Add like
Add dislike
Add to saved papers

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

Annals of Surgery 2016 December
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.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app