MRI diagnosis accuracy -
A study published in the New England Journal of Medicine rated the sensitivity of three methods of invasive breast cancer detection.
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Health Informatics Health Policy Health Systems and Quality Improvement Hematology Intensive Care and Critical Care Medicine Medical Education Medical Ethics Nephrology Neurology Nursing Nutrition Obstetrics and Gynecology Occupational and Environmental Health Oncology Ophthalmology Orthopedics Otolaryngology Pain Medicine Palliative Medicine Pathology Pediatrics Pharmacology and Therapeutics Primary Care Research Psychiatry and Clinical Psychology Public and Global Health Radiology and Imaging Rehabilitation Medicine and Physical Therapy Respiratory Medicine Rheumatology Sexual and Reproductive Health Sports Medicine Surgery Toxicology More importantly, we demonstrate that longitudinal change is detectable at a six-month follow-up visit.
We also report correlations between ALSFRS-R and the fiber density and cross-section metrics. Our findings suggest that multimodal MRI is useful in improving disease diagnosis, and fixel-based measures may serve as potential biomarkers of disease progression in ALS clinical trials.
ALS is a disease affecting the brain and spinal cord which leads to weakness and muscle wasting. It is important to be able to measure disease-related changes whilst clinical trials are ongoing to assess whether the treatments being tested are working.
We imaged the brain and spinal cord of people with and without ALS at 3 time points over a year. We found changes in the brain and spine over time. This study demonstrates that brain imaging could be potentially used to assess changes in disease progression during clinical trials, giving an indication of whether the treatments being tested are having an effect.
Diffusion MRI dMRI images the white matter structures indirectly by mapping the diffusion process of molecules, mainly water, in biological tissues 1.
The degeneration of axons affects the anisotropic characteristics of diffusion. Quantification of the changes in diffusion using dMRI represents the changes in anisotropic characteristics due to degeneration of axonal membranes, myelin sheaths, or the changes in axon density and coherence 2.
ALS, which is a fatal neurodegenerative disorder characterized by progressive weakness of the limb, bulbar and respiratory muscles 3 , causes degeneration in motor and extramotor neural pathways 4.
Notably, the name of the disease itself references pathological changes in corticospinal tract CST , a prominent white matter pathway for which the role of diffusion imaging has clear face validity.
Quantification of the alterations in the central nervous system due to neurodegeneration has the potential to improve our understanding of ALS and to provide novel objective biomarkers of disease progression. There exist several studies that report cross-sectional differences and longitudinal changes in brain dMRI metrics in ALS see reviews in ref.
Prior studies have shown the potential of diffusion tensor imaging DTI for early diagnosis of ALS before the development of clinical signs 8. The degree of directionality of diffusion anisotropic behavior measured by Fractional Anisotropy FA is useful for assessing degeneration in tissue structure in patients without upper motor neuron UMN signs 9 but is not sufficiently sensitive at the single-patient level.
FA abnormalities in the CST and callosal body have been shown to correlate with clinical UMN burden Both FA and tractography based fiber connectivity measures have also been shown to provide complementary information, with FA sensitive to the detection of patients vs. control group differences and connectivity measures correlating with disease progression rate Tract-based spatial statistics TBSS has also been used for ALS data analysis 12 , 13 , Li et al.
They reported consistent reduction in FA in the bilateral frontal white matter, cingulate gyrus and the posterior limb of bilateral internal capsules.
Reduced FA in the cingulate gyrus and superior longitudinal fasciculus, areas that play a crucial role in emotional processing, motivation, and goal-directed behaviors, has also been reported 15 , 16 , which may provide a potential imaging correlate for the cognitive and behavioral symptoms commonly seen in ALS.
Mendili et al. provided a comprehensive review of spinal cord imaging methods, their advantages, and drawbacks They discussed magnetization transfer imaging, MR spectroscopy, and functional MRI in addition to dMRI methods.
