Institute of Neuroscience and Medicine (INM-3, INM-4), Research Center Juelich (FZJ), Juelich, Germany; Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Brain Tumour Group, European Organization for Research and Treatment of Cancer, Brussels, Belgium.
Brain Tumour Group, European Organization for Research and Treatment of Cancer, Brussels, Belgium; IRCCS Istituto Scienze Neurologiche di Bologna, Nervous System Medical Oncology Department, Bologna, Italy.
Lancet Digit Health. 2022 Nov;4(11):e841-e849. doi: 10.1016/S2589-7500(22)00144-3. Epub 2022 Sep 28.
The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the use of statistical methods in research, and the development of codes of ethics, have considerably influenced the way clinical trials are conducted today. In addition, methods from the broad field of artificial intelligence, such as radiomics, have the potential to considerably affect clinical trials and clinical practice in the future. Radiomics is a method to extract undiscovered features from routinely acquired imaging data that can neither be captured by means of human perception nor conventional image analysis. In patients with brain cancer, radiomics has shown its potential for the non-invasive identification of prognostic biomarkers, automated response assessment, and differentiation between treatment-related changes from tumour progression. Despite promising results, radiomics is not yet established in routine clinical practice nor in clinical trials. In this Viewpoint, the European Organization for Research and Treatment of Cancer Brain Tumour Group summarises the current status of radiomics, discusses its potential and limitations, envisions its future role in clinical trials in neuro-oncology, and provides guidance on how to address the challenges in radiomics.
临床试验的发展使得许多疾病(包括脑癌)的预防和治疗有了实质性的改善。医学的进步,如手术技术的改进、新药和新设备的开发、统计方法在研究中的应用以及道德规范的制定,都极大地影响了临床试验的进行方式。此外,人工智能这一广泛领域中的方法,如放射组学,未来有可能对临床试验和临床实践产生重大影响。放射组学是一种从常规获取的影像数据中提取未被发现的特征的方法,这些特征既不能通过人类感知,也不能通过常规影像分析来捕捉。在脑癌患者中,放射组学已经显示出其在非侵入性识别预后生物标志物、自动反应评估以及区分治疗相关变化与肿瘤进展方面的潜力。尽管取得了有希望的结果,但放射组学尚未在常规临床实践或临床试验中得到确立。在本观点中,欧洲癌症研究与治疗组织脑肿瘤小组总结了放射组学的现状,讨论了其潜力和局限性,设想了它在神经肿瘤学临床试验中的未来作用,并就如何应对放射组学中的挑战提供了指导。