Machine learning diagnostic model for amyotrophic lateral sclerosis analysis using MRI-derived features.
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...
| Publicado en: | Neuroradiology Vol. 67; no. 10; pp. 2803 - 2813 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189357973&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189357973 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Oct2025 vid: 67 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189357973 187187531 189357973 189357973 10.1007/s00234-025-03732-9 189357973 ppf: 2803 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning diagnostic model for amyotrophic lateral sclerosis analysis using MRI-derived features. aug: au: Gil Chong, Pablo Mazon, Miguel Cerdá-Alberich, Leonor Beser Robles, Maria Carot, José Miguel Vázquez-Costa, Juan Francisco Martí-Bonmatí, Luis affil: https://ror.org/01460j859 Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain sug: subj: Machine Learning Algorithms Amyotrophic Lateral Sclerosis Diagnosis Magnetic Resonance Imaging Image Interpretation, Computer Assisted Methods Radiomics Methods Diagnosis, Neurologic Amyotrophic Lateral Sclerosis Physiopathology Human Male Female Adult Middle Age Aged Logistic Regression Random Forest Biological Markers Neurodegenerative Diseases Diagnosis Critical Thinking Ensemble Learning Iron Prospective Studies Imaging, Three-Dimensional Descriptive Statistics Comparative Studies Data Analysis Software Sensitivity and Specificity Frontal Lobe Models, Statistical Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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