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唇癌淋巴结转移预测模型及选择性颈清扫术建议

Prediction model for lymph node metastasis and recommendations for elective neck dissection in lip cancer.

作者信息

Wermker Kai, Belok Friederike, Schipmann Stephanie, Klein Martin, Schulze Hans-Joachim, Hallermann Christian

机构信息

Fachklinik Hornheide at the Westfalian Wilhelms-University of Muenster, Head and Neck Cancer Centre, Department of Cranio-Maxillofacial Surgery, Muenster, Germany.

Fachklinik Hornheide at the Westfalian Wilhelms-University of Muenster, Head and Neck Cancer Centre, Department of Cranio-Maxillofacial Surgery, Muenster, Germany.

出版信息

J Craniomaxillofac Surg. 2015 May;43(4):545-52. doi: 10.1016/j.jcms.2015.02.002. Epub 2015 Feb 11.

Abstract

INTRODUCTION

In patients with squamous cell carcinoma (SCC) of the lip, occurrence of lymph node metastasis (LNM) is more frequent than in other cutaneous head and neck SCCs. The aim of this study was to identify predictive factors for LNM in SCC of the lip and to establish a prediction model identifying patients at high LNM risk.

MATERIALS AND METHODS

Tumor characteristics of 326 patients with lip SCC were analyzed retrospectively to assess differences between the LNM group and controls. Using binary logistic and Cox regression analysis, a prediction model for LNM was calculated.

RESULTS

Lymph node metastasis occurred in 26 (8%) patients. Regression analysis revealed tumor extent, tumor depth and grading as the most important factors in the correct classification of LNM in 94.2% of patients. A prediction model taking tumor depth and grading into account allowed for stratification of patients into high and low risk groups (sensitivity 92.3%, specificity 78.3%, negative predictive value 99.2%).

CONCLUSIONS

Our new prediction model was able to identify patients with lip cancer who had a high risk of LNM with a good level of accuracy. This algorithm is easy to apply as part of the decision process for elective and selective lymph node dissection in SCC of the lip.

摘要

引言

唇鳞状细胞癌(SCC)患者发生淋巴结转移(LNM)的频率高于其他头颈部皮肤SCC。本研究的目的是确定唇SCC中LNM的预测因素,并建立一个识别LNM高风险患者的预测模型。

材料与方法

回顾性分析326例唇SCC患者的肿瘤特征,以评估LNM组与对照组之间的差异。使用二元逻辑回归和Cox回归分析,计算LNM的预测模型。

结果

26例(8%)患者发生淋巴结转移。回归分析显示,肿瘤范围、肿瘤深度和分级是94.2%患者LNM正确分类的最重要因素。一个考虑肿瘤深度和分级的预测模型可将患者分为高风险组和低风险组(敏感性92.3%,特异性78.3%,阴性预测值99.2%)。

结论

我们的新预测模型能够准确识别唇癌中LNM高风险患者。该算法易于应用,可作为唇SCC选择性和选择性淋巴结清扫决策过程的一部分。

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