Goodkin Olivia, Pemberton Hugh, Vos Sjoerd B, Prados Ferran, Sudre Carole H, Moggridge James, Cardoso M Jorge, Ourselin Sebastien, Bisdas Sotirios, White Mark, Yousry Tarek, Thornton John, Barkhof Frederik
1Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, United Kingdom.
2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Br J Radiol. 2019 Sep;92(1101):20190365. doi: 10.1259/bjr.20190365. Epub 2019 Aug 1.
There are numerous challenges to identifying, developing and implementing quantitative techniques for use in clinical radiology, suggesting the need for a common translational pathway. We developed the quantitative neuroradiology initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We hypothesize that quantification will support reporters with clinically relevant measures contextualized with normative data, increase the precision of longitudinal comparisons, and generate more consistent reporting across levels of radiologists' experience. The QNI framework comprises the following steps: (1) establishing an area of clinical need and identifying the appropriate proven imaging biomarker(s) for the disease in question; (2) developing a method for automated analysis of these biomarkers, by designing an algorithm and compiling reference data; (3) communicating the results via an intuitive and accessible quantitative report; (4) technically and clinically validating the proposed tool pre-use; (5) integrating the developed analysis pipeline into the clinical reporting workflow; and (6) performing in-use evaluation. We will use current radiology practice in dementia as an example, where radiologists have established visual rating scales to describe the degree and pattern of atrophy they detect. These can be helpful, but are somewhat subjective and coarse classifiers, suffering from floor and ceiling limitations. Meanwhile, several imaging biomarkers relevant to dementia diagnosis and management have been proposed in the literature; some clinically approved radiology software tools exist but in general, these have not undergone rigorous clinical validation in high volume or in tertiary dementia centres. The QNI framework aims to address this need. Quantitative image analysis is developing apace within the research domain. Translating quantitative techniques into the clinical setting presents significant challenges, which must be addressed to meet the increasing demand for accurate, timely and impactful clinical imaging services.
在临床放射学中识别、开发和应用定量技术面临诸多挑战，这表明需要一条通用的转化途径。我们开展了定量神经放射学倡议（QNI），作为将自动分割和其他图像定量软件嵌入临床神经放射学工作流程所需的技术和临床验证的模型框架。我们假设，定量分析将通过规范数据为报告者提供临床相关测量结果，提高纵向比较的精度，并在不同经验水平的放射科医生中产生更一致的报告。QNI框架包括以下步骤：（1）确定临床需求领域，并为相关疾病识别合适的已证实的成像生物标志物；（2）通过设计算法和汇编参考数据，开发对这些生物标志物进行自动分析的方法；（3）通过直观且易于理解的定量报告传达结果；（4）在使用前对所提议的工具进行技术和临床验证；（5）将开发的分析流程整合到临床报告工作流程中；（6）进行使用后评估。我们将以当前痴呆症的放射学实践为例，在该领域放射科医生已建立视觉评分量表来描述他们检测到的萎缩程度和模式。这些量表可能会有所帮助，但在某种程度上是主观且粗略的分类方法，存在下限和上限的局限性。同时，文献中已提出了几种与痴呆症诊断和管理相关的成像生物标志物；虽然存在一些临床批准的放射学软件工具，但总体而言，这些工具尚未在大量病例或三级痴呆症中心进行严格的临床验证。QNI框架旨在满足这一需求。定量图像分析在研究领域正迅速发展。将定量技术转化到临床环境中面临重大挑战，必须解决这些挑战以满足对准确、及时且有影响力的临床影像服务日益增长的需求。
