Author Of 2 Presentations
P0515 - Application of Metabolomics to Identify Biofluid Biomarkers for Multiple Sclerosis Diagnosis (ID 297)
Abstract
Background
Early diagnosis of multiple sclerosis (MS), a lifelong chronic disease without a permanent cure, allows for the implementation of therapies that may delay the progression of the disease, reduce neurological damage, reduce relapse rates, and improve the quality of life for patients. A diagnosis of MS is limited to the exclusion of other diseases through a complex combination of expensive, invasive, and risky tests (magnetic resonance imaging, spinal tap, etc.) and a subjective interpretation of a patient’s history. The pathological heterogeneity, the different phenotypic variations, the similarity with other CNS diseases, and the complex diagnostic protocol presents a serious challenge to obtaining a rapid and accurate diagnosis for MS. As a consequence, MS patients routinely encounter extensive delays (7.5 years on average) in receiving a correct diagnosis and proper treatments.
Objectives
The objective of this study was to use our integrated NMR and mass spectrometry metabolomics methodology to identify a statistically valid set of urinary, serum and cerebrospinal fluid (CSF) metabolites correlated with MS that can be used to differentiate MS patients from healthy controls as biomarkers of disease diagnosis.
Methods
Nuclear magnetic resonance (NMR) imaging was done to identify the spectral differences found in the biofluids of MS patients and healthy controls. Biofluid samples analyzed in this study included CSF, serum and urine. Then principal component analysis (PCA), partial least squares (PLS) and orthogonal projection to latent structures- discriminant analysis (OPLS-DA) scores plot statistical analyses were done to analyze statistical differences.
Results
A statistical difference was seen in the CSF, serum and urine profiles between healthy controls and MS patients as well as between Primary Progressive MS patients and Relapsing MS patients.
Conclusions
Urinary metabolites can be used to differentiate between MS patients and healthy controls. This methodology could be used in conjunction with the McDonald criteria to help support a more rapid and accurate diagnosis of Multiple Sclerosis.
P0577 - Feasibility of thalamic atrophy measurement in clinical routine using artificial intelligence: Results from multi-center study in RRMS patients (ID 1058)
Abstract
Background
The thalamus is a key gray matter structure, and a sensitive marker of neurodegeneration in multiple sclerosis (MS). Previous reports have indicated that thalamic volumetry on clinical-quality T2-FLAIR images alone is fast and reliable, using artificial intelligence (AI).
Objectives
To investigate the feasibility of thalamic atrophy measurement using AI in patients with MS, in a large multi-center, clinical routine study.
Methods
DeepGRAI (Deep Gray Rating via Artificial Intelligence) is a multi-center (31 USA sites), longitudinal, observational, real-word, registry study that will enroll 1,000 relapsing-remitting MS patients. Brain MRI exams previously acquired at baseline and at follow-up on 1.5T or 3T scanners with no prior standardization are used, in order to resemble real-world situation. Thalamic volume measurement is performed at baseline and follow-up on T2-FLAIR by DeepGRAI tool and on 3D T1-weighted image (WI) and 2D T1-WI by using FIRST software.
Results
In this pre-planned interim analysis, 515 RRMS patients were followed for an average of 2.7 years. There were 487 (94.6%) T2-FLAIR, 342 (66.4%) 2D T1-WI and 176 (34.2%) 3D T1-WI longitudinal pair of MRI exams available for analyses. Estimation of thalamic volume by DeepGRAI on T2-FLAIR correlated significantly with FIRST on 3D-T1-WI (r=0.733 and r=0.816, p<0.001) and with FIRST on 2D-T1-WI (r=0.555 and r=0.704, p<0.001) at baseline and at follow-up. The correlation between thalamic volume estimated by FIRST on 3D T1-WI and 2D T1-WI was r=0.642 and r=0.679, p<0.001, respectively. The thalamic volume % change over the follow-up was similar between DeepGRAI (-0.75) and 3D T1-WI (-0.82), but somewhat higher for 2D T1-WI (-0.92). Similar relationship was found between the Expanded Disability Status Scale (EDSS) and thalamic volume by DeepGRAI on T2-FLAIR and by FIRST on 3D T1-WI at baseline (r=-0.214, p=0.01 and r=-0.287, p=0.001) and at follow-up (r=-0.298, p=0.001 and r=-0.291, p=0.001).
Conclusions
DeepGRAI provides feasible thalamic volume measurement on multi-center clinical-quality T2-FLAIR images. The relationship between thalamic atrophy and physical disability is similar using DeepGRAI T2-FLAIR and standard high-resolution research approaches. This indicates potential for real-world thalamic volume monitoring, as well as quantification on legacy datasets without research-quality MRI.