Jiang Mengmeng, Zhang Yiqian, Xu Junshen, Ji Min, Guo Yinglong, Guo Yixian, Xiao Jie, Yao Xiuzhong, Shi Hongcheng, Zeng Mengsu
Shanghai Institute of Medical Imaging, 180 Fenglin Road, Xuhui District, Shanghai, China.
Research Collaboration, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Road, Shanghai.
Nucl Med Commun. 2019 Aug;40(8):842-849. doi: 10.1097/MNM.0000000000001043.
The aim of this study was to investigate whether quantitative and qualitative features extracted from PET/computed tomography (CT) can be used as imaging biomarkers for evaluating epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer patients.
Eighty patients with stage II and III non-small cell lung cancer from January 2017 to December 2017 were included in this study. All patients underwent PET/CT examination before operation. Patients with 30 EGFR positive and 50 EGFR negative were confirmed by pathological verification and gene detection. Least absolute shrinkage and selection operator was used for analysis and selection of imaging features. Support vector machine was used to classify EGFR positive/negative using the selected features. Ten-fold cross validation was used to estimate the accuracy.
A total of 512 quantitative features (radiomic features) were extracted from PET/CT (256 for PET and 256 for CT), and 12 qualitative features (semantic features) were extracted from CT. A total of 35 features were finally retained after least absolute shrinkage and selection operator (31 quantitative features and 4 qualitative features). The 35 selected features were significantly associated with EGFR mutation status. A predictive model was built using PET/CT data. Its performance was revealed as 0.953 using the area under the receiver operating characteristic curve.
A predictive model using PET/CT images might be used to detect EGFR mutation status in non-small cell lung cancer patients.
本研究旨在探讨从正电子发射断层扫描/计算机断层扫描（PET/CT）中提取的定量和定性特征是否可作为成像生物标志物，用于评估非小细胞肺癌患者的表皮生长因子受体（EGFR）突变状态。
纳入2017年1月至2017年12月期间的80例II期和III期非小细胞肺癌患者。所有患者在手术前均接受PET/CT检查。通过病理验证和基因检测确认30例EGFR阳性和50例EGFR阴性患者。采用最小绝对收缩和选择算子进行成像特征的分析和选择。使用支持向量机根据所选特征对EGFR阳性/阴性进行分类。采用十折交叉验证来估计准确性。
从PET/CT中总共提取了512个定量特征（影像组学特征）（PET为256个，CT为256个），从CT中提取了12个定性特征（语义特征）。经过最小绝对收缩和选择算子后，最终保留了35个特征（31个定量特征和4个定性特征）。所选的35个特征与EGFR突变状态显著相关。使用PET/CT数据建立了预测模型。其性能通过受试者操作特征曲线下面积显示为0.953。
使用PET/CT图像的预测模型可能用于检测非小细胞肺癌患者的EGFR突变状态。
