Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis
Abstract Background Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to c...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-09-01
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| Series: | The Egyptian Journal of Radiology and Nuclear Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43055-021-00582-2 |
| _version_ | 1818730844838166528 |
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| author | Mina Rizkallah Mohamed Hefida Mohamed Khalil Rasha Mahmoud Dawoud |
| author_facet | Mina Rizkallah Mohamed Hefida Mohamed Khalil Rasha Mahmoud Dawoud |
| author_sort | Mina Rizkallah |
| collection | DOAJ |
| description | Abstract Background Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to clinical disease progression, as well as serving as a useful outcome in evaluating MS therapies. The aim of our study was to evaluate the role of automated segmentation and quantification of deep grey matter structures and white matter lesions in Relapsing Remitting Multiple Sclerosis patients using MR images and to correlate the volumetric results with different degrees of disability based on expanded disability status scale (EDSS) scores. Results All the patients in our study showed relative atrophy of the thalamus and the putamen bilaterally when compared with the normal control group. Statistical analysis was significant for the thalamus and the putamen atrophy (P value < 0.05). On the other hand, statistical analysis was not significant for the caudate and the hippocampus (P value > 0.05); there was a significant positive correlation between the white matter lesions volume and EDSS scores (correlation coefficient of 0.7505). On the other hand, there was a significant negative correlation between the thalamus and putamen volumes, and EDSS scores (correlation coefficients < − 0.9), while the volumes of the caudate and the hippocampus had a very weak and non-significant correlation with the EDSS scores (correlation coefficients > − 0.35). Conclusions The automated segmentation and quantification tools have a great role in the assessment of brain structural changes in RRMS patients, and that it became essential to integrate these tools in the daily medical practice for the great value they add to the current evaluation measures. |
| first_indexed | 2024-12-17T23:08:15Z |
| format | Article |
| id | doaj.art-c461124bbdd94a1da5a548bdea5b78ec |
| institution | Directory Open Access Journal |
| issn | 2090-4762 |
| language | English |
| last_indexed | 2024-12-17T23:08:15Z |
| publishDate | 2021-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | The Egyptian Journal of Radiology and Nuclear Medicine |
| spelling | doaj.art-c461124bbdd94a1da5a548bdea5b78ec2022-12-21T21:29:12ZengSpringerOpenThe Egyptian Journal of Radiology and Nuclear Medicine2090-47622021-09-0152111710.1186/s43055-021-00582-2Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosisMina Rizkallah0Mohamed Hefida1Mohamed Khalil2Rasha Mahmoud Dawoud3Ministry of Health, Tanta UniversityFaculty of Medicine, Tanta UniversityFaculty of Medicine, Tanta UniversityFaculty of Medicine, Tanta UniversityAbstract Background Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to clinical disease progression, as well as serving as a useful outcome in evaluating MS therapies. The aim of our study was to evaluate the role of automated segmentation and quantification of deep grey matter structures and white matter lesions in Relapsing Remitting Multiple Sclerosis patients using MR images and to correlate the volumetric results with different degrees of disability based on expanded disability status scale (EDSS) scores. Results All the patients in our study showed relative atrophy of the thalamus and the putamen bilaterally when compared with the normal control group. Statistical analysis was significant for the thalamus and the putamen atrophy (P value < 0.05). On the other hand, statistical analysis was not significant for the caudate and the hippocampus (P value > 0.05); there was a significant positive correlation between the white matter lesions volume and EDSS scores (correlation coefficient of 0.7505). On the other hand, there was a significant negative correlation between the thalamus and putamen volumes, and EDSS scores (correlation coefficients < − 0.9), while the volumes of the caudate and the hippocampus had a very weak and non-significant correlation with the EDSS scores (correlation coefficients > − 0.35). Conclusions The automated segmentation and quantification tools have a great role in the assessment of brain structural changes in RRMS patients, and that it became essential to integrate these tools in the daily medical practice for the great value they add to the current evaluation measures.https://doi.org/10.1186/s43055-021-00582-2Multiple sclerosisMRI volumetry in RRMSBrain volume loss |
| spellingShingle | Mina Rizkallah Mohamed Hefida Mohamed Khalil Rasha Mahmoud Dawoud Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis The Egyptian Journal of Radiology and Nuclear Medicine Multiple sclerosis MRI volumetry in RRMS Brain volume loss |
| title | Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis |
| title_full | Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis |
| title_fullStr | Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis |
| title_full_unstemmed | Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis |
| title_short | Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis |
| title_sort | automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis |
| topic | Multiple sclerosis MRI volumetry in RRMS Brain volume loss |
| url | https://doi.org/10.1186/s43055-021-00582-2 |
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