We have provided a detailed overview of spinal cord dMRI studies 18 , 19 , 20 , 21 , 22 , 23 in ALS in our prior work In our spinal cord MRI paper, we reported the results of a tract-specific and along-the-tract analysis of different spinal cord measures including FA and cross-sectional area CSA , from C2 to C6.
We demonstrated that the tract-specific analysis with segmentation of ascending and descending tracts in the spinal cord white matter substantially increases the sensitivity of dMRI to disease-related changes in ALS.
We identified the tracts and spinal levels affected in ALS, and reported the involvement of sensory pathways in ALS. We also noted strong correlations between spinal diffusion and cross-sectional area metrics and the spinal components of ALSFRS-R.
In this study, our focus is on multimodal analysis of brain and spinal cord measures. van der Burgh et al. conducted a multimodal longitudinal study of disease-related structural changes in the brain in ALS, using dMRI and T2 imaging They assessed cortical thickness, subcortical volume, and white matter connectivity, and reported distinct patterns of cerebral degeneration based on heterogeneity of phenotype and C9orf72 genotype.
They reported widespread gray and white matter involvement at baseline, and extensive loss of white matter integrity over time in patients with a C9orf72 mutation.
In C9orfnegative patients, cortical thinning of motor and frontotemporal regions and loss of integrity of white matter pathways associated with the motor cortex were noted.
They also reported more white matter involvement at baseline in spinal onset participants and greater gray matter involvement in bulbar onset participants. Querin et al. conducted a multimodal analysis of spinal cord data, with dMRI, T2, and magnetization transfer ratio MTR imaging, with a goal to improve diagnostic performance Borsodi et al.
These multimodal studies used either brain or spinal cord data, not both, and primarily used tract-based DTI methods that do not resolve crossing fiber bundles.
In this study, we compare multiple measures from the brain and spinal cord in cohorts of people with ALS and healthy control participants. We use the ball-and-stick multi-compartment model that resolves crossing fibers FSL bedpostx algorithm 28 in a TBSS analysis rather than using the DTI model.
The ball-and-stick model uses a ball compartment to represent the isotropic diffusion and multiple stick components to represent the anisotropic diffusion in crossing fibers.
We present results from multiple MRI-derived measures of the brain including cortical thickness, FA, sum of fiber volume fractions Fsum , and fixel-based measures, comparing ALS participants with control participants.
The fixel refers to a single fiber population within a voxel, and the fixel-based analysis FBA conducts statistical analysis of the fixel-based measures, the fiber density FD , the fibre-bundle cross-section FC , and the combined measure of fiber density and cross-section FDC We combine these measures from the brain with FA and CSA of the spinal cord.
We hypothesize that combining diffusion and morphometry measures from the brain and spinal cord in a multimodal analysis will improve the discrimination power between ALS and control participants. Our results demonstrate that a multimodal analysis of brain and spinal cord MRI data can provide increased diagnostic accuracy and sensitivity, and the fixel-based measures are useful in measuring disease progression over shorter durations.
We also noted statistically significant correlations between fixel-based measures and the ALSFRS-R. We recruited participants who met revised El Escorial Criteria 30 for clinically possible, probable, or definite ALS from the ALS Association Certified Treatment Centers of Excellence at the University of Minnesota and Hennepin County Medical Center.
Healthy control participants with matching age range and sex frequency were recruited from the general public. Participants who had neurologic illnesses other than ALS, the inability to tolerate MRI scanning, or who failed to meet MRI safety criteria were all excluded from the study. Exclusion criteria for control participants included 1 presence of neurological illness, 2 abnormal neurological examination, or 3 abnormal cognitive screening.
Written informed consent was collected using procedures approved by the Institutional Review Board: Human Participants Committee of the University of Minnesota. All participants underwent imaging at time of enrollment, in compliance with all the ethical regulations.
