Predicting regional tau accumulation with machine learning-based tau-PET and advanced radiomics

IF 4.9 Q1 CLINICAL NEUROLOGY

Alzheimer''s and Dementia: Translational Research and Clinical Interventions Pub Date : 2024-10-29 DOI:10.1002/trc2.70005

Saima Rathore, Ixavier A. Higgins, Jian Wang, Ian A. Kennedy, Leonardo Iaccarino, Samantha C. Burnham, Michael J. Pontecorvo, Sergey Shcherbinin

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引用次数: 0

Abstract

INTRODUCTION

Alzheimer's disease is partially characterized by the progressive accumulation of aggregated tau-containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir-positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.

METHODS

We quantified the disease profile of participants (N = 276) using a comprehensive set of descriptors, including clinical/demographic (age, diagnosis, amyloid status, sex, race, ethnicity), genetic (apolipoprotein E [APOE]-ε4), and flortaucipir-PET imaging measures (regional flortaucipir standardized uptake value ratio [SUVr] and comprehensive radiomic texture features extracted from Automated Anatomical Labeling template regions). We trained an AdaBoost machine learning algorithm in a 2:1 split train-test configuration to derive a prognostic index that (i) stratifies individualized brain regions including global (AD-signature region) and lobar regions (frontal, occipital, parietal, temporal) into stable/slow- and fast-progressors based on future tau accumulation, and (ii) forecasts individualized regional annualized-rate-of-change in flortaucipir-PET SUVr. Further, we developed an adaptive model incorporating flortaucipir-PET measurements from the baseline and intermediate timepoints to predict annualized-rate-of-change.

RESULTS

In binary classification for predicting stable/slow- versus fast-progressors, the area-under-the-receiver-operating-characteristic curve was 0.86 in the AD-signature region and 0.83, 0.82, 0.84, and 0.83 in frontal, occipital, parietal, and temporal regions, respectively. The trained models successfully predicted annualized-rate-of-change of flortaucipir-PET regional flortaucipir SUVr in AD-signature and lobar regions (Pearson-correlation [R]: AD-signature = 0.73; frontal = 0.73; occipital = 0.71; parietal = 0.70; temporal = 0.69). The models’ performance in predicting annualized-rate-of-change slightly increased when imaging features from intermediate timepoints were used in the adaptive setting (R: AD-signature = 0.79; frontal = 0.87; occipital = 0.83; parietal = 0.74; temporal = 0.82).

DISCUSSION

Taken together, our results propose a robust approach to predict future tau accumulation that may improve the ability to enroll, stratify, and gauge efficacy in clinical trial participants.

Highlights

Machine learning predicts the future rate of tau accumulation in Alzheimer's disease.
Tau prediction in lobar/global regions benefits from diverse multimodal features.
This prognostic index can serve as a sensitive tool for patient stratification.

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利用基于机器学习的 tau-PET 和先进的放射组学预测区域性 tau 积累

引言阿尔茨海默病的部分特征是含有神经纤维缠结的 tau 逐渐聚集。尽管积累的 tau、神经变性和认知能力下降之间的关联对于疾病的理解和临床试验的设计至关重要，但我们仍然缺乏强有力的工具来预测个体化的 tau 积累轨迹。我们的目的是评估氟替哌啶正电子发射断层扫描（PET）的脑成像生物标记物是否能结合临床和基因组学指标预测未来的病理tau累积。方法我们使用一套全面的描述因子量化了参与者（N = 276）的疾病概况，包括临床/人口学（年龄、诊断、淀粉样蛋白状态、性别、种族、民族）、基因（载脂蛋白 E [APOE]-ε4）和氟陶西弼-正电子发射计算机断层成像测量（区域氟陶西弼标准化摄取值比 [SUVr] 和从自动解剖标记模板区域提取的全面放射纹理特征）。我们以 2:1 的训练-测试分离配置训练了 AdaBoost 机器学习算法，从而得出了一种预后指数：(i) 根据未来的 tau 累积情况，将包括全脑（AD 标志区）和脑叶区（额叶、枕叶、顶叶、颞叶）在内的个性化脑区分为稳定/慢进展和快进展脑区；(ii) 预测个性化区域的氟替卡西平-PET SUVr 年化变化率。此外，我们还开发了一个自适应模型，该模型结合了基线和中间时间点的花生苷-PET 测量值来预测年变化率。结果在预测稳定/缓慢进展者和快速进展者的二元分类中，AD 标志区的接收器工作特征曲线下面积为 0.86，额叶、枕叶、顶叶和颞叶区的接收器工作特征曲线下面积分别为 0.83、0.82、0.84 和 0.83。训练模型成功预测了AD特征区和脑叶区的floraucipir-PET区域floraucipir SUVr的年化变化率（Pears-correlation [R]：AD-特征=0.73；额叶=0.73；枕叶=0.71；顶叶=0.70；颞叶=0.69）。在自适应设置中使用中间时间点的成像特征时，模型预测年化变化率的性能略有提高（R：AD-特征 = 0.79；额叶 = 0.87；枕叶 = 0.83；顶叶 = 0.74；颞叶 = 0.82）。综上所述，我们的研究结果提出了一种预测未来 tau 累积的稳健方法，可提高临床试验参与者的入组、分层和疗效评估能力。亮点机器学习预测阿尔茨海默病未来的 tau 累积率。叶状/全局区域的 Tau 预测得益于多样化的多模态特征。这一预后指数可作为对患者进行分层的灵敏工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer''s and Dementia: Translational Research and Clinical Interventions Medicine-Psychiatry and Mental Health

CiteScore

10.10

自引率

2.10%

发文量

134

审稿时长

10 weeks

期刊介绍： Alzheimer''s & Dementia: Translational Research & Clinical Interventions (TRCI) is a peer-reviewed, open access,journal from the Alzheimer''s Association®. The journal seeks to bridge the full scope of explorations between basic research on drug discovery and clinical studies, validating putative therapies for aging-related chronic brain conditions that affect cognition, motor functions, and other behavioral or clinical symptoms associated with all forms dementia and Alzheimer''s disease. The journal will publish findings from diverse domains of research and disciplines to accelerate the conversion of abstract facts into practical knowledge: specifically, to translate what is learned at the bench into bedside applications. The journal seeks to publish articles that go beyond a singular emphasis on either basic drug discovery research or clinical research. Rather, an important theme of articles will be the linkages between and among the various discrete steps in the complex continuum of therapy development. For rapid communication among a multidisciplinary research audience involving the range of therapeutic interventions, TRCI will consider only original contributions that include feature length research articles, systematic reviews, meta-analyses, brief reports, narrative reviews, commentaries, letters, perspectives, and research news that would advance wide range of interventions to ameliorate symptoms or alter the progression of chronic neurocognitive disorders such as dementia and Alzheimer''s disease. The journal will publish on topics related to medicine, geriatrics, neuroscience, neurophysiology, neurology, psychiatry, clinical psychology, bioinformatics, pharmaco-genetics, regulatory issues, health economics, pharmacoeconomics, and public health policy as these apply to preclinical and clinical research on therapeutics.