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Validation of the prognostic value of lymph node ratio in patients with penile squamous cell carcinoma: A population-based study

机译:阴茎鳞状细胞癌患者淋巴结比率的预后价值的验证:一项基于人群的研究

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Purpose: The aim of this study was to validate the prognostic value of lymph node ratio (LNR), the proportion of metastatic among removed lymph nodes, for patients with penile squamous cell carcinoma in a population-based database. Methods: A total of 210 eligible patients with node-positive disease were identified from the surveillance epidemiology end results database. Cancer-specific survival (CSS) was the clinical outcome of interest. The prognostic ability of LNR was assessed by Cox regression analyses. Logrank test was used to compare CSS between low-risk and high-risk groups stratified by cutoff points of LNR. Results: The median number of LNs removed was 16, and the median value of LNR was 0.20. First, LNR was a significant prognostic factor of CSS in univariate analysis (HR = 4.08). Second, LNR retained independent predictive ability (HR = 6.74) in the multivariate model including demographic data, disease characteristics and number-based LN variables. Addition of LNR remarkably improved the predictive accuracy and clinical usefulness of the survival model. Third, maximum stratification of CSS can be achieved at the cutoff point of 0.33. Conclusion: In the population-based study, LNR outperformed number-based LN variables for predicting CSS of node-positive penile cancer. The ratio-based prognostic factor stresses the important role of adequate LND and identification of metastatic LNs in the community setting.
机译:目的:本研究的目的是在基于人群的数据库中验证淋巴结比率(LNR),切除的淋巴结之间转移的比例对阴茎鳞状细胞癌患者的预后价值。方法:从监测流行病学最终结果数据库中鉴定出总共210例符合条件的淋巴结阳性疾病患者。特定于癌症的生存(CSS)是令人感兴趣的临床结果。通过Cox回归分析评估LNR的预后能力。使用Logrank检验比较以LNR截止点分层的低风险和高风险组之间的CSS。结果:去除的LN的中位数为16,LNR的中值为0.20。首先,在单变量分析中,LNR是CSS的重要预后因素(HR = 4.08)。其次,LNR在包括人口统计数据,疾病特征和基于数字的LN变量在内的多变量模型中保留了独立的预测能力(HR = 6.74)。添加LNR可以显着提高生存模型的预测准确性和临床实用性。第三,CSS的最大分层可以在临界点0.33处实现。结论:在基于人群的研究中,LNR在预测淋巴结阳性阴茎癌CSS方面优于基于数量的LN变量。基于比率的预后因素强调了在社区环境中适当的LND和转移性LNs识别的重要作用。

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