El Ayachy Radouane, Giraud Nicolas, Giraud Paul, Durdux Catherine, Giraud Philippe, Burgun Anita, Bibault Jean Emmanuel
Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.
Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.
Front Oncol. 2021 May 5;11:603595. doi: 10.3389/fonc.2021.603595. eCollection 2021.
Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients' care.
A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020.
We identified several studies at each point of patient's care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications.
Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.
肺癌是全球癌症相关死亡的首要原因。在过去十年中，放射组学研究迅速兴起。本综述的目的是找出近期的重要出版物，以便综合回顾放射组学在肺癌患者治疗各阶段的现状。
利用PubMed/Medline进行文献综述，以检索2012年1月至2020年6月期间相关的同行评审出版物。
我们在患者治疗的每个阶段都确定了几项研究：肺结节的检测和分类（n = 16）、组织学和基因组学的确定（n = 10），以及最终的治疗结果预测（n = 23）。我们报告了这些研究的方法及其结果，并讨论了临床常规应用的局限性和有待取得的进展。
机器学习应用和基于放射组学的模型在肺癌研究中展现出了广阔前景，但要将其应用于日常护理还需要更多数据。应进一步考虑多中心合作以及对放射组学研究质量和可重复性的关注。
