预测行为变异型额颞叶痴呆的疾病进展。
Predicting disease progression in behavioral variant frontotemporal dementia.
作者信息
Anderl-Straub Sarah, Lausser Ludwig, Lombardi Jolina, Uttner Ingo, Fassbender Klaus, Fliessbach Klaus, Huppertz Hans-Jürgen, Jahn Holger, Kornhuber Johannes, Obrig Hellmuth, Schneider Anja, Semler Elisa, Synofzik Matthis, Danek Adrian, Prudlo Johannes, Kassubek Jan, Landwehrmeyer Bernhard, Lauer Martin, Volk Alexander E, Wiltfang Jens, Diehl-Schmid Janine, Ludolph Albert C, Schroeter Matthias L, Kestler Hans A, Otto Markus
机构信息
Department of Neurology University of Ulm Ulm Germany.
Institute of Medical Systems Biology University of Ulm Ulm Germany.
出版信息
Alzheimers Dement (Amst). 2021 Dec 31;13(1):e12262. doi: 10.1002/dad2.12262. eCollection 2021.
INTRODUCTION
The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline.
METHODS
One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast progressors" vs. "slow progressors"), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models.
RESULTS
We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models.
DISCUSSION
Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.
引言
额颞叶痴呆的行为变异型（bvFTD）是一种罕见的神经退行性疾病。尚未充分确定疾病进展的可靠预测指标。我们研究了多变量磁共振成像（MRI）生物标志物特征对个体衰退的预测价值。
方法
从德国额颞叶变性（FTLD）联合研究中招募了105例bvFTD患者。在定义两组（“快速进展者”与“缓慢进展者”）后，我们使用多变量分类模型进行详尽筛选，研究脑磁共振体积对疾病进展率的预测价值。
结果
我们确定了在1年内预测疾病进展率的区域。预测指标显示，在我们的50个顶级分类模型中，总体准确率为80%。这些模型中特别出现了苍白球、颞中回、额下回、扣带回、眶额中回和脑岛。
讨论
基于所揭示的标志物组合，个体预后似乎是可行的。这可用于个体化进展模型的临床研究。
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