Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases

Abstract

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.

Introduction

The prognosis of lung cancer patients who develop brain metastasis is poor. Lung cancer is the most common form of cancer to metastasize to the brain [1] with about 7–10% of non-small cell lung cancer (NSCLC) patients presenting with brain metastases upon diagnosis, and 20–40% developing brain metastases later on [2]. Options for the treatment of patients with NSCLC have greatly expanded in the past decade with the advent of targeted therapy. Treatment options are determined according to mutation status. Whereas alterations in epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), ROS proto-oncogene 1 (ROS-1), v-Raf murine sarcoma viral oncogene homolog B (BRAF), and neurotrophic tropomyosin receptor kinase (NTRK) genes can be targeted with FDA-approved drugs, alterations in the Kirsten rat sarcoma virus (KRAS) gene—which are estimated to comprise around 27% of lung cancer cases—are not targetable [[3], [4], [5]]. Nevertheless, patients with no actionable alterations may be treated with other therapies such as immunotherapy [6]. The treatment strategy for brain metastases arising from primary lung cancer should similarly be determined according to the genetic mutation status. However, brain metastases are usually small and can be scattered all over the brain, so it is not practical nor always feasible to invasively biopsy or surgically resect the metastases for molecular testing. As a result, most metastatic brain lesions are identified on magnetic resonance (MR) imaging without pathological tissue confirmation. Therefore, it is prudent to develop non-invasive imaging-based methods to evaluate the mutation status of brain metastases in lung cancer patients [7].

Radiomic analysis involves the computerized extraction of data from clinically obtained medical images and it can be used to scrutinize spatially and temporally heterogeneous tumors comprehensively over multiple time points to monitor disease status and treatment-related changes [8]. Conventional imaging evaluation of brain metastatic lesions typically includes only lesion size and location, enhancing characteristics, and the extent of peritumoral edema. By contrast, radiomic analysis of clinically acquired scans can extract highly detailed characteristics regarding tumor texture, shape, and image intensity, which are not discernable to the human eye [9,10]. Therefore, radiomic analysis is a potentially useful tool that could be used not only to evaluate brain metastases in lung cancer patients but also to identify genetic mutations to guide personalized treatment regimens.

Prior studies have demonstrated the ability of radiomic features to predict the mutation status of driver oncogenes [9,11,12]. Most studies in lung cancer were performed in primary lung cancer using computed tomography (CT) images. For example, Aerts and colleagues used features extracted from CT images to identify a prognostic radiomic signature associated with the gene expression profiles of patients with lung cancer and head-and-neck cancer [9]. Gevaert and colleagues developed a signature based on the CT images of primary lung cancer that could predict EGFR but not KRAS mutation status [11]. Liu and colleagues identified a set of five CT-based features that could be used to predict EGFR mutation status [12]. However, some patients may present with neurological symptoms such as headache leading to brain MRI scans and subsequent imaging diagnosis of brain metastases prior to confirmation of their primary lung cancer. In addition, a complimentary non-invasive imaging-focused method to predict mutation status from brain metastases is desirable than relying solely on the primary lung cancer. Although lung cancer frequently metastasizes to the brain leading to poor prognosis [1], prior radiomic studies have mostly focused on the primary lung cancer rather than brain metastases to predict mutation status, leaving a gap in knowledge and a need for further study.

There is limited information on the radiomic features of brain metastases. Existing radiomic studies of brain metastases have focused on developing models to distinguish between different primary cancers, such as lung cancer versus breast cancer or melanoma or between various lung cancer subtypes such as small cell lung carcinoma and NSCLC [[13], [14], [15], [16]]. However, to the best of our knowledge, no studies have been published using radiomic approaches and predictive modeling to classify the mutation status of brain metastases in lung cancer patients. To address this need, we collected brain magnetic resonance (MR) scans obtained for clinical care of lung cancer patients with brain metastatic lesions, segmented the tumors, extracted radiomic features, and used machine learning algorithms to classify the mutation status from the brain metastases. We hypothesized that radiomic features extracted from MR images of brain metastases could be used to classify EGFR, ALK, and KRAS mutation status in patients with primary lung cancer.