基于光谱CT增强图像的放射组学模型预测周围型肺癌中VEGF和EGFR的表达
Prediction of VEGF and EGFR Expression in Peripheral Lung Cancer Based on the Radiomics Model of Spectral CT Enhanced Images.
作者信息
Wu Linhua, Li Jian, Ruan Xiaowei, Ren Jialiang, Ping Xuejun, Chen Bing
机构信息
Department of Radiology, General Hosipital of Ningxia Medical University, YinChuan, Ningxia Hui Autonomous Region, People's Republic of China.
Department of Radiology, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, People's Republic of China.
出版信息
Int J Gen Med. 2022 Aug 22;15:6725-6738. doi: 10.2147/IJGM.S374002. eCollection 2022.
BACKGROUND
Energy spectrum CT is an effective method to evaluate the biological behavior of lung cancer. Radiomics is a non-invasive technology to obtain histological information related to lung cancer.
PURPOSE
To investigate the value of the radiomics models on the bases of enhanced spectral CT images of peripheral lung cancer to predict the expression of the vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR).
MATERIAL AND METHODS
This study retrospectively analyzed 73 patients with peripheral lung cancer confirmed by postoperative pathology. All patients underwent dual-phase enhanced spectral CT scans before surgery. Regions of interest (ROI) were delineated in the arterial phase and venous phase. Key radiomics features were extracted and models were established to predict the expression of VEGF and EGFR, respectively. All models were established based on the expression levels of VEGF and EGFR in tissues detected by immunohistochemical staining as reference standards. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of each model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the models.
RESULTS
In predicting the expression level of VEGF, the combined (COMB) model composed of one spectral feature and two radiomics features achieved the best performance with area under ROC (AUC) 0.867 (95% CI: 0.767-0.966), accuracy of 0.812, sensitivity of 0.879, and specificity of 0.667. According to the expression level of EGFR, three importance radiomics features were retained in the arterial and venous phases to establish the multiphase phase model which has the best performance with AUC of 0.950 (95% confidence interval: 0.89-1.00), accuracy of 0.896, sensitivity of 0.868, and specificity of 1.
CONCLUSION
The radiomics model of enhanced spectral CT images of peripheral lung cancer can predict the expression of EGFR and VEGF.
背景
能谱CT是评估肺癌生物学行为的有效方法。放射组学是一种获取与肺癌相关组织学信息的非侵入性技术。
目的
探讨基于周围型肺癌增强光谱CT图像的放射组学模型预测血管内皮生长因子（VEGF）和表皮生长因子受体（EGFR）表达的价值。
材料与方法
本研究回顾性分析了73例经术后病理证实的周围型肺癌患者。所有患者在手术前行双期增强光谱CT扫描。在动脉期和静脉期勾画感兴趣区（ROI）。提取关键的放射组学特征并分别建立模型来预测VEGF和EGFR的表达。所有模型均以免疫组织化学染色检测的组织中VEGF和EGFR的表达水平作为参考标准建立。采用受试者操作特征（ROC）曲线和校准曲线评估各模型的预测性能，并采用决策曲线分析（DCA）评估模型的临床实用性。
结果
在预测VEGF表达水平时，由一个光谱特征和两个放射组学特征组成的联合（COMB）模型表现最佳，ROC曲线下面积（AUC）为0.867（95%CI：0.767 - 0.966），准确率为0.812，灵敏度为0.879，特异性为0.667。根据EGFR表达水平，在动脉期和静脉期保留三个重要的放射组学特征建立多期模型，其表现最佳，AUC为0.950（95%置信区间：0.89 - 1.00），准确率为0.896，灵敏度为0.868，特异性为1。
结论
周围型肺癌增强光谱CT图像的放射组学模型能够预测EGFR和VEGF的表达。
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