The participants were asked to return for follow-up visits at 6 and 12 months after enrollment. We previously reported spinal cord MRI findings 24 and brain magnetic resonance spectroscopy findings in this cohort 31 , Neuromuscular examination was done by a neuromuscular neurologist D.
at enrollment and at the month follow-up. ALS Functional Rating Scale-Revised ALSFRS-R 33 score was calculated to measure the functional impairment in ALS participants at enrollment, six-month, and month visits. Behavioral and cognitive status was assessed in all participants at all visits using the Edinburgh Cognitive Behavioral ALS Screen ECAS Total ECAS and its ALS-specific component scores were recorded.
UMN burden score was derived based on the neuromuscular examination as previously described in ref. A lower motor neuron LMN burden score was derived based on the scoring system proposed by Devine et al. The LMN burden score ranges from 0 to 12, with a higher score indicating greater LMN burden.
All clinical assessments were done within one week of the MRI exam. Current riluzole use was also documented. The disease duration was calculated as the time from the date of first reported symptoms to the date of the MRI exam. Seventeen additional volumes without diffusion encoding were equally interleaved in the dataset yielding a total of volumes.
We obtained 90 slices with thickness 1. Two sets of data were collected during each session, with reversed phase encoding directions anterior to posterior and posterior to anterior.
They were subsequently combined to correct for distortions and to increase signal to noise ratio The protocol used for spinal cord data acquisition was described in our prior work Supplementary Fig. The data were corrected for distortions due to eddy currents, susceptibility-induced off-resonance artifacts and subject motion 37 , Brain was extracted from both diffusion and T1 images.
Both ball-and-stick bedpostx and DTI dtifit models were subsequently fitted to the corrected diffusion data using FSL DTI metrics as well as the sum of fiber volume fraction Fsum maps were calculated for further statistical analysis.
We included Fsum in the analysis as Fsum is considered an extension of FA, with fiber crossings taken into account. Cortical thickness was calculated using Freesurfer software 39 , The fixel-based metrics see the next subsection were calculated using Mrtrix software For the multimodal analysis we also used FA and CSA measures from the spinal cord, the extraction of these measures has been reported previously The fixel-based analysis FBA addresses the challenge of resolving crossing fibers by extracting measures that are linked to individual fiber populations called fixels within a voxel that are directly related to the white matter anatomy.
The amplitudes of the fiber orientation distributions FOD were calculated to derive the fixels, using constrained spherical deconvolution The FODs were segmented to evaluate the orientation and number of fixels in each voxel and generate the fiber density FD , fiber-bundle cross-section FC and a product of both, fiber density and cross-section FDC maps.
The identification of related fixels is driven by a template tractogram generated with anatomically relevant white matter tracts FBA metrics describe the total intra-axonal volume that is related to the white matter microstructure; and the fiber bundle cross-section that describes the macroscopic differences in fiber bundle.
Due to their specificity, FBA metrics are able to differentiate between affected and non-affected tracts within a given voxel unlike the conventional DTI that resolves only one fiber population Therefore, FBA is more useful in characterizing the complex geometry of the white matter with more clinical relevance pertaining to its biological specificity.
Due to a scanner upgrade during the study, we used both Trio and Prisma scanners for both baseline and follow-up scans. Sequence parameters and protocols were carefully matched. At baseline, 10 of the 20 ALS participants were scanned on Trio and the other 10 were scanned on Prisma.
For the controls, 7 participants were scanned on Trio and 13 participants were scanned on Prisma at baseline. We conducted two-sample, two-tailed, unpaired t-tests between the two scanner groups in the control participants at baseline, and did not find statistically significant differences in any of the metrics we used.
We also conducted a similar analysis between the two scanner groups in ALS participants, which too did not detect statistically significant differences.
We first conducted statistical analysis of FA data, as well as the sum of fiber volume fractions Fsum , from the cross-sectional and longitudinal data, using whole brain tract-based spatial statistics TBSS Mean cortical thickness measurements of the baseline ALS and control participants were compared using two-sample, two-tailed, unpaired t-tests across the corresponding left and right ROIs.
Cortical thickness measurements at baseline and follow-up 6 and 12 months for ALS participants were also analyzed for longitudinal change, using two-tailed paired t-test. Cross-sectional and longitudinal analysis of FD, FC, and FDC metrics were done using whole brain fixel-based analysis using non-parametric permutation testing In order to extract brain and spine metrics for the multimodal analysis, we used the following strategy.
