Valentin Demeusy1, Florent Roche1, Fabrice Vincent1, Jean-Pierre Guichard2, Jessica Lebenberg3,4, Eric Jouvent3,5, and Hugues Chabriat3,5
1Imaging Core Lab, Medpace, Lyon, France, 2Department of Neuroradiology, Hôpital Lariboisière, APHP, Paris, France, 3FHU NeuroVasc, INSERM U1141, Paris, France, 4Université de Paris, Paris, France, 5Departement of Neurology, Hôpital Lariboisière, APHP, Paris, France
1Imaging Core Lab, Medpace, Lyon, France, 2Department of Neuroradiology, Hôpital Lariboisière, APHP, Paris, France, 3FHU NeuroVasc, INSERM U1141, Paris, France, 4Université de Paris, Paris, France, 5Departement of Neurology, Hôpital Lariboisière, APHP, Paris, France
In 101 CADASIL patients, an automatic WMH segmentation method using a convolutional neural network showed consistent measures at baseline correlated with the Fazekas score and variable longitudinal volumetric changes at individual level also correlated to the amount of lesions at baseline.
Figure 2: WMH volume evolution for each subject according to age. Each subject was attributed a Fazekas score at baseline. This demonstrates the variable growth of the WMH for each subject even for those with an older age.
Figure 1: WMH volume per Fazekas score; both evaluated at baseline. WMH volume is highly correlated to the Fazekas score as shown by the clear separation of the different classes in the boxplot and the Spearman correlation of 0.921 (p-value < 0.001).