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。我们利用接收者操作特征曲线的曲线下面积 (AUC) 评估了该模型的性能。
结果。在本研究中，有 64.2% (300/467) 的患者表现出EGFR突变。外显子 21 的 L858R 突变是最常见的突变类型 (185/301)。我们确定了基于CT的放射组学特征，该特征可成功区分训练组 (AUC = 0.831) 和验证组 (AUC = 0.789) 中的EGFR阳性与EGFR阴性。结合临床因素模型的放射组学特征并不优于两组中的简单放射组学特征 (p > 0.05)。
结论。作为一种非侵入性方法，基于CT的放射组学特征可用于预测表现为亚实性结节的LADC的EGFR突变状态。
实践意义: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。但是，一些患有不能手术的亚实性LADC的患者无法通过活组织检查进行组织取样，用于在临床实践中进行分子分析。可将基于计算机断层扫描的放射组学特征作为非侵入性生物标志物，用于在无法或不可能进行突变分析时预测亚实性LADC的EGFR突变状态。
Keywords: Computed tomography; Epidermal growth factor receptor; Lung adenocarcinoma; Radiomics; Subsolid.
© AlphaMed Press 2019.