For brain FA and Fsum, we created an ROI mask of the voxels with statistically significant group difference between ALS and control participants in the case of cross-sectional analysis or between time points for ALS participants in the case of longitudinal analysis , as detected by the TBSS analysis.
Mean values for the metrics of interest were extracted, within this mask, and were used for subsequent analyses. We followed a similar procedure for the fixel-based metrics. We note however that this was only performed for the longitudinal data, as we did not find a statistically significant difference for those metrics in the cross-sectional analysis see the Results section.
The cortical thickness measure used for cross-sectional multimodal analysis is the cortical thickness of the precentral area and that for longitudinal multimodal analysis is the cortical thickness of the superior temporal area average of left and right ROIs in both cases.
We chose these areas for the multimodal analysis as we found cortical thinning in these areas to be most significant in the corresponding cross-sectional and longitudinal single modal analyses. The measures used from the spinal cord are the mean CSA at C2 level and the mean FA in CST at the C2 level.
The cross-sectional multimodal analysis was performed with two measures from the brain Fsum or FA and cortical thickness and two measures from the spinal cord CSA and CST FA, both at the C2 level.
The p-values reported from the unimodal analysis were corrected for multiple testing across the multiple modalities using the Bonferroni-Holm method The multimodal analysis was done using multivariable logistic regression with the disease status patient or control as the response variable and the above four metrics as predictors.
Leave-one-out cross-validation was done and the mean accuracy, sensitivity, and specificity are reported. In the longitudinal case we included the FDC measure from brain as well, thereby using three measures from the brain Fsum or FA, cortical thickness, and FDC and two measures from the spinal cord CSA and CST RD, both at the C2 level.
The predictor variable in the longitudinal case is the difference between the metrics at baseline and month follow-up. For the longitudinal data the effect size is represented using standardized response mean SRM and is calculated as the ratio of mean change D and the standard deviation of the change SD.
We studied the correlations of the imaging metrics with ALSFRS-R of the 20 ALS participants at the initial visit. The correlation is estimated by calculating the Pearson correlation coefficient between ALSFRS-R and mean FDC in left and right CST.
We also studied correlations between the within-person change in these metrics and the within-person change in ALSFRS-R over a one-year period. The statistical analysis was conducted as described in the previous subsections.
Sample size and other statistical parameters are detailed in Table 1. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Twenty ALS and 20 control participants were scanned. We have reported the demographic and clinical features of this cohort in our prior publication which focused on spinal cord data The table presenting demographic and clinical features, updated with 6-month data, is included here Table 1.
A diagram showing the flow of participants is shown in Supplementary Fig. The ALS cohort was an early-stage cohort on average mean ALSFRS-R of No ALS participants had a documented history of ALS in first-degree relatives.
Three had negative genetic testing using an ALS gene panel, two tested negative for C9orf72 repeat expansions. The others did not undergo genetic testing. One control participant scored ECAS score slightly below the cognitive screen normal cutoff ECAS score at enrollment but was included as this was determined to be due to a misunderstanding of one of the instructions; cognitive scores at 6 and 12 months were well within normal limits for this participant ECAS scores of and , respectively.
Repeat scanning at 6-month follow-up was done for 10 ALS and 14 control participants, and the repeat scanning at month follow-up was done for 11 ALS and 13 control participants.
The ALSFRS-R of ALS participants who were re-scanned at 12 months decreased by a mean of 5. Four deaths occurred in the ALS cohort and no deaths occurred in the control cohort, between the baseline and one-year follow-up. Figure 1 shows results of the TBSS analysis axial slices for the ALS and control participants.
