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A nomogram for predicting the likelihood of lymph node metastasis in early gastric signet ring cell carcinoma

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus

A nomogram predicting lymph node metastasis for early gastric cancer with signet ring cell carcinoma. Each level within variables was assigned a score according to the point scale. By summing up the total score and locating it on the total point scale, a corresponding probability of lymph node metastasis for each individual was determined.
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Figure 1: A nomogram predicting lymph node metastasis for early gastric cancer with signet ring cell carcinoma. Each level within variables was assigned a score according to the point scale. By summing up the total score and locating it on the total point scale, a corresponding probability of lymph node metastasis for each individual was determined.

Mentions: A nomogram predicting risk of LNM was established on the basis of the multivariate logistic regression model. Tumor size was the largest contributor to the score, and then followed by sex and depth of invasion in Fig. 1.


A nomogram for predicting the likelihood of lymph node metastasis in early gastric signet ring cell carcinoma
A nomogram predicting lymph node metastasis for early gastric cancer with signet ring cell carcinoma. Each level within variables was assigned a score according to the point scale. By summing up the total score and locating it on the total point scale, a corresponding probability of lymph node metastasis for each individual was determined.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120931&req=5

Figure 1: A nomogram predicting lymph node metastasis for early gastric cancer with signet ring cell carcinoma. Each level within variables was assigned a score according to the point scale. By summing up the total score and locating it on the total point scale, a corresponding probability of lymph node metastasis for each individual was determined.
Mentions: A nomogram predicting risk of LNM was established on the basis of the multivariate logistic regression model. Tumor size was the largest contributor to the score, and then followed by sex and depth of invasion in Fig. 1.

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus