Departments of Radiology and Center for Imaging Science.
Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan.
J Thorac Imaging. 2019 Mar;34(2):103-115. doi: 10.1097/RTI.0000000000000390.
Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.
多模态影像学测量治疗反应对于临床实践、肿瘤学试验和新治疗方式的评估至关重要。目前,非小细胞肺癌(NSCLC)治疗反应的标准是基于 RECIST 标准的肿瘤大小。分子靶向药物和免疫疗法通常会引起形态学变化而不缩小肿瘤大小。因此,传统方法很难评估治疗反应。放射组学是对使用机器学习和其他语义特征提取的癌症成像特征进行研究的学科。这种方法可以提供肿瘤表型的全面信息,并可用于评估免疫治疗新时代的治疗反应。评估放射组学特征纵向变化的 Delta 放射组学在评估 NSCLC 治疗反应方面显示出了潜力。众所周知,由于技术因素的差异,定量测量方法可能存在较大的可变性,需要标准化。在这篇综述中,我们描述了 NSCLC 评估中的测量变异性和放射组学的新兴作用。