| 赵敏,刘凯,王若峥.新疆地区局部晚期鼻咽癌Nomogram预后模型研究[J].中华放射医学与防护杂志,2021,41(4):259-264 |
| 新疆地区局部晚期鼻咽癌Nomogram预后模型研究 |
| A prognostic nomogram model for locally advanced nasopharyngeal carcinoma in Xinjiang region |
| 投稿时间：2020-08-08 |
| DOI：10.3760/cma.j.issn.0254-5098.2021.04.004 |
| 中文关键词: 鼻咽癌 列线图 EB病毒 预后 |
| 英文关键词:Nasopharyngeal carcinoma Nomogram Epstein-Barr virus Prognosis |
| 基金项目:新疆维吾尔自治区科技支疆项目（2020E0265） |
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| 摘要点击次数: 2090 |
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| 中文摘要: |
| 目的 分析新疆地区局部晚期鼻咽癌患者治疗后的预后相关因素，构建列线图（Nomogram）预后模型，并对此模型进行验证。方法 选择2010年7月至2017年6月新疆医科大学附属肿瘤医院收治并行根治性调强放射治疗的鼻咽癌患者317例，使用最小绝对收缩和选择算子（LASSO）回归法进行单因素筛选后行Cox多因素回归分析，并构建Nomogram预后模型对局部晚期鼻咽癌患者预后进行评估。采用一致性指数（C-index）、校准曲线、净重分类改善指数（NRI）、综合判别改善指数（IDI）进行Nomogram与TNM分期系统之间模型的验证与评估。使用决策树算法对患者列线图风险进行分层，生存率采用Kaplan-Meier法计算，并采用Log-rank法检验。结果 T分期、N分期、乳酸脱氢酶（LDH）、转移性淋巴结体积（GTVnd）及初治血浆EBV-DNA拷贝量（EBV-DNA）与总生存（OS）相关，将各因素纳入Nomogram预后模型，C-index为0.784（95%CI：0.736~0.831，P<0.01）。校准曲线显示，由Nomogram模型预测的OS概率与实际观察到的OS有较好的一致性，结果在验证队列中获得了验证；且在使用净重分类改善指数及综合判别改善指数对OS的准确性进行评估时Nomogram模型结果均优于美国癌症联合委员会（AJCC）第8版分期系统所建模型。使用决策树算法根据Nomogram得分可将患者分为4个不同危险程度的亚组，组间生存率差异有统计学意义（χ2=113.21，P<0.01），高风险队列内的患者能从诱导化疗联合同步放化疗中获得总生存获益。结论 本课题组建立的Nomogram模型可为本地区局部晚期鼻咽癌患者临床诊疗及预后评估提供参考意见。 |
| 英文摘要: |
| Objective To analyze the prognostic factors of patients with locally advanced nasopharyngeal carcinoma after treatment, to develop and validate the prognostic Nomogram model. Methods From July 2010 to June 2017, 317 patients with nasopharyngeal carcinoma who were treated with definitive intensity modulated radiation therapy were selected. The regression method of least absolute shrinkage and selection operator (LASSO) was used for univariate screening, and Cox multivariate regression analysis was performed. The prognostic Nomogram model was constructed for locally advanced nasopharyngeal carcinoma patients. C-index, calibration curve, Net Reclassification Index (NRI), integrated discrimination improvement (IDI) were used to validate and evaluate the model between Nomogram and TNM staging system. The risk evaluated through nomogram was stratified by decision tree algorithm, and the survival rate was calculated by Kaplan-Meier method and compared by Log-rank test. Results T stage, N stage, LDH, GTVnd and pre-treated plasma EBV-DNA copy (EBV-DNA) were correlated with total survival (OS). All the above factors were included in prognostic Nomogram model, and C-index was 0.784 (95%CI:0.736-0.831, P<0.01). The calibration curve showed that the OS probability predicted by Nomogram model was in good agreement with the actual OS, and the result were verified in the validation cohort. Furthermore, the accuracy of the Nomogram model for OS predicting was superior to AJCC 8th version staging system judged by NRI and IDI. According to the Nomogram score, patients can be divided into four subgroups with different risk by decision tree algorithm. K-M survival curve showed that the difference of OS between different groups was statistically significant (χ2=113.21, P<0.01), and patients in high-risk group can benefit from induction chemotherapy combined with concurrent chemoradiotherapy in survival. Conclusions The Nomogram model established by our research group can provide information on diagnosis, treatment and prognosis evaluation for locally advanced nasopharyngeal carcinoma patients in this area. |
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