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Reproducibility of Lesion Count in Various Subregions on MRI Scans in Multiple Sclerosis
Bence Bozsik1, Eszter Tóth1, Ilona Polyák2
1Department of Neurology, University of Szeged, Szeged, Hungary.
Purpose:
Lesion number and burden can predict the long-term outcome of multiple sclerosis, while the localization of the lesions is also a good predictive marker of disease progression. These biomarkers are used in studies and in clinical practice, but the reproducibility of lesion count is not well-known.
Methods:
In total, five raters evaluated T2 hyperintense lesions in 140 patients with multiple sclerosis in six localizations: periventricular, juxtacortical, deep white matter, infratentorial, spinal cord, and optic nerve. Black holes on T1-weighted images and brain atrophy were subjectively measured on a binary scale. Reproducibility was measured using the intraclass correlation coefficient (ICC). ICCs were also calculated for the four most accurate raters to see how one outlier can influence the results.
Results:
Overall, moderate reproducibility (ICC 0.5-0.75) was shown, which did not improve considerably when the most divergent rater was excluded. The areas that produced the worst results were the optic nerve region (ICC: 0.118) and atrophy judgment (ICC: 0.364). Comparing high- and low-lesion burdens in each region revealed that the ICC is higher when the lesion count is in the mid-range. In the periventricular and deep white matter area, where lesions are common, higher ICC was found in patients who had a lower lesion count. On the other hand, juxtacortical lesions and black holes that are less common showed higher ICC when the subjects had more lesions. This difference was significant in the juxtacortical region when the most accurate raters compared patients with low (ICC: 0.406 CI: 0.273-0.546) and high (0.702 CI: 0.603-0.785) lesion loads.
Conclusion:
Lesion classification showed high variability by location and overall moderate reproducibility. The excellent range was not achieved, owing to the fact that some areas showed poor performance. Hence, putting effort toward the development of artificial intelligence for the evaluation of lesion burden should be considered.
Insights
Multiple sclerosis lesion reproducibility is moderate, with optic nerve and atrophy showing poor performance. Developing AI for lesion burden evaluation is recommended.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Lesion number, burden, and location in multiple sclerosis (MS) predict long-term outcomes and disease progression.
- These biomarkers are crucial in MS research and clinical practice.
- Reproducibility of lesion count, a key MS biomarker, requires thorough investigation.
Purpose of the Study:
- To assess the reproducibility of counting T2 hyperintense lesions in multiple sclerosis (MS) across various brain and spinal cord locations.
- To evaluate the reproducibility of subjective assessments of T1 black holes and brain atrophy in MS patients.
- To determine the impact of rater variability and lesion burden on the reproducibility of MS lesion quantification.
Main Methods:
- Five raters evaluated T2 hyperintense lesions in 140 MS patients across six defined localizations.
- T1 black holes and brain atrophy were subjectively assessed on a binary scale.
- Reproducibility was quantified using the intraclass correlation coefficient (ICC), with analyses performed including and excluding the most divergent rater.
Main Results:
- Overall moderate reproducibility (ICC 0.5-0.75) was observed for lesion counting, with limited improvement upon outlier exclusion.
- The optic nerve region (ICC: 0.118) and atrophy judgment (ICC: 0.364) demonstrated the poorest reproducibility.
- Reproducibility varied with lesion burden; mid-range lesion counts generally yielded higher ICCs, with specific differences noted in juxtacortical regions and for black holes.
Conclusions:
- Lesion classification in MS exhibits significant variability based on location, resulting in overall moderate reproducibility.
- The study did not achieve excellent reproducibility due to poor performance in specific regions like the optic nerve.
- Development and implementation of artificial intelligence (AI) for lesion burden evaluation in MS are recommended to enhance accuracy and consistency.
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