Pfaehler Elisabeth, Zhovannik Ivan, Wei Lise, Boellaard Ronald, Dekker Andre, Monshouwer René, El Naqa Issam, Bussink Jan, Gillies Robert, Wee Leonard, Traverso Alberto
Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Phys Imaging Radiat Oncol. 2021 Nov 9;20:69-75. doi: 10.1016/j.phro.2021.10.007. eCollection 2021 Oct.
Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years.
Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ranging from 2017/05/01 to 2020/12/01. Each abstract including the keywords was independently screened by four observers. Forty-two full-text articles were selected for further analysis. Patient population data, radiomic feature classes, feature extraction software, image preprocessing, and reproducibility results were extracted from each article. To support the community with a standardized reporting strategy, we propose a specific reporting checklist to evaluate the feasibility to reproduce each study.
Many studies continue to under-report essential reproducibility information: all but one clinical and all but two phantom studies missed to report at least one important item reporting image acquisition. The studies included in this review indicate that all radiomic features are sensitive to image acquisition, reconstruction, tumor segmentation, and interpolation. However, the amount of sensitivity is feature dependent, for instance, textural features were, in general, less robust than statistical features.
Radiomics repeatability, reproducibility, and reporting quality can substantially be improved regarding feature extraction software and settings, image preprocessing and acquisition, cutoff values for stable feature selection. Our proposed radiomics reporting checklist can serve to simplify and improve the reporting and, eventually, guarantee the possibility to fully replicate and validate radiomic studies.
尽管定量图像生物标志物（放射组学）在癌症诊断、预后和治疗评估方面显示出有前景的价值，但这些生物标志物仍缺乏可重复性。在本系统评价中，我们旨在评估近年来放射组学可重复性和重复性方面的进展。
根据最初的PubMed搜索模式检索了451篇摘要，发表日期为2017年5月1日至2020年12月1日。包括关键词的每篇摘要由四名观察者独立筛选。选择了42篇全文文章进行进一步分析。从每篇文章中提取患者群体数据、放射组学特征类别、特征提取软件、图像预处理和可重复性结果。为了用标准化报告策略支持该领域，我们提出了一个特定的报告清单来评估重现每项研究的可行性。
许多研究继续未充分报告基本的可重复性信息：除一项临床研究外，所有临床研究以及除两项体模研究外，所有体模研究都未报告至少一项关于图像采集的重要项目。本评价纳入的研究表明，所有放射组学特征对图像采集、重建、肿瘤分割和插值都很敏感。然而，敏感程度因特征而异，例如，纹理特征一般不如统计特征稳健。
在特征提取软件和设置、图像预处理和采集、稳定特征选择的临界值方面，放射组学的重复性、可重复性和报告质量可得到显著提高。我们提出的放射组学报告清单可用于简化和改进报告，并最终保证完全复制和验证放射组学研究的可能性。
