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Anticancer Res. 2014 Aug;34(8):4471-4.

Prediction of lymph node metastasis in patients with submucosa-invading early gastric cancer.

Author information

1
Department of Surgery, Jikei University School of Medicine, Tokyo, Japan atsuoshida@ybb.ne.jp.
2
Department of Surgery, Jikei University School of Medicine, Tokyo, Japan.

Abstract

BACKGROUND AND AIM:

Early gastric cancer (EGC), with wall invasion limited to the submucosa, has approximately 15 to 20% chance of lymph node metastasis. The purpose of this study is to clarify the parameters which affect lymph node metastasis and survey whether lymph node metastasis can be predicted preoperatively.

PATIENTS AND METHODS:

We retrospectively analyzed 145 consecutive patients with EGC using multivariate analysis and developed a formula which predicts lymph node metastasis by linear discriminant analysis. In addition, we prospectively validated this formula in another subset of 106 consecutive patients with EGC and compared the predicted with the actual pathological lymph node metastasis.

RESULTS:

Multivariate analyses revealed that independent factors, which affect lymph node metastasis for EGC, were lymphatic system invasion (p=0.00002, odds ratio 3.11) and venous system invasion (p=0.039, odds ratio 2.44). In addition, we developed the lymph node metastasis-predicting formula using these two factors by linear discriminant analysis. The formula is as follows: Y=0.12 × (venous system invasion: 0, 1, 2 or 3) + 0.19 × (lymphatic system invasion: 0, 1, 2, or 3) - 0.14. If Y>0, we judge that a patient with gastric cancer is susceptible lymph node metastasis. The result of this prospective study showed that the sensitivity and specificity rates were 70% and 61.6%, respectively.

CONCLUSION:

We developed a formula which can predict lymph node metastasis using linear discriminant analysis. This formula seems useful in predicting for lymph node metastasis in patients with EGC.

KEYWORDS:

Gastric cancer; linear discriminant analysis; prediction of lymph node metastasis

PMID:
25075087
[Indexed for MEDLINE]

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