Li Fenglan, Qi Linlin, Cheng Sainan, Liu Jianing, Chen Jiaqi, Cui Shulei, Dong Shushan, Wang Jianwei
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China.
Clinical Science, Philips Healthcare, Beijing, China.
Insights Imaging. 2024 Apr 29;15(1):109. doi: 10.1186/s13244-024-01678-9.
To determine whether quantitative parameters of detector-derived dual-layer spectral computed tomography (DLCT) can reliably identify epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC).
Patients with NSCLC who underwent arterial phase (AP) and venous phase (VP) DLCT between December 2021 and November 2022 were subdivided into the mutated and wild-type EGFR groups following EGFR mutation testing. Their baseline clinical data, conventional CT images, and spectral images were obtained. Iodine concentration (IC), iodine no water (INW), effective atomic number (Zeff), virtual monoenergetic images, the slope of the spectral attenuation curve (λ), enhancement degree (ED), arterial enhancement fraction (AEF), and normalized AEF (NAEF) were measured for each lesion.
Ninety-two patients (median age, 61 years, interquartile range [51, 67]; 33 men) were evaluated. The univariate analysis indicated that IC, normalized IC (NIC), INW and ED for the AP and VP, as well as Zeff and λ for the VP were significantly associated with EGFR mutation status (all p < 0.05). INW(VP) showed the best diagnostic performance (AUC, 0.892 [95% confidence interval {CI}: 0.823, 0.960]). However, neither AEF (p = 0.156) nor NAEF (p = 0.567) showed significant differences between the two groups. The multivariate analysis showed that INW(AP) and NIC(VP) were significant predictors of EGFR mutation status, with the latter showing better performance (p = 0.029; AUC, 0.897 [95% CI: 0.816, 0.951] vs. 0.774 [95% CI: 0.675, 0.855]).
Quantitative parameters of DLCT can help predict EGFR mutation status in patients with NSCLC.
Quantitative parameters of DLCT, especially NIC(VP), can help predict EGFR mutation status in patients with NSCLC, facilitating appropriate and individualized treatment for them.
Determining EGFR mutation status in patients with NSCLC before starting therapy is essential. Quantitative parameters of DLCT can predict EGFR mutation status in NSCLC patients. NIC in venous phase is an important parameter to guide individualized treatment selection for NSCLC patients.
确定探测器衍生的双层光谱计算机断层扫描（DLCT）的定量参数能否可靠识别非小细胞肺癌（NSCLC）患者的表皮生长因子受体（EGFR）突变状态。
对2021年12月至2022年11月期间接受动脉期（AP）和静脉期（VP）DLCT检查的NSCLC患者进行EGFR突变检测，之后将其分为EGFR突变组和野生型组。获取患者的基线临床数据、传统CT图像和光谱图像。测量每个病灶的碘浓度（IC）、无水碘（INW）、有效原子序数（Zeff）、虚拟单能量图像、光谱衰减曲线斜率（λ）、强化程度（ED）、动脉强化分数（AEF）和标准化AEF（NAEF）。
共评估了92例患者（中位年龄61岁，四分位间距[51, 67]；男性33例）。单因素分析表明，AP期和VP期的IC、标准化IC（NIC）、INW和ED，以及VP期的Zeff和λ与EGFR突变状态显著相关（均p < 0.05）。INW（VP）表现出最佳诊断性能（曲线下面积[AUC]，0.892[95%置信区间{CI}：0.823, 0.960]）。然而，两组之间AEF（p = 0.156）和NAEF（p = 0.567）均无显著差异。多因素分析表明，INW（AP）和NIC（VP）是EGFR突变状态的显著预测因子，后者表现更佳（p = 0.029；AUC，0.897[95% CI：0.816, 0.951] vs. 0.774[95% CI：0.675, 0.855]）。
DLCT的定量参数有助于预测NSCLC患者的EGFR突变状态。
DLCT的定量参数，尤其是NIC（VP），有助于预测NSCLC患者的EGFR突变状态，为其提供合适的个体化治疗。
在开始治疗前确定NSCLC患者的EGFR突变状态至关重要。DLCT的定量参数可预测NSCLC患者的EGFR突变状态。静脉期的NIC是指导NSCLC患者个体化治疗选择的重要参数。
