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应用机器学习方法描述结合雌激素治疗对特定区域脑容量的影响。

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes.

机构信息

Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

出版信息

Magn Reson Imaging. 2011 May;29(4):546-53. doi: 10.1016/j.mri.2010.12.001. Epub 2011 Feb 2.

DOI:10.1016/j.mri.2010.12.001
PMID:21292420
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3079024/
Abstract

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years; however, it is unknown whether this results in a broad-based characteristic pattern of effects. Structural magnetic resonance imaging was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L(1) penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and nonusers.

摘要

使用结合雌激素(CEE)与≥65 岁女性的区域性脑容量减小相关;然而,目前尚不清楚这是否会导致广泛的特征性影响模式。在一项平均持续 6.6 年、起始年龄为 65-80 岁的 CEE 治疗随机临床试验中,513 名女性接受了结构性磁共振成像检查,以评估正常组织和缺血性病变的区域性体积。采用一种基于机器学习技术的多元模式分析方法,该方法结合了随机森林和逻辑回归以及 L(1)罚分,用于识别与治疗相关的区域性体积模式,以及这些模式是否可以区分治疗组。多元模式分析检测到 CEE 治疗组女性的边缘和颞叶区域的正常组织体积较小。左内嗅皮层和左旁海马皮层的平均下降幅度高达 7%,5%,超过了先前在额叶和海马体中报告的效应大小。然而,基于这些模式的分类总体准确率预计仅为 54.5%。平均持续 6.6 年的 CEE 治疗处方与较低的区域性脑容量相关,但它不会引起脑容量变化的特征性空间模式,这种模式的变化幅度不足以区分使用者和非使用者。

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