School of Medical Informatics, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning Province, PR China; School of Medicine, Department of Radiology, Stanford University, 1201 Welch Rd Lucas Center PS055, Palo Alto, CA 94306, United States.
School of Medical Informatics, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning Province, PR China.
Eur J Radiol. 2020 Jun;127:108991. doi: 10.1016/j.ejrad.2020.108991. Epub 2020 Apr 12.
To determine the characteristics of and trends in research in the emerging field of radiomics through bibliometric and hotspot analyses of relevant original articles published between 2013 and 2018.
We evaluated 553 original articles concerning radiomics, published in a total of 61 peer-reviewed journals between 2013 and 2018. The following information was retrieved for each article: radiological subspecialty, imaging technique(s), machine learning technique(s), sample size, study setting and design, statistical result(s), study purpose, software used for feature calculation, funding declarations, author number, first author's affiliation, study origin, and journal name. Qualitative and quantitative analyses were performed for the manually extracted data for identification and visualization of the trends in radiomics research.
The annual growth rate in the number of published papers was 177.82% (p < 0.001). The characteristics and trends of research hotspots in the field of radiomics were clarified and visualized in this study. It was found that the field of radiomics is at a more mature stage for lung, breast, and prostate cancers than for other sites. Radiomics studies primarily focused on radiological characterization (215) and monitoring (182). Logistic regression and LASSO were the two most commonly used techniques for feature selection. Non-clinical researchers without a medical background dominated radiomics studies (70.52%), the vast majority of which only highlighted positive results (97.80%) while downplaying negative findings.
The reporting of quantifiable knowledge about the characteristics and trajectories of radiomics can inform researchers about the gaps in the field of radiomics and guide its future direction.
通过对 2013 年至 2018 年发表的有关放射组学的原始研究进行文献计量和热点分析,确定这一新兴领域的研究特点和趋势。
我们评估了 2013 年至 2018 年期间发表在 61 种同行评审期刊上的 553 篇有关放射组学的原始文章。为每篇文章检索了以下信息:放射学亚专业、成像技术、机器学习技术、样本量、研究设置和设计、统计结果、研究目的、用于特征计算的软件、资助声明、作者数量、第一作者的隶属关系、研究起源和期刊名称。对手动提取的数据进行了定性和定量分析,以识别和可视化放射组学研究趋势。
发表论文的年增长率为 177.82%(p < 0.001)。本研究阐明和可视化了放射组学领域研究热点的特点和趋势。结果表明,放射组学领域在肺癌、乳腺癌和前列腺癌方面比其他部位更为成熟。放射组学研究主要集中在放射学特征描述(215 篇)和监测(182 篇)。逻辑回归和 LASSO 是特征选择中最常用的两种技术。非临床研究人员没有医学背景,他们主导了放射组学研究(70.52%),其中绝大多数只强调了阳性结果(97.80%),而淡化了负面发现。
对放射组学特征和轨迹的可量化知识的报告可以为研究人员提供放射组学领域的差距信息,并指导其未来方向。