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

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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.

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Abbreviations

AUROC:

Area under receiver operating characteristic

CI:

Confidence interval

EGC:

Early gastric cancer

ESD:

Endoscopic submucosal dissection

EUS:

Endoscopic ultrasonography

LR+:

Positive likelihood ratio

MPHD:

Model for prediction of histologic depth

NCCN:

National comprehensive cancer network

NPV:

Negative predictive value

PPV:

Positive predictive value

SM:

Submucosal cancer

SGC:

Superficial gastric cancer

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Funding

National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2013BAI01B10); Beijing Municipal Science and Technology Project (D141100000414005).

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Authors and Affiliations

  1. Division of Gastroenterology, Peking Union Medical College Hospital, 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China

    Jieyao Cheng, Xi Wu, Aiming Yang, Qingwei Jiang, Fang Yao, Yunlu Feng, Tao Guo, Dongsheng Wu, Xuemin Yan, Yamin Lai, Jiaming Qian & Xinghua Lu

  2. Division of Pathology, Peking Union Medical College Hospital, Beijing, China

    Weixun Zhou

  3. Division of General Internal Medicine, Peking Union Medical College Hospital, Beijing, China

    Weigang Fang

Authors
  1. Jieyao Cheng
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  2. Xi Wu
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  3. Aiming Yang
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  4. Qingwei Jiang
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  5. Fang Yao
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  6. Yunlu Feng
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  7. Tao Guo
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  8. Weixun Zhou
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  9. Dongsheng Wu
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  10. Xuemin Yan
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  11. Yamin Lai
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  12. Jiaming Qian
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  13. Xinghua Lu
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  14. Weigang Fang
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Corresponding authors

Correspondence to Xi Wu or Aiming Yang.

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Disclosures

Drs. Jieyao Cheng, Xi Wu, Aiming Yang, Qingwei Jiang, Fang Yao, Yunlu Feng, Tao Guo, Weixun Zhou, Dongsheng Wu, Xuemin Yan, Yamin Lai, Jiaming Qian, Xinghua Lu, and Weigang Fang have no conflicts of interest or financial ties to disclose.

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Cheng, J., Wu, X., Yang, A. et al. Model to identify early-stage gastric cancers with deep invasion of submucosa based on endoscopy and endoscopic ultrasonography findings. Surg Endosc 32, 855–863 (2018). https://doi.org/10.1007/s00464-017-5754-z

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  • DOI: https://doi.org/10.1007/s00464-017-5754-z

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