Marina C. Ruppert-Junck, Gunter Kräling, Andrea Greuel, Marc Tittgemeyer, Lars Timmermann, Alexander Drzezga, Carsten Eggers, David Pedrosa
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In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [<jats:sup>18</jats:sup>F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [<jats:sup>18</jats:sup>F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [<jats:sup>18</jats:sup>F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level\",\"authors\":\"Marina C. 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引用次数: 0
摘要
目前,帕金森病(PD)的诊断主要依靠专家判断,神经影像学检查只是辅助工具。在最近的一项研究中,我们根据[18F]-氟脱氧葡萄糖([18F]-FDG)正电子发射计算机断层扫描的分组比较,确定了包括部分黑质在内的低代谢中脑群是区分帕金森病患者的最佳代谢特征。一项独立研究表明,黑质代谢在帕金森综合征的诊断工作中具有巨大潜力。在本研究中,我们采用了一种机器学习方法来评估用 [18F]-FDG PET 测量的中脑代谢,将其作为帕金森病的诊断标志物。共有 51 名中期帕金森病患者和 16 名健康对照受试者接受了高分辨率 [18F]-FDG PET 扫描。通过组间比较确定的中脑群的归一化示踪剂更新值从个人扫描中逐个体素提取。提取的摄取值采用随机森林特征分类算法。为了测试模型在区分患者和对照组方面的稳健性,采用了一种经过调整的 "留一弃一 "交叉验证方法。通过计算验证数据集的灵敏度、特异性和模型准确性,以及测试数据集正确分类受试者的百分比,评估了模型在所有运行中的性能。在验证数据集中,基于中脑簇体素摄取值的随机森林特征分类能识别出患者,平均灵敏度为 0.91(最低:0.82,最高:0.94)。在所有 67 次运行中,每个人都被作为测试数据集处理一次,测试数据集被我们的模型正确分类。所应用的特征重要性提取方法在所有运行中都一致识别出了中脑集群中重要性最高的一个体素子集,该子集在空间上与左侧黑质趋于一致。我们的数据表明,[18F]-FDG PET 测量的中脑代谢是一种很有前景的诊断帕金森病的成像工具。鉴于其与帕金森病病理生理学的密切关系和极高的鉴别准确性,这种方法有助于客观化帕金森病的诊断,并能在临床试验中进行更准确的分类,这也适用于前驱期疾病患者。
Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level
Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [18F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.
期刊介绍:
Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions.
Also: comp neuro