Hannoun Salem, Fayad Grace, El Ayoubi Nabil K, Khoury Samia J
Medical Imaging Sciences Program, Division of Health Professions, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon.
Nehme and Therese Tohme Multiple Sclerosis Center, Department of Neurology, American University of Beirut Medical Center, Beirut, Lebanon.
BMC Med Imaging. 2025 Aug 27;25(1):356. doi: 10.1186/s12880-025-01897-6.
Brain age estimation is an emerging biomarker for assessing neurodegeneration in multiple sclerosis (MS). However, MS-related lesions can distort structural measurements, potentially leading to inaccuracies in age prediction models. Lesion filling has been proposed as a corrective step, but its impact on brain age estimation and its associations with clinical and structural markers remains unclear.
We analyzed 571 relapsing-remitting MS patients using the BrainAgeR pipeline to estimate brain age from both non-lesion-filled and lesion-filled T1-weighted images. Bias correction was applied to remove age-related prediction bias. Brain Age Gap (BAG) was computed as the difference between corrected predicted brain age and chronological age. Multivariable linear regression models were used to assess associations between BAG and clinical outcomes (EDSS, 9HPT, SDMT, 25FWT) and volumetric measures.
Non-lesion-filled and lesion-filled brain age estimates showed excellent agreement (r = 0.97; ICC = 0.962), with a mean difference of 1.23 years and slightly lower mean absolute error for lesion-filled predictions (8.12 vs. 9.40 years). Both BAG measures were significantly associated with EDSS, 9HPT, and SDMT, though effect sizes were modest. Lesion-filled BAG showed stronger and more consistent associations with gray matter, thalamic, and hippocampal volumes, and these associations remained significant after Bonferroni correction.
Lesion filling modestly improves structural interpretability of brain age estimates in MS but has limited effect on clinical correlations. The high concordance between lesion-filled and non-lesion-filled estimates confirms the robustness of brain age as a biomarker, while supporting the use of lesion correction when structural precision is essential.
脑龄估计是评估多发性硬化症（MS）神经退行性变的一种新兴生物标志物。然而，与MS相关的病变会扭曲结构测量结果，可能导致年龄预测模型不准确。有人提出进行病变填充作为校正步骤，但其对脑龄估计的影响及其与临床和结构标志物的关联仍不清楚。
我们使用BrainAgeR流程分析了571例复发缓解型MS患者，从未填充病变和填充病变的T1加权图像估计脑龄。应用偏差校正以消除与年龄相关的预测偏差。脑龄差距（BAG）计算为校正后的预测脑龄与实际年龄之间的差值。使用多变量线性回归模型评估BAG与临床结局（扩展残疾状态量表、9孔插板试验、符号数字模式测验、25英尺步行时间）和体积测量之间的关联。
未填充病变和填充病变的脑龄估计显示出极好的一致性（r = 0.97；组内相关系数 = 0.962），平均差异为1.23岁，填充病变预测的平均绝对误差略低（8.12岁对9.40岁）。两种BAG测量均与扩展残疾状态量表、9孔插板试验和符号数字模式测验显著相关，尽管效应大小适中。填充病变的BAG与灰质、丘脑和海马体积显示出更强且更一致的关联，并且在Bonferroni校正后这些关联仍然显著。
病变填充适度改善了MS中脑龄估计的结构可解释性，但对临床相关性的影响有限。填充病变和未填充病变估计之间的高度一致性证实了脑龄作为生物标志物的稳健性，同时支持在结构精度至关重要时使用病变校正。
