Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

Abstract

Objectives QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. Methods Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland–Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. Results The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. Conclusions QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. Key Points • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review

“… volume of lesions 1st, 2nd ( Yildirim and Dandil, 2021a ) 38 MS T2-w mask R-CNN with ResNet101 as backbone / DSC, volume overlap error, LTPR, LFPR 1st, 2nd ( Tran et al, 2022 ) 30 MS T1-w and FLAIR intensity-based b.c. WM hyperintensities volume agreement, DSC, FPR, TPR, F1 score 1st, 2nd, 3rd ( Cavedo et al, 2022 ) 130 images multi-centric, different populations FLAIR intensity-based / WM hyperintensities volume, DSC, relative VD, absolute volume error 1st, 2nd, 3rd ( Brune et al, 2020 ) 56 MS MPRAGE and FLAIR intensity-based / lesion count of tool vs neuroradiologists, single and multiple timepoints 1st, 2nd, 3rd ( Jain et al, 2017 ) 22 MS T1-w and FLAIR maximum a posteriori model on image intensities of both time points b.c., normalisation DSC, F1, LTPR, LFPR, AVD 1st, 2nd ( Van Hecke et al, 2021 ) batches of 10 and 25 MS, plus 87 subjects with CIS and MS T1-w and FLAIR U-Net with attention gate layers MNI reg., skull stripping, z-score normalisation with vs without tool performances, surveys on patient’s perspective 1st, 2nd, 3rd, 4th, 5th, 6th ( Sousa et al, 2021 ) …”

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

“…Technical: Qynapse has compared the current method for lesion segmentation to a previous method, to state-of-theart lesion segmentation methods (including LST, Lesion-TOADS, lesionBrain, BIANCA, and nicMSlesions), and to manual segmentation [25,30]. QyScore has been tested for robustness to different input data [30].…”

“…These tools aim to improve the objectivity of image interpretation by increasing the sensitivity of MRI analysis [18,19], the accuracy [20][21][22][23][24][25] and reproducibility of results [22,26], and potentially decreasing reporting time [18]. QReports can facilitate cross-sectional diagnosis [20,[26][27][28][29][30], longitudinal assessment [20,22,23,31], and therapy response monitoring [32] via user-friendly graphical displays. QReports may also offer automatic contextualization of an individual patient's volumetric results against a relevant reference population [33], which could assist clinicians in disease course prognostication and deciding on therapeutic strategies.…”

Data-driven normative values based on generative manifold learning for quantitative MRI

“…These approaches are used in a number of popular software tools, such as VolBrain 6 , Qyscore 7 , Neuroquant 8 , or IcoBrain DM 9 . One potential limitation with these approaches is the use of civil age and sex as co-variables.…”