Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.

中文翻译：

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基于肺癌脑转移的MR成像的放射学预测突变状态。

肺癌转移包括成年人中所有脑转移中的大部分，并且大多数脑转移是通过磁共振（MR）扫描诊断的。这项研究的目的是对来自原发性肺癌患者的脑转移病变进行基于MR成像的放射学分析，以对转移性疾病的突变状态进行分类。我们回顾性地确定了在2009年至2017年间在我们机构接受过脑转移的肺癌患者的原发性肺癌的基因型测试。脑部MR图像用于增强肿瘤和肿瘤周围水肿的分割，以及放射特征的提取。确定最相关的放射学特征并将其与临床数据一起用于训练随机森林分类器以对突变状态进行分类。研究队列中的110名患者（平均年龄57.51±12。32岁; M：F = 37:73），75个具有EGFR突变，21个具有ALK易位，15个具有KRAS突变。1例患者同时患有ALK易位和EGFR突变。与突变分类最相关的大多数放射学特征是质地。与仅使用放射学特征和临床数据一起进行模型构建相比，可以产生更准确的分类。对于EGFR，ALK和KRAS突变状态的分类，基于放射学特征和临床数据构建的模型分别基于0.912、0.915和0.985的交叉验证得出了曲线下面积（AUC）值。我们的研究表明，基于MR影像学的原发性肺癌患者脑转移的放射学分析可用于对突变状态进行分类。这种方法对于设计治疗策略和告知预后可能是有用的。