Li Yan, Zang Jian, Liu Jingyi, Luo Shanquan, Wang Jianhua, Hou Bingxin, Zhao Lina, Shi Mei
Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Front Oncol. 2021 Sep 6;11:739103. doi: 10.3389/fonc.2021.739103. eCollection 2021.
To accurately stratify nasopharyngeal carcinoma (NPC) patients who were benefit from induction chemotherapy (IC) followed by chemoradiotherapy (CCRT), we established residual volume of lymph nodes during chemoradiotherapy based nomogram to predict survival for NPC patients.
Cox regression analysis were used to evaluate predictive effects of tumor volume parameters. Multivariate Cox regression analysis was used to identify the prognostic factors, and nomogram models were developed to predict survival of NPC patients receiving IC followed by CCRT.
Compared with other tumor volumetric parameters, midRT GTVnd was the best predictive factor for OS (HR: 1.043, 95%CI: 1.031-1.055), PFS (HR: 1.040, 95%CI: 1.030- 1.051), and DMFS (HR: 1.046, 95%CI: 1.034 - 1.059) according to the HR of Cox regression analysis. Based on multivariate analysis, three nomograms included midRT GTVnd were constructed to predict 4-year survival. The C-index of nomograms for each survival endpoints were as follow (training cohort . validation cohort): 0.746 . 0.731 for OS; 0.747 . 0.735 for PFS; 0.768 . 0.729 for DMFS, respectively. AUC showed a good discriminative ability. Calibration curves demonstrated a consistence between actual results and predictions. Decision curve analysis (DCA) showed that the nomograms had better clinical predictive effects than current TNM staging system.
We identified the best volumetric indicator associated with prognosis was the residual volume of lymph nodes at the fourth week of chemoradiotherapy for patients receiving IC followed by CCRT. We developed and validated three nomograms to predict specific probability of 4-year OS, PFS and DMFS for NPC patient receiving IC followed by CCRT.
为了准确分层那些能从诱导化疗(IC)后序贯放化疗(CCRT)中获益的鼻咽癌(NPC)患者,我们建立了基于放化疗期间淋巴结残余体积的列线图来预测NPC患者的生存情况。
采用Cox回归分析评估肿瘤体积参数的预测效果。使用多变量Cox回归分析确定预后因素,并建立列线图模型来预测接受IC后序贯CCRT的NPC患者的生存情况。
根据Cox回归分析的风险比(HR),与其他肿瘤体积参数相比,放疗中期颈淋巴结转移灶大体肿瘤体积(midRT GTVnd)是总生存期(OS,HR:1.043,95%置信区间:1.031 - 1.055)、无进展生存期(PFS,HR:1.040,95%置信区间:1.030 - 1.051)和远处转移无进展生存期(DMFS,HR:1.046,95%置信区间:1.034 - 1.059)的最佳预测因素。基于多变量分析,构建了三个包含midRT GTVnd的列线图来预测4年生存率。每个生存终点的列线图的C指数如下(训练队列. 验证队列):OS为0.746. 0.731;PFS为0.747. 0.735;DMFS为0.768. 0.729,曲线下面积(AUC)显示出良好的鉴别能力。校准曲线表明实际结果与预测结果一致。决策曲线分析(DCA)表明列线图比当前的TNM分期系统具有更好的临床预测效果。
我们确定与预后相关的最佳体积指标是接受IC后序贯CCRT的患者在放化疗第4周时的淋巴结残余体积。我们开发并验证了三个列线图,以预测接受IC后序贯CCRT的NPC患者4年OS、PFS和DMFS的具体概率。