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A Risk-prediction Model Based on Lymph-node Metastasis for Incorporation Into a Treatment Algorithm for Signet Ring Cell-type Intramucosal Gastric Cancer.

Publication ,  Journal Article
Pyo, JH; Shin, CM; Lee, H; Min, B-H; Lee, JH; Kim, SM; Choi, MG; Lee, JH; Sohn, TS; Bae, JM; Kim, KM; Kim, HS; Jung, S-H; Kim, JJ; Kim, S ...
Published in: Ann Surg
December 2016

OBJECTIVE: The aim of the study was to develop a reliable and easy-to-use risk-scoring system (RSS) to predict lymph-node metastasis (LNM) and determine the feasibility of endoscopic submucosal dissection for mucosa-confined signet ring cell carcinomas (SRCs). BACKGROUND: Fewer LNM and better survival rates have been reported for early gastric SRCs compared with other undifferentiated early gastric cancers (EGCs). METHODS: Data from 1544 patients with mucosa-confined SRCs were reviewed. Stepwise logistic regression analysis determined the independent predictors of LNM. Risk scores were based on the final predictive factors for LNM, and performance was internally validated using a split-sample approach. External validation was also performed in an independent dataset (n = 208) to assess the discriminatory power of the RSS. RESULTS: The overall LNM incidence was 3.8% (57/1544). Three risk factors (tumor size ≥1.7 cm, tumors of elevated type, and lymphatic-vascular involvement) were significantly associated with LNM. These factors were incorporated into the RSS, and were assigned scores ranging from 0 to 4. The area under the receiver-operating characteristic curve for predicting LNM after internal and external validation was 0.68 (95% confidence interval, 0.0793-0.2865) and 0.686 (95% confidence interval, 0.618-0.748), respectively. A score of 2 points was the optimal cut-off value for LNM prediction, and the overall diagnostic accuracy was 96%. LNM were found in 2.9% and 23.8% of the low and high-risk groups of the RSS, respectively. CONCLUSIONS: A RSS may help to predict LNM and evaluate endoscopic submucosal dissection feasibility in patients with intramucosal SRC.

Duke Scholars

Published In

Ann Surg

DOI

EISSN

1528-1140

Publication Date

December 2016

Volume

264

Issue

6

Start / End Page

1038 / 1043

Location

United States

Related Subject Headings

  • Survival Rate
  • Surgery
  • Stomach Neoplasms
  • Risk Factors
  • Risk Assessment
  • Prospective Studies
  • Predictive Value of Tests
  • Neoplasm Staging
  • Neoplasm Grading
  • Middle Aged
 

Citation

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Pyo, J. H., Shin, C. M., Lee, H., Min, B.-H., Lee, J. H., Kim, S. M., … JHP and CMS contributed equally as the first authors of this study., . (2016). A Risk-prediction Model Based on Lymph-node Metastasis for Incorporation Into a Treatment Algorithm for Signet Ring Cell-type Intramucosal Gastric Cancer. Ann Surg, 264(6), 1038–1043. https://doi.org/10.1097/SLA.0000000000001602
Pyo, Jeung Hui, Cheol Min Shin, Hyuk Lee, Byung-Hoon Min, Jun Haeng Lee, Su Mi Kim, Min Gew Choi, et al. “A Risk-prediction Model Based on Lymph-node Metastasis for Incorporation Into a Treatment Algorithm for Signet Ring Cell-type Intramucosal Gastric Cancer.Ann Surg 264, no. 6 (December 2016): 1038–43. https://doi.org/10.1097/SLA.0000000000001602.
Pyo, Jeung Hui, et al. “A Risk-prediction Model Based on Lymph-node Metastasis for Incorporation Into a Treatment Algorithm for Signet Ring Cell-type Intramucosal Gastric Cancer.Ann Surg, vol. 264, no. 6, Dec. 2016, pp. 1038–43. Pubmed, doi:10.1097/SLA.0000000000001602.
Pyo JH, Shin CM, Lee H, Min B-H, Lee JH, Kim SM, Choi MG, Sohn TS, Bae JM, Kim KM, Kim HS, Jung S-H, Kim JJ, Kim S, JHP and CMS contributed equally as the first authors of this study. A Risk-prediction Model Based on Lymph-node Metastasis for Incorporation Into a Treatment Algorithm for Signet Ring Cell-type Intramucosal Gastric Cancer. Ann Surg. 2016 Dec;264(6):1038–1043.

Published In

Ann Surg

DOI

EISSN

1528-1140

Publication Date

December 2016

Volume

264

Issue

6

Start / End Page

1038 / 1043

Location

United States

Related Subject Headings

  • Survival Rate
  • Surgery
  • Stomach Neoplasms
  • Risk Factors
  • Risk Assessment
  • Prospective Studies
  • Predictive Value of Tests
  • Neoplasm Staging
  • Neoplasm Grading
  • Middle Aged