Castello Angelo, Castellani Massimo, Florimonte Luigia, Urso Luca, Mansi Luigi, Lopci Egesta
Nuclear Medicine Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy.
Nuclear Medicine Unit, Oncological Medical and Specialist Department, University Hospital of Ferrara, 44121 Ferrara, Italy.
J Clin Med. 2022 Mar 21;11(6):1740. doi: 10.3390/jcm11061740.
Immune checkpoint inhibitors (ICI) have demonstrated encouraging results in terms of durable clinical benefit and survival in several malignancies. Nevertheless, the search to identify an "ideal" biomarker for predicting response to ICI is still far from over. Radiomics is a new translational field of study aiming to extract, by dedicated software, several features from a given medical image, ranging from intensity distribution and spatial heterogeneity to higher-order statistical parameters. Based on these premises, our review aims to summarize the current status of radiomics as a potential predictor of clinical response following immunotherapy treatment. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2021 were selected, comprising those that explored computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for radiomic analyses in the setting of ICI. Several studies have demonstrated the potential applicability of radiomic features in the monitoring of the therapeutic response beyond the traditional morphologic and metabolic criteria, as well as in the prediction of survival or non-invasive assessment of the tumor microenvironment. Nevertheless, important limitations emerge from our review in terms of standardization in feature selection, data sharing, and methods, as well as in external validation. Additionally, there is still need for prospective clinical trials to confirm the potential significant role of radiomics during immunotherapy.
免疫检查点抑制剂(ICI)在多种恶性肿瘤的持久临床获益和生存方面已显示出令人鼓舞的结果。然而,寻找一种用于预测对ICI反应的“理想”生物标志物的工作仍远未结束。放射组学是一个新的转化研究领域,旨在通过专用软件从给定的医学图像中提取多种特征,范围从强度分布和空间异质性到高阶统计参数。基于这些前提,我们的综述旨在总结放射组学作为免疫治疗后临床反应潜在预测指标的现状。我们对PubMed结果进行了全面检索。选取了截至2021年12月(含)发表的所有英文研究,包括那些在ICI背景下探索计算机断层扫描(CT)、磁共振成像(MRI)和正电子发射断层扫描(PET)用于放射组学分析的研究。多项研究已证明,放射组学特征在监测治疗反应方面具有潜在适用性,超越了传统的形态学和代谢标准,以及在预测生存或对肿瘤微环境进行非侵入性评估方面。然而,我们的综述在特征选择、数据共享和方法的标准化以及外部验证方面出现了重要局限性。此外,仍需要进行前瞻性临床试验来证实放射组学在免疫治疗期间的潜在重要作用。