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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| Vydáno v: | NeuroImage (Orlando, Fla.) Ročník 225; s. 117451 |
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Elsevier Inc
15.01.2021
Elsevier Limited Elsevier |
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| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
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| Abstract | 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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| AbstractList | 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. 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. 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 mm or 50 mm . 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 mm . 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. 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.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. |
| ArticleNumber | 117451 |
| Author | Wang, Yi Sweeney, Elizabeth M. Zhang, Shun Zexter, Lily Nguyen, Thanh D. Gauthier, Susan A. Ryan, Sarah M. Kuceyeski, Amy |
| AuthorAffiliation | c Brain and Mind Institute, Weill Cornell Medical College, New York, NY, United States b Department of Radiology, Weill Cornell Medical College, New York, NY, United States e Department of Radiology, Tongji Hospital, Wuhan, China a Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, United States d Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States f Department of Neurology, Weill Cornell Medical College, New York, NY, United States |
| AuthorAffiliation_xml | – name: c Brain and Mind Institute, Weill Cornell Medical College, New York, NY, United States – name: d Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States – name: e Department of Radiology, Tongji Hospital, Wuhan, China – name: b Department of Radiology, Weill Cornell Medical College, New York, NY, United States – name: f Department of Neurology, Weill Cornell Medical College, New York, NY, United States – name: a Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, United States |
| Author_xml | – sequence: 1 givenname: Elizabeth M. surname: Sweeney fullname: Sweeney, Elizabeth M. email: ems4003@med.cornell.edu organization: Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, United States – sequence: 2 givenname: Thanh D. orcidid: 0000-0002-1411-7694 surname: Nguyen fullname: Nguyen, Thanh D. organization: Department of Radiology, Weill Cornell Medical College, New York, NY, United States – sequence: 3 givenname: Amy surname: Kuceyeski fullname: Kuceyeski, Amy organization: Department of Radiology, Weill Cornell Medical College, New York, NY, United States – sequence: 4 givenname: Sarah M. orcidid: 0000-0002-4395-1696 surname: Ryan fullname: Ryan, Sarah M. organization: Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States – sequence: 5 givenname: Shun orcidid: 0000-0002-0001-1880 surname: Zhang fullname: Zhang, Shun organization: Department of Radiology, Tongji Hospital, Wuhan, China – sequence: 6 givenname: Lily surname: Zexter fullname: Zexter, Lily organization: Department of Neurology, Weill Cornell Medical College, New York, NY, United States – sequence: 7 givenname: Yi surname: Wang fullname: Wang, Yi organization: Department of Radiology, Weill Cornell Medical College, New York, NY, United States – sequence: 8 givenname: Susan A. surname: Gauthier fullname: Gauthier, Susan A. organization: Department of Radiology, Weill Cornell Medical College, New York, NY, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33069865$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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 |
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| Snippet | We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating... |
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| StartPage | 117451 |
| SubjectTerms | 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 |
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| Title | Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging |
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