Radiomics as an emerging tool in the management of brain metastases

Abstract

Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than one year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.

Adverse radiation effect versus tumor progression following stereotactic radiosurgery for brain metastases: Implications of radiologic uncertainty

“…Notably, only 11.7% of lesions in our study were assessed with nuclear imaging, highlighting its limited use even within a large, academic institution. Most recently, machine learning-based analyses of radiomics signatures have been increasingly explored in patients with RU after intracranial radiation [ 49 ]. This approach extracts radiographic features from a variety of imaging sequences and modalities to build predictive models that can aid in diagnosis and management.…”

Exploring the Molecular Tumor Microenvironment and Translational Biomarkers in Brain Metastases of Non-Small-Cell Lung Cancer

“…The emerging evidence indicates that NSCLC with different features exhibits variability in imaging characteristics of the primary tumor and metastatic locations. The pro-curement of high-quality medical imaging through computed tomography and magnetic resonance imaging (MRI) is essential in the diagnostic process and treatment stratification for brain metastases [130]. Radiomics, a cutting-edge approach that utilizes advanced computational techniques to extract high-throughput quantitative features from medical images, has garnered considerable attention for its utility in characterizing various types of brain metastases, especially those originating from NSCLC [131].…”

Pretreatment Spatially Aware Magnetic Resonance Imaging Radiomics Can Predict Distant Brain Metastases (DBMs) After Stereotactic Radiosurgery/Radiation Therapy (SRS/SRT)

“…Indeed, the role of MRI-based radiomic models in prediction of response to SRS/SRT has been reported. 13 , 14 , 15 , 16 , 17 However, these previous works have largely studied local control as measured by changes in tumor volume after SRS/SRT and primarily investigated methods for binary classification of patient response to therapy.…”