Computed Tomography‐Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule

Background Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR‐targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)‐based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule. Materials and Methods A total of 467 eligible patients were divided into training and validation cohorts (n = 306 and 161, respectively). Radiomics features were extracted from unenhanced CT images by using Pyradiomics. A CT‐based radiomics signature for distinguishing EGFR mutation status was constructed using the random forest (RF) method in the training cohort and then tested in the validation cohort. A combination of the radiomics signature with a clinical factors model was also constructed using the RF method. The performance of the model was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve. Results In this study, 64.2% (300/467) of the patients showed EGFR mutations. L858R mutation of exon 21 was the most common mutation type (185/301). We identified a CT‐based radiomics signature that successfully discriminated between EGFR positive and EGFR negative in the training cohort (AUC = 0.831) and the validation cohort (AUC = 0.789). The radiomics signature combined with the clinical factors model was not superior to the simple radiomics signature in the two cohorts (p > .05). Conclusion As a noninvasive method, the CT‐based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule. Implications for Practice Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR‐targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography‐based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutation status of subsolid LADCs when mutational profiling is not available or possible. 摘要 背景。伴随表皮生长因子受体 (EGFR) 突变的肺腺癌 (LADC) 被认为是对EGFR靶向酪氨酸激酶抑制剂敏感的肺癌亚组。我们的目的在于开发并验证基于计算机断层扫描 (CT) 的放射组学特征，用于预测表现为亚实性结节的LADC的EGFR突变状态。 材料和方法。我们将共 467 名符合条件的患者分为训练组和验证组(n 分别 = 306 和 161)。用 Pyradiomics 从未增强的CT图像中提取出放射组学特征。采用随机森林 (RF) 法在训练组中构建了用于区分EGFR突变状态的基于CT的放射组学特征，然后在验证组中进行测试。另外，我们还采用RF法构建了放射组学特征与临床因素模型的组合。我