ArticleMedicine2016
A nomogram for predicting the likelihood of lymph node metastasis in early gastric signet ring cell carcinoma: A single center retrospective analysis with external validation.
Article in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- Endoscopic resection or radical gastrectomy? Risk factors for lymph node metastasis in early gastric cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach.Computational and mathematical methods in medicine · 2022Article
- Predictive factors for lymph node metastasis and defining a subgroup treatable for laparoscopic lymph node dissection after endoscopic submucosal dissection in poorly differentiated early gastric cancer.World journal of gastrointestinal oncology · 2018Article
- Clinical significance of expression of proliferating cell nuclear antigen and E-cadherin in gastric carcinoma.World journal of gastroenterology · 2017Article
- A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Treatment algorithm has not been established for early gastric cancer with signet ring cell carcinoma (SRC), which has a reported low rate of lymph node metastasis (LNM) similar to differentiated cancer. A cohort of 256 patients with early gastric SRC at our center between January 2002 and December 2015 were retrospectively reviewed. Multivariate logistic regression analysis was used to determine the independent factors of LNM. A nomogram for predicting LNM was constructed and internally validated. Additional external validation was performed using the database from Cancer Institute Ariake Hospital in Tokyo (n = 1273). Clinical performance of the model was assessed by decision analysis of curve. The overall LNM incidence was 12.9% (33/256). The multivariate logistic model identified sex, tumor size, and LVI as covariates associated with LNM. Subsequently, a nomogram consisted of sex, tumor size, and depth of invasion was established. The model showed qualified discrimination ability both in internal validation (area under curve, 0.801; 95% confidence interval [CI], 0.729-0.873) and in external dataset (area under curve, 0.707; 95% CI, 0.657-0.758). Based on the nomogram, treatment algorithm for early gastric SRC was proposed to assist clinicians in making better decisions. We developed a nomogram predicting risk of LNM for early gastric SRC, which should be helpful for patient counseling and surgical decision-making.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.