The identification and management of Multiple Sclerosis (MS) patients who are at risk of disease progression and/or conversion to Secondary Progressive MS (SPMS) is a significant unmet clinical need. This study aimed to develop machine and deep learning (ML/DL) models to predict disability progression and/or conversion to SPMS at least 6 months before progression, determined by Expanded Disability Status Scale (EDSS) score. Three traditional ML algorithms were trained on brain parcellation volume measurements derived from T1 weighted magnetic resonance images (MRI) and convolutional neural networks (CNNs) were trained directly on MRI images. The results showed that the three ML models performed slightly better than a random classifier, with the Support Vector Classifier achieving the best results in terms of area under the receiver operating characteristic curve (AUROC) with a score of 0.62. The CNN approach yielded the most promising results, with an AUROC score of 0.75. This suggests that the ability to identify MS patients at high risk of future disability progression directly from MRI scans is a promising research direction. Further development of these algorithms could enable timely interventions, potentially preventing or delaying the onset of disability progression and/or SPMS.

Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI) and represents a simple diagnostic biomarker of brain ageing and associated diseases. While the current best accuracy of brain age predictions on healthy subject T1w MRIs is from two to three years, comparing results from different studies is challenging due to differences in datasets, T1w preprocessing pipelines, and performance metrics used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning-based brain age models presented in recent literature. Four preprocessing pipelines were evaluated, differing in terms of registration, grayscale correction, and software implementation. The results showed that the choice of software implementation can significantly affect the prediction error, with a maximum increase of 0.7 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, the affine registration, compared to the rigid registration of T1w images to brain atlas was shown to statistically significantly improve MAE. Models trained on 3D images with isotropic 1mm3resolution were less sensitive to T1w preprocessing than 2D models or models trained on downsampled 3D images. Contrary to the indications from research literature that models trained on less preprocessed T1w scans are better suited for age predictions on T1w images from new scanners, not seen in model training, our results show that extensive T1w preprocessing in fact improves the MAE when new dataset is used. Regardless of model or T1w preprocessing used, we show that to enable generalization of model’s performance on a new dataset with either the same or different T1w preprocessing than the one applied in model training some form of bias correction should be applied.HighlightsThis study involved a thorough and reproducible quantitative assessment of the impact of four T1w preprocessing variants on brain age prediction accuracy using four recent deep learning-based model architectures.Repeated model training with random initialization and use of linear mixed-effects models enabled the statistical analysis of the effect of various confounding factors on brain age prediction accuracy.The choice of T1w preprocessing software implementation resulted in statistically significant increase in mean absolute error of up to 0.7 years for the same model and dataset.Our results show that extensive T1w preprocessing, with higher degree of freedom in T1w to atlas registration and extensive grayscale corrections, and bias correction improve the generalization of brain age models’ performances when applied on new unseen datasets.

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