Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging

We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and can be used in applications such as studying the tem...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Vol. 225; p. 117451
Main Authors: Sweeney, Elizabeth M., Nguyen, Thanh D., Kuceyeski, Amy, Ryan, Sarah M., Zhang, Shun, Zexter, Lily, Wang, Yi, Gauthier, Susan A.
Format: Journal Article
Language:English
Published: United States Elsevier Inc 15.01.2021
Elsevier Limited
Elsevier
Subjects:
Adult
Age
Age determination
Algorithms
Brain - diagnostic imaging
Contrast Media
Female
Gadolinium
Humans
Lesions
Longitudinal studies
Machine Learning
Magnetic Resonance Imaging
Male
Middle Aged
Multiple sclerosis
Multiple Sclerosis - diagnostic imaging
Patients
Radiomics
Reproducibility of Results
Scanners
Statistical analysis
Time Factors
ISSN:1053-8119, 1095-9572, 1095-9572
Online Access:Get full text
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Summary:We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and can be used in applications such as studying the temporal dynamics of chronic active MS lesions. Our lesion age estimation models use first order radiomic features over a lesion derived from conventional T1 (T1w) and T2 weighted (T2w) and fluid attenuated inversion recovery (FLAIR), T1w with gadolinium contrast (T1w+c), and Quantitative Susceptibility Mapping (QSM) MRI sequences as well as demographic information. For this analysis, we have a total of 32 patients with 53 new lesions observed at 244 time points. A one or two step random forest model for lesion age is fit on a training set using a lesion volume cutoff of 15 mm3 or 50 mm3. We explore the performance of nine different modeling scenarios that included various combinations of the MRI sequences and demographic information and a one or two step random forest models, as well as simpler models that only uses the mean radiomic feature from each MRI sequence. The best performing model on a validation set is a model that uses a two-step random forest model on the radiomic features from all of the MRI sequences with demographic information using a lesion volume cutoff of 50 mm3. This model has a mean absolute error of 7.23 months (95% CI: [6.98, 13.43]) and a median absolute error of 5.98 months (95% CI: [5.26, 13.25]) in the validation set. For this model, the predicted age and actual age have a statistically significant association (p-value <0.001) in the validation set.
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EMS, TDN, YW, AK and SG conceptualized the problem. EMS developed the methodology and analyzed the data. TDN processed the data. SR assisted in methodology development. SZ made manual segmentations. LZ compiled patient demographics. TDN, YW, and SG recruited the participants and acquired the data. EMS TDN and SG wrote the manuscript. SG supervised the work. All authors read and approved the final manuscript.
Contributions
ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2020.117451