Jieyao Cheng1, Xi Wu2, Aiming Yang3, Qingwei Jiang1, Fang Yao1, Yunlu Feng1, Tao Guo1, Weixun Zhou4, Dongsheng Wu1, Xuemin Yan1, Yamin Lai1, Jiaming Qian1, Xinghua Lu1, Weigang Fang5. 1. Division of Gastroenterology, Peking Union Medical College Hospital, 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China. 2. Division of Gastroenterology, Peking Union Medical College Hospital, 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China. wxpumch@163.com. 3. Division of Gastroenterology, Peking Union Medical College Hospital, 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China. yangaiming@medmail.com.cn. 4. Division of Pathology, Peking Union Medical College Hospital, Beijing, China. 5. Division of General Internal Medicine, Peking Union Medical College Hospital, Beijing, China.
Abstract
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 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. 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.
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 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. 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.
Entities:
Keywords:
Endosonography; Prediction model; Risk factor; Stomach cancer
Authors: Jaffer A Ajani; Thomas A D'Amico; Khaldoun Almhanna; David J Bentrem; Joseph Chao; Prajnan Das; Crystal S Denlinger; Paul Fanta; Farhood Farjah; Charles S Fuchs; Hans Gerdes; Michael Gibson; Robert E Glasgow; James A Hayman; Steven Hochwald; Wayne L Hofstetter; David H Ilson; Dawn Jaroszewski; Kimberly L Johung; Rajesh N Keswani; Lawrence R Kleinberg; W Michael Korn; Stephen Leong; Catherine Linn; A Craig Lockhart; Quan P Ly; Mary F Mulcahy; Mark B Orringer; Kyle A Perry; George A Poultsides; Walter J Scott; Vivian E Strong; Mary Kay Washington; Benny Weksler; Christopher G Willett; Cameron D Wright; Debra Zelman; Nicole McMillian; Hema Sundar Journal: J Natl Compr Canc Netw Date: 2016-10 Impact factor: 11.908
Authors: Jung Ho Shim; Kyo Young Song; Hae Myung Jeon; Cho Hyun Park; Lindsay M Jacks; Mithat Gonen; Manish A Shah; Murray F Brennan; Daniel G Coit; Vivian E Strong Journal: Ann Surg Oncol Date: 2014-03-06 Impact factor: 5.344