Machine learning diagnostic model for amyotrophic lateral sclerosis analysis using MRI-derived features.

IF 2.6 3区 医学 Q2 CLINICAL NEUROLOGY
Pablo Gil Chong, Miguel Mazon, Leonor Cerdá-Alberich, Maria Beser Robles, José Miguel Carot, Juan Francisco Vázquez-Costa, Luis Martí-Bonmatí
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Abstract

Purpose: Amyotrophic Lateral Sclerosis is a devastating motor neuron disease characterized by its diagnostic difficulty. Currently, no reliable biomarkers exist in the diagnosis process. In this scenario, our purpose is the application of machine learning algorithms to imaging MRI-derived variables for the development of diagnostic models that facilitate and shorten the process.

Methods: A dataset of 211 patients (114 ALS, 45 mimic, 22 genetic carriers and 30 control) with MRI-derived features of volumetry, cortical thickness and local iron (via T2* mapping, and visual assessment of susceptibility imaging). A binary classification task approach has been taken to classify patients with and without ALS. A sequential modeling methodology, understood from an iterative improvement perspective, has been followed, analyzing each group's performance separately to adequately improve modelling. Feature filtering techniques, dimensionality reduction techniques (PCA, kernel PCA), oversampling techniques (SMOTE, ADASYN) and classification techniques (logistic regression, LASSO, Ridge, ElasticNet, Support Vector Classifier, K-neighbors, random forest) were included. Three subsets of available data have been used for each proposed architecture: a subset containing automatic retrieval MRI-derived data, a subset containing the variables from the visual analysis of the susceptibility imaging and a subset containing all features.

Results: The best results have been attained with all the available data through a voting classifier composed of five different classifiers: accuracy = 0.896, AUC = 0.929, sensitivity = 0.886, specificity = 0.929.

Conclusion: These results confirm the potential of ML techniques applied to imaging variables of volumetry, cortical thickness, and local iron for the development of diagnostic model as a clinical tool for decision-making support.

使用mri衍生特征分析肌萎缩侧索硬化的机器学习诊断模型。
目的:肌萎缩侧索硬化症是一种以诊断困难为特征的破坏性运动神经元疾病。目前,在诊断过程中还没有可靠的生物标志物。在这种情况下,我们的目的是应用机器学习算法对mri衍生变量进行成像,以开发诊断模型,从而促进和缩短该过程。方法:211例患者(114例ALS, 45例模拟,22例遗传携带者和30例对照)的数据集,其mri特征包括体积、皮质厚度和局部铁(通过T2*作图和视觉易感性成像评估)。采用二元分类任务方法对ALS患者和非ALS患者进行分类。遵循了从迭代改进的角度理解的顺序建模方法,分别分析每个组的性能以充分改进建模。包括特征滤波技术、降维技术(PCA、核PCA)、过采样技术(SMOTE、ADASYN)和分类技术(logistic回归、LASSO、Ridge、ElasticNet、支持向量分类器、k近邻、随机森林)。每个提出的架构使用了三个可用数据子集:一个子集包含自动检索mri衍生数据,一个子集包含敏感性成像的视觉分析变量,一个子集包含所有特征。结果:由5个不同分类器组成的投票分类器在所有可用数据下获得了最佳结果:准确率= 0.896,AUC = 0.929,灵敏度= 0.886,特异性= 0.929。结论:这些结果证实了ML技术应用于体积、皮质厚度和局部铁等成像变量的潜力,可以作为决策支持的临床工具开发诊断模型。
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来源期刊
Neuroradiology
Neuroradiology 医学-核医学
CiteScore
5.30
自引率
3.60%
发文量
214
审稿时长
4-8 weeks
期刊介绍: Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.
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