The ALS group exhibited lower fiber volume fractions Fsum, panels in rows one and two compared to the healthy control group in many regions. Clusters with significantly lower Fsum included the corpus callosum regions of genu, body and splenium , corticospinal tract, posterior limb of the internal capsule, retrolenticular part of internal capsule, external capsule, anterior, superior and posterior corona radiata, posterior thalamic radiation, superior longitudinal fasciculus, tapetum, sagittal stratum, cerebellar peduncle, superior cerebellar peduncle, superior fronto-occipital fasciculus, anterior limb of internal capsule, uncinate fasciculus bilateral findings for all the regions of interest, ROIs , and the pontine crossing tract refer Supplementary Data 1 for the full list of regions with cluster size and MNI coordinates, where lower Fsum is detected.
FA was also lower in many of these regions lower FA in ALS participants, panels in rows three and four , but not the left superior fronto-occipital fasciculus, left anterior limb of internal capsule, and the right uncinate fasciculus.
Both precentral gyrus which includes the primary motor cortex and post central gyrus which includes the primary somatosensory cortex show detectable group differences by Fsum as well as FA. The Fsum analysis detected a higher number of voxels with significant differences between the ALS and control cohorts in all the regions Fig.
Other cortical areas with significant differences in Fsum but not FA include the supplementary motor cortex the juxtapositional lobule cortex , precuneus cortex, middle temporal gyrus, superior frontal gyrus, and the frontal pole.
Note that our observations about these cortical areas are based on dMRI metrics tested in the white matter adjacent to those cortical areas. In all the above analyses, Fsum and FA were lower in the ALS cohort in all the regions detected.
There were no regions in which Fsum or FA was higher in the ALS cohort compared to controls. Green arrows indicate regions where group differences are detected by Fsum but not by FA Fsum - Sum of fiber volume fractions, FA Fractional anisotropy.
Statistically significant cortical thinning in the ALS participants is noted in the precentral cortex and pars opercularis Bonferroni-Holm corrected p-values 0. We did Bonferroni-Holm correction 49 for multiple testing for the 34 cortical regions we tested as per the Desikan—Killiany atlas We noted marginally significant cortical thinning in many other areas, but those p -values did not survive the correction for multiple testing.
The areas where we found marginally significant cortical thinning are uncorrected p-value from two-sample t-test in parenthesis : paracentral 0. We did not find significant cross-sectional differences between ALS and control participants in FD, FC, or FDC at baseline.
We have previously reported DTI metrics of individual spinal cord tracts and the CSA of the spinal cord white matter, gray matter, and the whole cord from C2 to C6 Here we report the mean CSA of the whole cord and the mean FA of CST at the C2 level, and use those for a multimodal analysis with metrics from the brain.
In the cross-sectional multimodal analysis we used four independent metrics, two diffusion metrics one each from the brain and the spine , and two morphology metrics one each from the brain and the spine.
The metrics used are mean FA extracted from the voxels from TBSS analysis where we found statistically significant group differences between ALS and control participants which cover affected regions listed in the TBSS results section, including corticospinal tract and corpus callosum , cortical thickness of the precentral area, FA in CST at C2 level, and CSA at C2 level.
We first present the unimodal analysis results with these metrics as well as with Fsum which is extracted in a similar manner as FA. Group differences in these diffusion and morphometric measures, as well as the effect sizes and p-values from two-sample t-tests, are shown in Fig.
Brain FA provided the greatest effect size and lowest p-value, followed by brain Fsum. In Table 3 we show the group classification performance of individual metrics as well as multimodal metric. The multimodal metric with brain FA, precentral cortical thickness, CSA at C2, and FA in CST at C2 provided the highest accuracy Although Fsum detected more affected regions in the TBSS analysis, FA provided a higher classification rate see Discussion section for a discussion on this , hence Fsum was not included in the multimodal analysis.
We also studied the ability of the multimodal metrics to distinguish ALS participants with low UMN burden from controls. We analyzed the data from those 13 ALS participants who had a UMN burden score of 1 or 2 out of 6 , and found that all metrics except Fsum which did not survive correction for multiple testing differentiated these participants from controls, albeit with lower significance i.
We did not find statistically significant differences in any of these metrics between low and high UMN burden score participants. We also report the accuracy, sensitivity, and specificity of the separate and multimodal metrics in distinguishing these low UMN score participants from controls.
