The effect of lesion filling on brain age estimation in multiple sclerosis.

IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Salem Hannoun, Grace Fayad, Nabil K El Ayoubi, Samia J Khoury
{"title":"The effect of lesion filling on brain age estimation in multiple sclerosis.","authors":"Salem Hannoun, Grace Fayad, Nabil K El Ayoubi, Samia J Khoury","doi":"10.1186/s12880-025-01897-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"356"},"PeriodicalIF":3.2000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12382106/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-025-01897-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Abstract Image

Abstract Image

病变填充对多发性硬化症脑年龄估计的影响。
背景:脑年龄估计是评估多发性硬化症(MS)神经退行性变的新兴生物标志物。然而,ms相关病变会扭曲结构测量,可能导致年龄预测模型的不准确性。病变填充已被提出作为一种纠正步骤,但其对脑年龄估计的影响及其与临床和结构标志物的关联尚不清楚。方法:我们分析了571例复发缓解型MS患者,使用BrainAgeR管道从非病变填充和病变填充的t1加权图像估计脑年龄。应用偏倚校正去除年龄相关的预测偏倚。脑年龄差距(BAG)计算为校正后的预测脑年龄与实足年龄之间的差异。采用多变量线性回归模型评估BAG与临床结果(EDSS、9HPT、SDMT、25FWT)和体积测量之间的关系。结果:非病变填充和病变填充的脑年龄估计显示出极好的一致性(r = 0.97; ICC = 0.962),平均差异为1.23岁,病变填充预测的平均绝对误差略低(8.12比9.40岁)。两种BAG测量均与EDSS、9HPT和SDMT显著相关,尽管效应量不大。病变填充的BAG显示出与灰质、丘脑和海马体积更强、更一致的关联,这些关联在Bonferroni校正后仍然显著。结论:病变填充适度提高了MS脑年龄估计的结构可解释性,但对临床相关性的影响有限。病变填充和非病变填充估计之间的高度一致性证实了脑年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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信