Department of Oral and Maxillofacial-Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, P.R. China.
Sci Rep. 2017 Jul 27;7(1):6708. doi: 10.1038/s41598-017-07134-7.
The lymph node ratio(LNR) has been described as a novel predictor of the survival of patients with oral and oropharyngeal squamous cell carcinoma(O/OPSCC). The purpose of this study was to evaluate whether LNR is better at predicting survival and the need for adjuvant treatment than traditional tumour-nodal-metastasis(TNM) staging. Eight hundred nine patients with O/OPSCC and positive lymph node disease were retrospectively enrolled in this study. LNR equal to 0.075 is the best cut-off value for stratifying 5-year disease-free survival(DFS). High LNR is closely associated with more advanced T stage, higher N stage, more severe pathological grade, the presence of diffuse infiltration and extracapsular spread(ECS). LNR is better for evaluating prognosis than the pathological N stage. Patients with high LNR coupled with high number of positive lymph nodes who received adjuvant concurrent chemo-radiotherapy(CCRT) had a better 5-year DFS than patients who received surgery alone. Multivariate analyses revealed that T stage, ECS and LNR are independent prognostic factors of 5-year DFS and disease-specific survival(DSS). Therefore, high LNR is closely correlated with adverse parameters that markedly hinder prognosis. LNR is superior to traditional TNM staging for the evaluation of prognosis,and the combination of the LNR with the number of positive lymph nodes can predict the benefits of adjuvant CCRT.
淋巴结比率(LNR)已被描述为一种预测口腔和口咽鳞状细胞癌(O/OPSCC)患者生存的新指标。本研究旨在评估 LNR 是否比传统的肿瘤-淋巴结-转移(TNM)分期更能预测生存和辅助治疗的需要。本研究回顾性纳入了 809 例 O/OPSCC 伴阳性淋巴结疾病的患者。LNR 等于 0.075 是分层 5 年无病生存率(DFS)的最佳截断值。高 LNR 与更晚期的 T 分期、更高的 N 分期、更严重的病理分级、弥漫性浸润和包膜外扩散(ECS)密切相关。LNR 比病理 N 分期更能评估预后。高 LNR 合并阳性淋巴结数量较多的患者接受辅助同步放化疗(CCRT)的 5 年 DFS 优于仅接受手术的患者。多因素分析显示,T 分期、ECS 和 LNR 是 5 年 DFS 和疾病特异性生存率(DSS)的独立预后因素。因此,高 LNR 与明显影响预后的不良参数密切相关。LNR 比传统的 TNM 分期更能评估预后,LNR 与阳性淋巴结数量的结合可以预测辅助 CCRT 的获益。