Search

Purpose: MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation. Methods: We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment. Results: We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation. Conclusion: We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS. [ABSTRACT FROM AUTHOR]

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis. [ABSTRACT FROM AUTHOR]

Objectives: We examined whether providing a quantitative report (QReport) of regional brain volumes improves radiologists' accuracy and confidence in detecting volume loss, and in differentiating Alzheimer's disease (AD) and frontotemporal dementia (FTD), compared with visual assessment alone. Methods: Our forced-choice multi-rater clinical accuracy study used MRI from 16 AD patients, 14 FTD patients, and 15 healthy controls; age range 52–81. Our QReport was presented to raters with regional grey matter volumes plotted as percentiles against data from a normative population (n = 461). Nine raters with varying radiological experience (3 each: consultants, registrars, 'non-clinical image analysts') assessed each case twice (with and without the QReport). Raters were blinded to clinical and demographic information; they classified scans as 'normal' or 'abnormal' and if 'abnormal' as 'AD' or 'FTD'. Results: The QReport improved sensitivity for detecting volume loss and AD across all raters combined (p = 0.015* and p = 0.002*, respectively). Only the consultant group's accuracy increased significantly when using the QReport (p = 0.02*). Overall, raters' agreement (Cohen's κ) with the 'gold standard' was not significantly affected by the QReport; only the consultant group improved significantly (κs 0.41➔0.55, p = 0.04*). Cronbach's alpha for interrater agreement improved from 0.886 to 0.925, corresponding to an improvement from 'good' to 'excellent'. Conclusion: Our QReport referencing single-subject results to normative data alongside visual assessment improved sensitivity, accuracy, and interrater agreement for detecting volume loss. The QReport was most effective in the consultants, suggesting that experience is needed to fully benefit from the additional information provided by quantitative analyses. Key Points: • The use of quantitative report alongside routine visual MRI assessment improves sensitivity and accuracy for detecting volume loss and AD vs visual assessment alone. • Consultant neuroradiologists' assessment accuracy and agreement (kappa scores) significantly improved with the use of quantitative atrophy reports. • First multi-rater radiological clinical evaluation of visual quantitative MRI atrophy report for use as a diagnostic aid in dementia. [ABSTRACT FROM AUTHOR]

Many studies report an overlap of MRI and clinical findings between patients with relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS), which in part is reflective of inclusion of subjects with variable disease duration and short periods of follow-up. To overcome these limitations, we examined the differences between RRMS and SPMS and the relationship between MRI measures and clinical outcomes 30 years after first presentation with clinically isolated syndrome suggestive of multiple sclerosis. Sixty-three patients were studied 30 years after their initial presentation with a clinically isolated syndrome; only 14% received a disease modifying treatment at any time point. Twenty-seven patients developed RRMS, 15 SPMS and 21 experienced no further neurological events; these groups were comparable in terms of age and disease duration. Clinical assessment included the Expanded Disability Status Scale, 9-Hole Peg Test and Timed 25-Foot Walk and the Brief International Cognitive Assessment For Multiple Sclerosis. All subjects underwent a comprehensive MRI protocol at 3 T measuring brain white and grey matter (lesions, volumes and magnetization transfer ratio) and cervical cord involvement. Linear regression models were used to estimate age- and gender-adjusted group differences between clinical phenotypes after 30 years, and stepwise selection to determine associations between a large sets of MRI predictor variables and physical and cognitive outcome measures. At the 30-year follow-up, the greatest differences in MRI measures between SPMS and RRMS were the number of cortical lesions, which were higher in SPMS (the presence of cortical lesions had 100% sensitivity and 88% specificity), and grey matter volume, which was lower in SPMS. Across all subjects, cortical lesions, grey matter volume and cervical cord volume explained 60% of the variance of the Expanded Disability Status Scale; cortical lesions alone explained 43%. Grey matter volume, cortical lesions and gender explained 43% of the variance of Timed 25-Foot Walk. Reduced cortical magnetization transfer ratios emerged as the only significant explanatory variable for the symbol digit modality test and explained 52% of its variance. Cortical involvement, both in terms of lesions and atrophy, appears to be the main correlate of progressive disease and disability in a cohort of individuals with very long follow-up and homogeneous disease duration, indicating that this should be the target of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Objective: Hippocampal sclerosis (HS) is the most common cause of drug‐resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross‐sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities. Methods: T1‐weighted and T2 relaxometry data from 69 HS patients (32 left, 32 right, 5 bilateral) and 111 healthy controls were acquired on a 3‐T magnetic resonance imaging (MRI) scanner. Automated hippocampal segmentation and T2 relaxometry were performed and used to calculate whole‐hippocampal volumes and to estimate quantitative T2 (qT2) values. By generating a group template from the controls, and aligning this so that the hippocampal long axes were along the anterior‐posterior axis, we were able to calculate hippocampal cross‐sectional area and qT2 by a slicewise method to localize any volume loss or T2 hyperintensity. Individual patient profiles were compared with normative data generated from the healthy controls. Results: Profiling of hippocampal volumetric and qT2 data could be performed automatically and reproducibly. HS patients commonly showed widespread decreases in volume and increases in T2 along the length of the affected hippocampus, and focal changes may also be identified. Patterns of atrophy and T2 increase in the left hippocampus were similar between left, right, and bilateral HS. These profiles have potential to distinguish between sclerosis affecting volume and qT2 in the whole or parts of the hippocampus, and may aid the radiological diagnosis in uncertain cases or cases with subtle or focal abnormalities where standard whole‐hippocampal measurements yield normal values. Significance: Hippocampal profiling of volumetry and qT2 values can help spatially localize hippocampal MRI abnormalities and work toward improved sensitivity of subtle focal lesions. [ABSTRACT FROM AUTHOR]

