Nowakowski Alexander, Lahijanian Zubin, Panet-Raymond Valerie, Siegel Peter M, Petrecca Kevin, Maleki Farhad, Dankner Matthew
Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada.
McGill University Health Centre, Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada.
Neurooncol Adv. 2022 Sep 6;4(1):vdac141. doi: 10.1093/noajnl/vdac141. eCollection 2022 Jan-Dec.
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 1 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.
脑转移瘤（BM）与晚期癌症患者的高发病率和死亡率相关。尽管近年来手术、放疗和全身治疗取得了显著进展，但BM患者的中位总生存期仍不足1年。获取医学影像，如计算机断层扫描（CT）和磁共振成像（MRI），对于BM患者的诊断和分层以选择合适的治疗方法至关重要。放射组学分析有潜力通过将人工智能（AI）应用于已获取的医学影像来预测临床结果并指导BM患者的个性化护理，从而提高BM患者的治疗水平。在此，我们概述了将放射组学应用于BM临床管理的现有文献。这包括预测患者对放疗的反应并识别放射性坏死、进行虚拟活检以预测肿瘤突变状态，以及确定通过影像学识别的脑肿瘤的原发癌。随着进一步发展，放射组学有潜力帮助进行BM患者分层，同时避免进行侵入性组织采样的需求，特别是对于不符合手术切除条件的患者。
