DOI：10.1007/s00464-017-5754-z

• Stomach cancer
• Prediction model
• Risk factor
• Endosonography

Background 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.
Methods 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 mu m from the muscularis mucosae (M-SM1) or penetration of 500 mu m 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.
Results 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.
Conclusions 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.

• 出版日期2018-2
• 单位中国医学科学院北京协和医院