Abstract #2057
Prediction of Time Between CIS Onset and Clinical Conversion to MS using Random Forests
Viktor Wottschel 1,2 , Daniel C. Alexander 2 , Declan T. Chard 3 , Christian Enzinger 4 , Massimo Filippi 5 , Jette Frederiksen 6 , Claudio Gasperini 7 , Antonio Giorgio 8 , Maria A. Rocca 5 , Alex Rovira 9 , Nicola De Stefano 8 , Mar Tintor 9 , David H. Miller 3 , and Olga Ciccarelli 1
1
NMR Research Unit, Department of Brain
Repair and Rehabilitation, Queen Square MS Centre, UCL
Institute of Neurology, London, London, United Kingdom,
2
Microstructure
Imaging Group, Centre for Medical Image Computing,
Department for Computer Science, UCL, London, London,
United Kingdom,
3
NMR
Research Unit, Department of Neuroimflammation, Queen
Square MS Centre, UCL Institute of Neurology, London,
United Kingdom,
4
Department
of Neurology and Section of Neuroradiology, Medical
Unversity of Graz, Graz, Graz, Austria,
5
Neuroimaging
Research Unit, Vita-Salute San Raffaele University,
Milan, Milan, Italy,
6
Department
of Neurology, Glostrup Hospital and University of
Copenhagen, Copenhagen, Copenhagen, Denmark,
7
Neurology
Unit, San Camillo-Forlanini Hospital, Rome, Rome, Italy,
8
Department
of Neurological and Behavioral Sciences, University of
Siena, Siena, Siena, Italy,
9
Department
of Radiology and Neuroimmunology Unit, Hospital Vall
d'Hebron, Barcelona, Barcelona, Spain
We present a feasibility study predicting the
time-to-conversion (in days) from clinically isolated
syndrome (CIS) to clinically definite multiple sclerosis
(CDMS) using the machine learning technique random
forests. T1 weighted baseline MRI data of 203 CIS
patients from multiple European centres was spatially
normalised and subdivided in 100 independent training
and testing sets. From every training set an individual
random forests was created consisting of 100 trees. The
median error over all 100 bootstraps was 0.7 (range
0.57-1.16). Considering the slightly skewed data set and
high similarity in T1 signal in the patient cohort, this
is a very promising result.
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