The quantitative neuroradiology initiative framework: application to dementia

Abstract

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.

Box 1.

Summary of the medical device regulatory framework applicable to software medical devices in the European Union.

Figure 1.

A normative data set of BPF has been generated from 468 normal control subjects aged 30–90 years. Mean and standard deviation BPF are shown with the solid and dotted blue lines. The subject’s BPF (large red dot) is placed along the normative curve for easy comparison with normal control subjects. Examples shown are of (a) normal control; (b) a subject with FTD. BPF, brain parenchymal fraction; FTD, frontotemporal dementia.

Figure 2.

Patient-specific anatomical volumes and respective normative data can also be generated by brain region and presented as an easily interpreted and clinically useful graphic report. Examples from patients with (a) established bilateral medial temporal atrophy, (b) posterior cortical atrophy, and (c) healthy appearing brain.