Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States.
Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States.
Magn Reson Imaging. 2020 Jun;69:49-56. doi: 10.1016/j.mri.2020.03.002. Epub 2020 Mar 13.
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.
肺癌转移是成年人脑转移的主要类型，大多数脑转移是通过磁共振（MR）扫描诊断的。本研究旨在对原发性肺癌脑转移患者的脑转移病灶进行基于 MR 成像的放射组学分析，以对转移疾病的突变状态进行分类。我们回顾性地确定了 2009 年至 2017 年在我们机构接受治疗且对原发性肺癌进行了基因分型检测的肺癌伴脑转移患者。脑 MR 图像用于增强肿瘤和瘤周水肿的分割，以及放射组学特征提取。确定最相关的放射组学特征，并结合临床数据使用随机森林分类器对分类进行训练，以确定突变状态。在研究队列的 110 例患者中（平均年龄 57.51±12.32 岁；男：女=37：73），75 例患者存在 EGFR 突变，21 例患者存在 ALK 易位，15 例患者存在 KRAS 突变。1 例患者同时存在 ALK 易位和 EGFR 突变。对突变分类最相关的大多数放射组学特征都是纹理特征。使用放射组学特征和临床数据构建模型比单独使用任何一种方法的分类都更准确。对于 EGFR、ALK 和 KRAS 突变状态的分类，使用放射组学特征和临床数据构建的模型在交叉验证时的 AUC 值分别为 0.912、0.915 和 0.985。本研究表明，原发性肺癌脑转移患者的基于 MR 成像的放射组学分析可用于对突变状态进行分类。这种方法可能有助于制定治疗策略并提供预后信息。
