背景:在相似的肿瘤淋巴结转移阶段,鼻咽癌(NPC)患者的治疗效果可能有所不同。此外,治疗结束时肿瘤消退是治疗敏感性的可靠指标.本研究旨在探讨定量双能计算机断层扫描(DECT)参数是否可以预测鼻咽癌患者对颈部淋巴结放疗的敏感性。
方法:总的来说,从98例接受预处理DECT的NPC患者中收集了388个淋巴结。将患者分为完全缓解(CR)和部分缓解(PR)组。比较各组临床特点和定量DECT参数,并使用接收器工作特性(ROC)分析确定每个参数的最佳预测能力。使用单变量和二元逻辑回归构建并验证了列线图预测模型。
结果:CR组的DECT参数高于PR组。碘浓度(IC),归一化IC,Mix-0.6,光谱Hounsfield单位曲线斜率,有效原子序数,两组之间的虚拟单能量图像存在显着差异。DECT参数的ROC曲线下面积为0.73-0.77。基于二元逻辑回归,使用10个预测因子构建柱状图,包括年龄,性别,N级,最大淋巴结直径,动脉期NIC,静脉期NIC,λHU和70keV下的光谱亨氏单位。模型的ROC曲线下面积为0.813,敏感性和特异性分别为85.6%和81.3%。分别。
结论:定量DECT参数可有效预测鼻咽癌放疗的敏感性。因此,DECT参数和NPC临床特征可以组合以构建具有高预测能力的列线图并用作临床分析工具。
BACKGROUND: Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor-node-metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck-lymph node radiotherapy in patients with NPC.
METHODS: Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression.
RESULTS: DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73-0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively.
CONCLUSIONS: Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool.