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Quantitative neuroimaging in multiple sclerosis (MS): which biomarker is the most reliable in the evaluation of MS-induced atrophy?
Author(s): ,
E. Makras
Affiliations:
General Anticancer and Oncological Hospital of Athens `St. Savvas`
,
M. Pelechrini
Affiliations:
MRI and Neuroradiology, General Anticancer and Oncological Hospital of Athens `St. Savvas`
,
A.-A. Katsarou
Affiliations:
Euroclinic of Athens, Athens, Greece
,
K. Pelechrinis
Affiliations:
School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States
,
A. Gouliamos
Affiliations:
Iatropolis, Athens, Greece
,
S. Bisdas
Affiliations:
University College of London (UCL), London, United Kingdom
V. Katsaros
Affiliations:
MRI and Neuroradiology, University of Athens, Athens, Greece
ECTRIMS Online Library. Makras E. Oct 12, 2018; 229130; P1290
Elefterios Makras
Elefterios Makras
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Abstract: P1290

Type: Poster Sessions

Abstract Category: Therapy - Others

Introduction: Brain atrophy induced by MS is a frequent and well-established entity according to international literature.
Objectives: In the era of quantitative neuroimaging/ artificial intelligence assisted diagnosis and precision/personalized medicine, there is a need for tools that will allow us to reliably evaluate brain atrophy.
Aims: Our aim was to determine which quantitative biomarker extracted from whole brain volumetric assessment could be considered as the most reliable in the evaluation of MS induced brain atrophy.
Methods: Volumetric data from 118 examinations of 81 patients (mean age: 41) with an established MS diagnosis, were retrospectively included from four centers (three with 1.5T MRI, one 3T MRI). The available 3D T1 MPRAGE pre-/ 10 minutes post-Gd and FLAIR sequences were uploaded to IcoBrain(TM) platform for volumetric evaluation. . Logistic regression statistical analysis with cross-validation was then used to evaluate the resulting volume percentiles. We randomly split the data into a training set (80%) that we used to build our model and a test set (20%) that we used for its evaluation. We repeated this process 20 times and for each one of them we built three different logistic regression (logit) models for the probability of the presence of atrophy (defined as the bottom 5th percentile of the whole brain volume). Using the test set in each of the splits we calculated the sensitivity, specificity, AUROC and Brier scores respectively and report the average values.
Results: The best performing model had both white and gray matter volume percentiles as independent variables and showed the following average values: AUROC 0.99, Brier score 0.04, sensitivity 94% and specificity 95%. The rest of the models used only one independent variable (either white matter volume percentile or grey matter volume percentile). The model with the white matter volume percentile as an independent variable showed average AUROC 0.85, Brier score 0.16, sensitivity 78% and specificity 77%, while the one with the gray matter volume percentile showed average AUROC 0.86, Brier score 0.15, sensitivity 77% and specificity 79%.
Conclusion: The combination of white and gray matter volume percentiles seems the most reliable biomarker in the evaluation of MS induced atrophy and holds promise as a prognostic one.
Disclosure:
Eleftherios Makras: nothing to disclose
Maria Pelechrini: nothing to disclose
Agapi-Alexandra Katsarou: nothing to disclose
Konstantinos Pelechrinis: nothing to disclose
Sotirios Bisdas: nothing to disclose
Vasileios Katsaros: nothing to disclose
Athanasios Gouliamos: nothing to disclose

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