Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, PR China.
Oral Oncol. 2024 Apr;151:106725. doi: 10.1016/j.oraloncology.2024.106725. Epub 2024 Mar 1.
Non-anatomical factors significantly affect treatment guidance and prognostic prediction in nasopharyngeal carcinoma (NPC) patients. Here, we developed a novel survival model by combining conventional TNM staging and serological indicators.
We retrospectively enrolled 10,914 eligible patients with nonmetastatic NPC over 2009-2017 and randomly divided them into training (n = 7672) and validation (n = 3242) cohorts. The new staging system was constructed based on T category, N category, and pretreatment serological markers by using recursive partitioning analysis (RPA).
In multivariate Cox analysis, pretreatment cell-free Epstein-Barr virus (cfEBV) DNA levels of >2000 copies/mL [HR (95 % CI) = 1.78 (1.57-2.02)], elevated lactate dehydrogenase (LDH) levels [HR (95 % CI) = 1.64 (1.41-1.92)], and C-reactive protein-to-albumin ratio (CAR) of >0.04 [HR (95 % CI) = 1.20 (1.07-1.34)] were associated with negative prognosis (all P < 0.05). Through RPA, we stratified patients into four risk groups: RPA I (n = 3209), RPA II (n = 2063), RPA III (n = 1263), and RPA IV (n = 1137), with 5-year overall survival (OS) rates of 93.2 %, 86.0 %, 80.6 %, and 71.9 % (all P < 0.001), respectively. Compared with the TNM staging system (eighth edition), RPA risk grouping demonstrated higher prognostic prediction efficacy in the training [area under the curve (AUC) = 0.661 vs. 0.631, P < 0.001] and validation (AUC = 0.687 vs. 0.654, P = 0.001) cohorts. Furthermore, our model could distinguish sensitive patients suitable for induction chemotherapy well.
Our novel RPA staging model outperformed the current TNM staging system in prognostic prediction and clinical decision-making. We recommend incorporating cfEBV DNA, LDH, and CAR into the TNM staging system.
非解剖因素显著影响鼻咽癌（NPC）患者的治疗指导和预后预测。在这里，我们通过结合常规 TNM 分期和血清学指标开发了一种新的生存模型。
我们回顾性纳入了 2009 年至 2017 年间的 10914 名非转移性 NPC 合格患者，并将其随机分为训练（n=7672）和验证（n=3242）队列。通过递归分区分析（RPA），根据 T 分期、N 分期和预处理血清学标志物构建新的分期系统。
在多变量 Cox 分析中，预处理游离 Epstein-Barr 病毒（cfEBV）DNA 水平>2000 拷贝/ml[风险比（95%可信区间）=1.78（1.57-2.02）]、乳酸脱氢酶（LDH）水平升高[风险比（95%可信区间）=1.64（1.41-1.92）]和 C 反应蛋白与白蛋白比值（CAR）>0.04[风险比（95%可信区间）=1.20（1.07-1.34）]与预后不良相关（均 P<0.05）。通过 RPA，我们将患者分为四个风险组：RPA I（n=3209）、RPA II（n=2063）、RPA III（n=1263）和 RPA IV（n=1137），5 年总生存率（OS）分别为 93.2%、86.0%、80.6%和 71.9%（均 P<0.001）。与 TNM 分期系统（第八版）相比，RPA 风险分组在训练[曲线下面积（AUC）=0.661 与 0.631，P<0.001]和验证[AUC=0.687 与 0.654，P=0.001]队列中具有更高的预后预测效能。此外，我们的模型能够很好地区分适合诱导化疗的敏感患者。
我们的新 RPA 分期模型在预后预测和临床决策方面优于现行的 TNM 分期系统。我们建议将 cfEBV DNA、LDH 和 CAR 纳入 TNM 分期系统。
