Bozsik Bence, Tóth Eszter, Polyák Ilona, Kerekes Fanni, Szabó Nikoletta, Bencsik Krisztina, Klivényi Péter, Kincses Zsigmond Tamás
Department of Neurology, University of Szeged, Szeged, Hungary.
Department of Radiology, University of Szeged, Szeged, Hungary.
Front Neurol. 2022 May 10;13:843377. doi: 10.3389/fneur.2022.843377. eCollection 2022.
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.
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.
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.
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.
病灶数量和负荷可预测多发性硬化症的长期预后，而病灶的定位也是疾病进展的良好预测指标。这些生物标志物用于研究和临床实践，但病灶计数的可重复性尚不清楚。
共有5名评估者对140例多发性硬化症患者在六个部位的T2高信号病灶进行评估：脑室周围、皮质旁、深部白质、幕下、脊髓和视神经。T1加权图像上的黑洞和脑萎缩采用二元尺度进行主观测量。使用组内相关系数（ICC）测量可重复性。还计算了四名最准确评估者的ICC，以观察一个异常值如何影响结果。
总体而言，显示出中等可重复性（ICC为0.5 - 0.75），排除差异最大的评估者后，可重复性没有显著提高。结果最差的区域是视神经区域（ICC：0.118）和萎缩判断（ICC：0.364）。比较每个区域高病灶负荷和低病灶负荷情况发现，当病灶计数处于中等范围时，ICC更高。在病灶常见的脑室周围和深部白质区域，病灶计数较低的患者ICC更高。另一方面，皮质旁病灶和黑洞较少见，当受试者病灶较多时，ICC更高。当最准确的评估者比较低病灶负荷（ICC：0.406，CI：0.273 - 0.546）和高病灶负荷（0.702，CI：0.603 - 0.785）患者时，皮质旁区域的这种差异很显著。
病灶分类在不同位置显示出高度变异性，总体可重复性中等。由于某些区域表现不佳，未达到优异范围。因此，应考虑努力开发用于评估病灶负荷的人工智能。