In multiple sclerosis, microstructural damage of normal-appearing brain tissue is an important feature of its pathology. Understanding these mechanisms is vital to help develop neuroprotective strategies. The visual pathway is a key model to study mechanisms of damage and recovery in demyelination. Anterograde trans-synaptic degeneration across the lateral geniculate nuclei has been suggested as a mechanism of tissue damage to explain optic radiation abnormalities seen in association with demyelinating disease and optic neuritis, although evidence for this has relied solely on cross-sectional studies. We therefore aimed to assess: (i) longitudinal changes in the diffusion properties of optic radiations after optic neuritis suggesting trans-synaptic degeneration; (ii) the predictive value of early optic nerve magnetic resonance imaging measures for late optic radiations changes; and (iii) the impact on visual outcome of both optic nerve and brain post-optic neuritis changes. Twenty-eight consecutive patients with acute optic neuritis and eight healthy controls were assessed visually (logMAR, colour vision, and Sloan 1.25%, 5%, 25%) and by magnetic resonance imaging, at baseline, 3, 6, and 12 months. Magnetic resonance imaging sequences performed (and metrics obtained) were: (i) optic nerve fluid-attenuated inversion-recovery (optic nerve cross-sectional area); (ii) optic nerve proton density fast spin-echo (optic nerve proton density-lesion length); (iii) optic nerve post-gadolinium T1-weighted (Gd-enhanced lesion length); and (iv) brain diffusion-weighted imaging (to derive optic radiation fractional anisotropy, radial diffusivity, and axial diffusivity). Mixed-effects and multivariate regression models were performed, adjusting for age, gender, and optic radiation lesion load. These identified changes over time and associations between early optic nerve measures and 1-year global optic radiation/clinical measures. The fractional anisotropy in patients' optic radiations decreased (P = 0.018) and radial diffusivity increased (P = 0.002) over 1 year following optic neuritis, whereas optic radiation measures were unchanged in controls. Also, smaller cross-sectional areas of affected optic nerves at 3 months post-optic neuritis predicted lower fractional anisotropy and higher radial diffusivity at 1 year (P = 0.007) in the optic radiations, whereas none of the inflammatory measures of the optic nerve predicted changes in optic radiations. Finally, greater Gd-enhanced lesion length at baseline and greater optic nerve proton density-lesion length at 1 year were associated with worse visual function at 1 year (P = 0.034 for both). Neither the cross-sectional area of the affected optic nerve after optic neuritis nor the damage in optic radiations was associated with 1-year visual outcome. Our longitudinal study shows that, after optic neuritis, there is progressive damage to the optic radiations, greater in patients with early residual optic nerve atrophy, even after adjusting for optic radiation lesions. These findings provide evidence for trans-synaptic degeneration. [ABSTRACT FROM AUTHOR]

Background: Alzheimer’s disease is currently one of our greatest socioeconomic challenges. Understanding the ability of biomarkers to predict clinical progression, prior to significant cognitive decline, is critical when considering secondary prevention strategies. Here we consider the association between a range of single time‐point baseline biomarkers in a cognitively‐normal group and the subsequent risk of developing Mild Cognitive Impairment (MCI). Method: Data from Alzheimer’s Disease Neuroimaging Initiative (ADNIGO/2/3) were used. Baseline markers (CSF amyloid 1‐42, CSF ptau, white matter hyperintensities [WMH], microbleeds, whole‐brain volume, and hippocampal volume) were obtained for 192 cognitively‐normal (CN) individuals (controls and with significant memory concern; see table). Longitudinal diagnostic data to calculate conversion time was also collected. Cox regression models were fitted for time to diagnosis of MCI. Models were fitted for each marker separately, and in a fully‐adjusted model (containing all markers); adjustments for total intracranial volume (TIV) were performed when needed. Models were refitted adjusting for baseline age. Result: There was strong evidence that increased WMH volume and lower hippocampal volume were associated with conversion to MCI. This was found in both individual models, and in the fully‐adjusted model (see figure). Decreased whole brain volume, CSF amyloid, and increased microbleeds were found to be associated with conversion to MCI when considered individually but not in a fully‐adjusted model. CSF ptau was not associated with conversion, either individually or in the fully‐adjusted model. Adjustments for baseline age did not materially change results. Conclusion: Single time‐point baseline measurements of increased WMH and decreased hippocampal volume were independently predictive of subsequent progression to MCI. Hippocampal volume and WMH may represent different processes; lower hippocampal volumes representing greater neurodegeneration and WMH as a surrogate marker of numerous pathologies, including cerebrovascular disease. While decreased whole brain volume, decreased CSF amyloid, and increased microbleeds were predictive of conversion to MCI when assessed separately, their non‐significant association in the fully‐adjusted model suggests that these are not as strongly predictive of conversion. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]