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A prediction model for lymph node metastasis in early-stage gastric cancer: Toward tailored lymphadenectomy.
- Source :
-
Journal of surgical oncology [J Surg Oncol] 2019 Sep; Vol. 120 (4), pp. 670-675. Date of Electronic Publication: 2019 Jul 13. - Publication Year :
- 2019
-
Abstract
- Background: The aim of this study was to develop a prediction model for the presence and location of lymph node metastasis (LNM) in early gastric cancer.<br />Method: We reviewed medical records of 4 929 patients who underwent radical gastrectomy for early gastric cancer. Variables of age, sex, lymphatic invasion, depth of invasion, location, gross type, differentiation, and tumor size were analyzed. Logistic regression analysis was used to determine independent predictors of LNM at each LN station.<br />Result: Overall incidence of LNM was 9.1% (448/4 929 patients). For the presence of LNM, risk factors of age, sex, lymphatic invasion, depth of invasion, anatomical part, gross ulceration, size, and tumor differentiation were significantly associated with LNM. The area under the curve (AUC) for predicting LNM after validation was 0.834 for the test set. For the location of LNM, age, sex, lymphatic invasion, depth of invasion, anatomical part, circumferential portion, gross type, differentiation, and tumor size were significantly associated with LNM. The AUC of each LN station was favorable with the test set.<br />Conclusion: Predicting the location of metastatic LNs appeared to be possible in patients with early gastric cancer.<br /> (© 2019 Wiley Periodicals, Inc.)
- Subjects :
- Adenocarcinoma surgery
Early Detection of Cancer
Female
Follow-Up Studies
Humans
Lymph Nodes surgery
Lymphatic Metastasis
Male
Middle Aged
Neoplasm Invasiveness
Predictive Value of Tests
Prognosis
ROC Curve
Risk Factors
Stomach Neoplasms surgery
Adenocarcinoma secondary
Gastrectomy methods
Lymph Node Excision methods
Lymph Nodes pathology
Models, Statistical
Stomach Neoplasms pathology
Subjects
Details
- Language :
- English
- ISSN :
- 1096-9098
- Volume :
- 120
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Journal of surgical oncology
- Publication Type :
- Academic Journal
- Accession number :
- 31301150
- Full Text :
- https://doi.org/10.1002/jso.25628