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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | NeuroImage |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920309368 |
| _version_ | 1818610207250448384 |
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| author | Elizabeth M. Sweeney Thanh D. Nguyen Amy Kuceyeski Sarah M. Ryan Shun Zhang Lily Zexter Yi Wang Susan A. Gauthier |
| author_facet | Elizabeth M. Sweeney Thanh D. Nguyen Amy Kuceyeski Sarah M. Ryan Shun Zhang Lily Zexter Yi Wang Susan A. Gauthier |
| author_sort | Elizabeth M. Sweeney |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T15:10:46Z |
| format | Article |
| id | doaj.art-8956a5b875544314a0320ab186282389 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-16T15:10:46Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-8956a5b875544314a0320ab1862823892022-12-21T22:26:58ZengElsevierNeuroImage1095-95722021-01-01225117451Estimation of Multiple Sclerosis lesion age on magnetic resonance imagingElizabeth M. Sweeney0Thanh D. Nguyen1Amy Kuceyeski2Sarah M. Ryan3Shun Zhang4Lily Zexter5Yi Wang6Susan A. Gauthier7Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, United States; Corresponding author.Department of Radiology, Weill Cornell Medical College, New York, NY, United StatesDepartment of Radiology, Weill Cornell Medical College, New York, NY, United States; Brain and Mind Institute, Weill Cornell Medical College, New York, NY, United StatesDepartment of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United StatesDepartment of Radiology, Tongji Hospital, Wuhan, ChinaDepartment of Neurology, Weill Cornell Medical College, New York, NY, United StatesDepartment of Radiology, Weill Cornell Medical College, New York, NY, United StatesDepartment of Radiology, Weill Cornell Medical College, New York, NY, United States; Brain and Mind Institute, Weill Cornell Medical College, New York, NY, United States; Department of Neurology, Weill Cornell Medical College, New York, NY, United StatesWe 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.http://www.sciencedirect.com/science/article/pii/S1053811920309368 |
| spellingShingle | Elizabeth M. Sweeney Thanh D. Nguyen Amy Kuceyeski Sarah M. Ryan Shun Zhang Lily Zexter Yi Wang Susan A. Gauthier Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging NeuroImage |
| title | Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging |
| title_full | Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging |
| title_fullStr | Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging |
| title_full_unstemmed | Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging |
| title_short | Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging |
| title_sort | estimation of multiple sclerosis lesion age on magnetic resonance imaging |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920309368 |
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