quinta-feira, 1 de dezembro de 2016

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

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

Pyo, Jeung Hui MD; Shin, Cheol Min MD; Lee, Hyuk MD, PhD; Min, Byung-Hoon MD, PhD; Lee, Jun Haeng MD, PhD; Kim, Su Mi MD; Choi, Min Gew MD, PhD; Lee, Jun Ho MD, PhD; Sohn, Tae Sung MD, PhD; Bae, Jae Moon MD, PhD; Kim, Kyung Mee MD, PhD; Kim, Hye Seung MS; Jung, Sin-Ho PhD; Kim, Jae J. MD, PhD; Kim, Sung MD, PhD; JHP and CMS contributed equally as the first authors of this study.
doi: 10.1097/SLA.0000000000001602
Original Articles

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).

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.

http://journals.lww.com/annalsofsurgery/Abstract/2016/12000/A_Risk_prediction_Model_Based_on_Lymph_node.26.aspx

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