A Risk Prediction Model Based on Lymph-Node Metastasis in Poorly Differentiated-Type Intramucosal Gastric Cancer.

Journal Article (Journal Article)

BACKGROUND AND AIM: Endoscopic submucosal dissection (ESD) for undifferentiated type early gastric cancer is regarded as an investigational treatment. Few studies have tried to identify the risk factors that predict lymph-node metastasis (LNM) in intramucosal poorly differentiated adenocarcinomas (PDC). This study was designed to develop a risk scoring system (RSS) for predicting LNM in intramucosal PDC. METHODS: From January 2002 to July 2015, patients diagnosed with mucosa-confined PDC, among those who underwent curative gastrectomy with lymph node dissection were reviewed. A risk model based on independent predicting factors of LNM was developed, and its performance was internally validated using a split sample approach. RESULTS: Overall, LNM was observed in 5.2% (61) of 1169 patients. Four risk factors [Female sex, tumor size ≥ 3.2 cm, muscularis mucosa (M3) invasion, and lymphatic-vascular involvement] were significantly associated with LNM, which were incorporated into the RSS. The area under the receiver operating characteristic curve for predicting LNM after internal validation was 0.69 [95% confidence interval (CI), 0.59-0.79]. A total score of 2 points corresponded to the optimal RSS threshold with a discrimination of 0.75 (95% CI 0.69-0.81). The LNM rates were 1.6% for low risk (<2 points) and 8.9% for high-risk (≥2 points) patients, with a negative predictive value of 98.6% (95% CI 0.98-1.00). CONCLUSIONS: A RSS could be useful in clinical practice to determine which patients with intramucosal PDC have low risk of LNM.

Full Text

Duke Authors

Cited Authors

  • Pyo, JH; Lee, H; Min, B-H; Lee, JH; Choi, MG; Lee, JH; Sohn, TS; Bae, JM; Kim, K-M; Ahn, HS; Jung, S-H; Kim, S; Kim, JJ

Published Date

  • 2016

Published In

Volume / Issue

  • 11 / 5

Start / End Page

  • e0156207 -

PubMed ID

  • 27228258

Pubmed Central ID

  • PMC4881979

Electronic International Standard Serial Number (EISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0156207


  • eng

Conference Location

  • United States