A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma
A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma
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DOI：
10.1088/1361-6560/ab6f98
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发表时间：
2020-03-01
影响因子：
3.5
通讯作者：
Cao, Dianbo
中科院分区：
文献类型：
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作者：
Lu, Xiaoqian;Li, Mingyang;Cao, Dianbo
为利用定量放射组学生物标志物和语义特征预测肺腺癌患者的表皮生长因子受体（EGFR）突变状态，我们分析了2016 - 2018年在我中心接受手术切除和EGFR突变检测的104例肺腺癌患者的计算机断层扫描（CT）图像和病历资料。从术前非增强CT扫描中提取反映肿瘤异质性和表型的CT放射学和语义特征。最小绝对收缩和选择算子的方法被用来选择最可区分的功能。建立了3个logistic回归模型，分别结合CT语义与临床病理特征、单独使用放射组学特征以及结合放射组学与临床病理特征来预测EGFR突变状态。采用五重交叉验证法进行受试者工作特征（ROC）曲线分析，计算平均曲线下面积（AUC）值，并比较模型间的差异，以获得预测EGFR突变的最佳模型。最后，通过构建放射组列线图来验证模型的性能，共提取了1025个放射组特征，并将其缩减为13个特征，作为构建放射组特征的最重要的预测因子。基于放射组学特征、性别、吸烟、血管浸润和病理组织学类型开发了放射组学和临床病理学特征组合模型。训练的AUC为0.90 +/- 0.02，验证的AUC为0.88 +/- 0.11，测试数据集的AUC为0.894。该模型优于其他结合CT语义和临床病理特征的预测模型，具有上级优势（测试数据集的AUC：0.768）和单独的放射组学特征（测试数据集的AUC：0.837）。通过放射组学生物标志物和临床病理特征（包括放射组学标记、性别、吸烟、血管浸润和病理类型）建立的预测模型，结果表明，该模型能有效预测周围型肺腺癌患者的EGFR突变状态。该模型的放射组学列线图有望成为需要辅助靶向治疗的肺腺癌患者的有效生物标志物。
To predict the epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using quantitative radiomic biomarkers and semantic features.We analyzed the computed tomography (CT) images and medical record data of 104 patients with lung adenocarcinoma who underwent surgical excision and EGFR mutation detection from 2016 to 2018 at our center. CT radiomic and semantic features that reflect the tumors' heterogeneity and phenotype were extracted from preoperative non-enhanced CT scans. The least absolute shrinkage and selection operator method was applied to select the most distinguishable features. Three logistic regression models were built to predict the EGFR mutation status by combining the CT semantic with clinicopathological characteristics, using the radiomic features alone, and by combining the radiomic and clinicopathological features. Receiver operating characteristic (ROC) curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for predicting EGFR mutation. Furthermore, radiomic nomograms were constructed to demonstrate the performance of the model.In total, 1025 radiomic features were extracted and reduced to 13 features as the most important predictors to build the radiomic signature. The combined radiomic and clinicopathological features model was developed based on the radiomic signature, sex, smoking, vascular infiltration, and pathohistological type. The AUC was 0.90 +/- 0.02 for the training, 0.88 +/- 0.11 for the verification, and 0.894 for the test dataset. This model was superior to the other prediction models that used the combined CT semantic and clinicopathological features (AUC for the test dataset: 0.768) and radiomic features alone (AUC for the test dataset: 0.837).The prediction model built by radiomic biomarkers and clinicopathological features, including the radiomic signature, sex, smoking, vascular infiltration, and pathological type, outperformed the other two models and could effectively predict the EGFR mutation status in patients with peripheral lung adenocarcinoma. The radiomic nomogram of this model is expected to become an effective biomarker for patients with lung adenocarcinoma requiring adjuvant targeted treatment.