Novel composite MRI scale correlates highly with disability in multiple sclerosis patients

Abstract

Understanding genotype-phenotype relationships or development/validation of biomarkers requires large multicenter cohorts integrated by universal quantification of crucial phenotypical traits, such as central nervous system (CNS) tissue destruction. We hypothesized that mathematical modeling-guided combination of biologically meaningful, semiquantitative MRI elements characterized by high signal-to-noise ratio will provide such reliable, universal tool for measuring CNS tissue destruction. We retrospectively graded 15 elements in MRI scans performed in 419 untreated subjects with or without neurological diseases, while being blinded to their prospectively acquired clinical scores. We then used 305 subjects for disability-guided mathematical modeling to select and combine MRI elements that had non-redundant contributions to clinical disability, resulting in Combinatorial MRI Scale (COMRIS). We validated our model on the remaining 114 independent subjects. COMRIS requires 5-10 minutes per scan on average to compute and demonstrates highly significant (p<0.0001) and validation-consistent Spearman correlation coefficients (0.75, 0.76, and 0.65) for the expanded disability status scale (EDSS), Scripps neurological rating scale (SNRS), and symbol digit modality test (SDMT) measures of neurological disability, respectively. Because COMRIS is not greatly influenced by MRI scanners or protocols and can be computed even in the presence of some motion artifacts, it does not require censoring out patients and it provides comparable results across different cohorts. As such, it represents a broadly available clinical and research tool that can facilitate multicenter research studies and comparative analyses across patient cohorts and research projects.

New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability

“…In addition to variables that were previously hypothesized or shown to affect disability progression such as gender, smoking, and race, we selected new features for statistical learning based on clinical knowledge. We hypothesized that the following features may negatively influence recovery from CNS lesions and, therefore, speed up accumulation of disability: age and the amount of CNS tissue destruction reflected by baseline disability (measured by CombiWISE) and/or quantified by MRI using published Combinatorial MRI Scale of CNS Tissue Destruction [COMRIS-CTD ( 11 )]. Both factors diminish available reserves for restoring lost function by plasticity, while aging limits both plasticity and remyelination.…”

“…From these data, the research database automatically computed the CombiWISE score based on the published formula ( 10 ): where log 2 (T25FW) is the logarithm (base 2) of the T25FW, T25FW FAIL is binary (0 for completed test, 1 for failed test), log 2 (NDH-9HPT) is the logarithm (base 2) of the non-dominant hand 9HPT, and NDH FAIL is binary (0 for completed test, 1 for failed test). Prospectively acquired MRI scans were semi-quantitatively rated according to the published protocol ( 11 ) and grades were entered into the research database, which automatically calculates Combinatorial MRI Scale of CNS Tissue Destruction (COMRIS-CTD) as described ( 11 ).…”

Genetic model of MS severity predicts future accumulation of disability

“…MS‐DSS (Weideman et al., ) is assigned by a statistical model using gradient boosting machines. MS‐DSS includes disability measured by a highly sensitive CombiWISE (Kosa et al., ), mathematically adjusted for the efficacy of administered treatments using a published formula (Weideman, Tapia‐Maltos, Johnson, Greenwood, & Bielekova, ), the amount of CNS‐tissue destruction measured by the Combinatorial MRI Scale of CNS Tissue Destruction (COMRIS‐CTD) (Kosa et al., ), and additional features of lower variable importance, including demographic data. The model uses the following cross‐sectional data, listed in order of statistical importance: (1) therapy‐adjusted CombiWISE divided by patient age (CombiWISE/age); (2) CombiWISE; (3) COMRIS‐CTD; (4) time to first therapy, which measures the delay (in years) from disease onset to initiation of treatment; (5) difference in therapy‐adjusted and measured CombiWISE, which reflects the variant of the disease that is treatable by current immunomodulatory treatments; (6) age, and (7) family history of MS. MS‐DSS, the modeling outcome in the current study, is automatically calculated from user‐inputted raw data via a web interface (https://bielekovalab.shinyapps.io/msdss).…”

Promise, Progress, and Pitfalls in the Search for Central Nervous System Biomarkers in Neuroimmunological Diseases: A Role for Cerebrospinal Fluid Immunophenotyping

“…Future biomarker studies need to collect high quality clinical (i.e. scales of neurological and cognitive disability) and imaging data (i.e., especially volumetric data and/or composite measures such as Combinatorial MRI Scale of CNS tissue destruction COMRIS-CTD), 51 to facilitate systems-wide analysis of relationships between biomarkers, neurological functions and structural integrity of CNS tissues. In parallel, in-vitro and in-vivo models need to generate knowledge-base for understanding biomarker-life cycles and relationships between biomarkers measured by multiplex assays.…”