The multimodal analysis provided much higher accuracy and sensitivity compared to the corresponding single modality metrics in this case. The multimodal analysis with brain FA, precentral cortical thickness, CSA at C2, and FA in CST at C2 provided an accuracy of We have defined the LMN predominant participants as participants who received low UMN score 1 or 2 and high LMN score.
High LMN score is defined as severe weakness antigravity or worse in 2 or more limbs. Six participants were identified in this subgroup Fig.
Brain FA, Fsum, and precentral cortical thickness distinguished these participants from controls with high statistical significance Table 2 , last column. However the spinal cord measures did not distinguish the LMN predominant ALS participants from controls.
As we only had 6 participants in this category, we have not conducted the accuracy and sensitivity cross-validation analysis for this subgroup.
We performed an exploratory analysis comparing ALS participants with upper-limb onset 10 participants , lower-limb onset 5 participants , or bulbar-onset ALS 5 participants.
Limb onset participants had lower Fsum and FA of the brain, cortical thickness, and FA and CSA of the cervical cord than bulbar onset participants.
Lower limb onset participants had lower cortical thickness p -value 0. For the other measures Fsum and FA of the brain, CSA and FA of the cord , the value was slightly lower in the upper limb onset participants, but this difference was not statistically significant. We found marginally significant that is p -values were not significant after correction for multiple testing differences in Fsum and FA between these participants.
Both Fsum uncorrected p -value: 0. We did not find statistically significant correlations between ALSFRS-R and FA, Fsum, or cortical thickness.
However we found strong correlations between ALSFRS-R and mean FDC of both left Fig. There were no statistically significant correlations between any of the MRI measures and UMN burden score.
Correlation between ALSFRS-R and a mean FDC of brain CST left and b mean FDC of brain CST right. In our prior work 24 we found that gray matter FA at C2 at the initial visit predicted study withdrawal due to progression of functional deficits from ALS. By contrast, brain metrics at enrollment did not predict survival or study withdrawal due to disease progression.
Figure 4 upper two rows shows the longitudinal TBSS analysis for the ALS participants baseline vs. Longitudinal decreases in both Fsum and FA were detected in the cerebral peduncle, posterior limb of internal capsule, retrolenticular part of internal capsule bilateral findings for all the ROIs , right thalamus, left pallidum, and brain-stem refer Supplementary Data 1 for the full list of regions with cluster size and MNI coordinates, where decrease in Fsum is detected.
We did not find any longitudinal change in the control participants. Fsum Sum of fiber volume fractions, FA Fractional anisotropy. After a Bonferroni-Holm correction for 34 regions tested, we noted only marginally significant group reduction that is, those p-values did not survive the correction for multiple testing in cortical thickness in the ALS cohort in the following regions uncorrected p-value from paired t-test in parentheses : medial orbital frontal 0.
FDC, FD, and FC detected greater longitudinal decline than FA or Fsum. More regions showed decline in FDC, compared to FD or FC.
Areas of statistically significant longitudinal change in the ALS cohort are shown in Fig. Decreased FDC was detected in corticospinal tract, superior corona radiata, cerebral peduncle, posterior limb of internal capsule bilateral findings for all the ROIs , body of corpus callosum, and the pontine crossing tract refer Supplementary Data 1 for the full list of regions with cluster size and MNI coordinates, where decrease in FDC is detected.
Figure 2 Longitudinal FBA results showing coronal and sagittal views of fiber alterations.
Background: Djagnosis response assessment in patients with Blood pressure tips metastasis uses contrast enhanced T1-weighted MRI. Advanced MRI diagnosis accuracy techniques have wccuracy MRI diagnosis accuracy, but the diagnostic accuracy is not well known. Therefore, we performed a metaanalysis to assess the diagnostic accuracy of the currently available MRI techniques for treatment response. Methods: A systematic literature search was done. Study selection and data extraction were done by two authors independently. Meta-analysis was performed using a bivariate random effects model.
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