A preoperative nomogram for the prediction of ipsilateral central compartment lymph node metastases in papillary thyroid cancer
- Publication Type:
- Journal Article
- Thyroid, 2014, 24 (4), pp. 675 - 682
- Issue Date:
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Background: Central compartment lymph node metastases in papillary thyroid carcinoma (PTC) are difficult to detect preoperatively, and the role of routine or prophylactic central compartment lymph node dissection (CLND) in managing PTC remains controversial. The aim of this project was to create a nomogram able to predict the occurrence of central compartment lymph node metastasis using readily available preoperative clinical characteristics. Methods: Records from patients undergoing total thyroidectomy and lymph node dissection for PTC in the period 1968-2012 were analyzed. Nodal status was based on results of serial hematoxylin and eosin (H&E) examination. Age, sex, tumor size, tumor site, and multifocality were included in a multivariable logistic regression model to predict lymph node metastasis. A coefficient-based nomogram was developed and validated using an external patient cohort. Results: The study population included 914 patients (80% females) with an average central compartment nodal yield of eight per patient. Central compartment lymph node metastases were present in 390 patients (42.7%). The variables with the strongest predictive value were age (p<0.001), male sex (p<0.001), increasing tumor size (p<0.001), and tumor multifocality (p<0.05). The nomogram had good discrimination with a concordance index of 76.4% [95% confidence interval 73.3-79.4], supported by an external validation point estimate of 61.5% [95% confidence interval 49.5-73.6]. An online calculator and smartphone application were developed for point of care use. Conclusions: A validated nomogram utilizing readily available preoperative variables has been developed to give a predicted probability of central lymph node metastases in patients presenting with PTC. This nomogram may help guide surgical decision making in PTC. © 2014, Mary Ann Liebert, Inc. 2014.
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