感度加权磁共振成像中脑微出血的自动量化:与血管风险因素、白质高密度负荷和认知功能的关联。

Ji Su Ko, Yangsean Choi, Eun Seon Jeong, Hyun-Jung Kim, Grace Yoojin Lee, Ji Eun Park, Namkug Kim, Ho Sung Kim
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引用次数: 0

摘要

背景和目的:训练和验证基于深度学习(DL)的脑微小出血(CMB)易感加权核磁共振成像分割模型;发现CMB、认知障碍和血管风险因素之间的关联:这项单一机构回顾性研究的参与者在 2023 年 1 月至 9 月期间接受了脑磁共振成像,以评估认知障碍。在训练 DL 模型时,使用了 nnU-Net 框架,未作任何修改。DL 模型的性能在独立的内部和外部验证数据集上进行了评估。线性回归分析用于发现对数变换的CMB数量、认知功能(迷你精神状态检查[MMSE])、白质高密度(WMH)负担和临床血管风险因素(年龄、性别、高血压、糖尿病、血脂状况和体重指数)之间的关联:对 DL 模型(n = 287)进行训练后,内部验证集(n = 67)的平均骰子得分为 0.73(95% CI,0.67-0.79),外部验证集(骰子得分 = 0.46,95% CI,0.33-0.59,n = 68)的平均骰子得分为 0.73(95% CI,0.67-0.79),具有稳健的分割性能。在一个时间上独立的临床数据集中(n = 448),年龄较大、高血压和 WMH 负荷与所有分布(总、叶、深和小脑;所有 P P P = .04)中的 CMB 数量显著相关:结论:DL模型对CMB具有稳健的分割性能。在所有分布中,CMB 与 WMH 负荷呈显著正相关。WMH负荷和CMB数量的增加与认知功能的下降有关:缩写:CMB = 脑微出血;DL = 深度学习;DSC = 骰子相似系数;MMSE = 迷你精神状态检查;SVD = 小血管疾病;SWI = 易感加权图像;WMH = 白质高密度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated quantification of cerebral microbleeds in susceptibility-weighted MRI: association with vascular risk factors, white matter hyperintensity burden, and cognitive function.

Background and purpose: To train and validate a deep learning (DL)-based segmentation model for cerebral microbleeds (CMB) on susceptibility-weighted MRI; and to find associations between CMB, cognitive impairment, and vascular risk factors.

Materials and methods: Participants in this single-institution retrospective study underwent brain MRI to evaluate cognitive impairment between January-September 2023. For training the DL model, the nnU-Net framework was used without modifications. The DL model's performance was evaluated on independent internal and external validation datasets. Linear regression analysis was used to find associations between log-transformed CMB numbers, cognitive function (mini-mental status examination [MMSE]), white matter hyperintensity (WMH) burden, and clinical vascular risk factors (age, sex, hypertension, diabetes, lipid profiles, and body mass index).

Results: Training of the DL model (n = 287) resulted in a robust segmentation performance with an average dice score of 0.73 (95% CI, 0.67-0.79) in an internal validation set, (n = 67) and modest performance in an external validation set (dice score = 0.46, 95% CI, 0.33-0.59, n = 68). In a temporally independent clinical dataset (n = 448), older age, hypertension, and WMH burden were significantly associated with CMB numbers in all distributions (total, lobar, deep, and cerebellar; all P <.01). MMSE was significantly associated with hyperlipidemia (β = 1.88, 95% CI, 0.96-2.81, P <.001), WMH burden (β = -0.17 per 1% WMH burden, 95% CI, -0.27-0.08, P <.001), and total CMB number (β = -0.01 per 1 CMB, 95% CI, -0.02-0.001, P = .04) after adjusting for age and sex.

Conclusions: The DL model showed a robust segmentation performance for CMB. In all distributions, CMB had significant positive associations with WMH burden. Increased WMH burden and CMB numbers were associated with decreased cognitive function.

Abbreviations: CMB = cerebral microbleed; DL = deep learning, DSC = dice similarity coefficient; MMSE = mini-mental status examination; SVD = small vessel disease; SWI = susceptibility-weighted image; WMH = white matter hyperintensity.

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