医学
计算机断层摄影术
肺癌
突变
预测值
价值(数学)
基因
基因突变
放射科
癌症研究
病理
内科学
遗传学
机器学习
生物
计算机科学
作者
Yingying Yu,Chao Han,Xiaojing Gan,Weili Tian,Cheng Zhou,Yong Zhou,Xiaoyan Xu,Zhi Wen,Wenya Liu
标识
DOI:10.1016/j.crad.2024.04.019
摘要
Purpose To explore the predictive value of morphological signs and quantitative parameters from spectral CT for EGFR gene mutations in intermediate and advanced non-small cell lung cancer (NSCLC). Materials and Methods This retrospective observational study included patients with intermediate or advanced NSCLC at the XXX Hospital between January 2017 and December 2019. The patients were divided into the EGFR gene mutation-positive and -negative groups. Results Seventy-nine patients aged 60.75 ± 9.66 years old were included: 32 were EGFR mutation-positive, and 47 were negative. There were significant differences in pathological stage (P<0.001), tumor diameter (P=0.019), lobulation sign, intrapulmonary metastasis, mediastinal lymph node metastasis, distant metastasis (P<0.001), bone metastasis (P<0.001), arterial phase normalized iodine concentration (NIC) (P=0.001), venous phase NIC (P=0.001), slope of the energy spectrum curve (λ) (P<0.001), and CT value at 70 keV in arterial phase (P=0.004) and venous phase (P=0.003) between the EGFR mutation-positive and -negative patients. The multivariable logistic regression analysis showed that intrapulmonary metastasis, distant metastasis, venous phase NIC, venous phase λ, and pathological stage were independent factors predicting EGFR gene mutations, with high diagnostic power (AUC = 0.975, 91.5% sensitivity, and 90.6% specificity). Conclusions The pathological stage and the spectral CT parameters of intrapulmonary metastasis, distant metastasis, venous phase NIC, and venous phase λ might pre-operatively predict EGFR gene mutations in intermediate and advanced NSCLC.
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