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      Development of an Automated MRI-Based Diagnostic Protocol for Amyotrophic Lateral Sclerosis Using Disease-Specific Pathognomonic Features: A Quantitative Disease-State Classification Study

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      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          Background

          Despite significant advances in quantitative neuroimaging, the diagnosis of ALS remains clinical and MRI-based biomarkers are not currently used to aid the diagnosis. The objective of this study is to develop a robust, disease-specific, multimodal classification protocol and validate its diagnostic accuracy in independent, early-stage and follow-up data sets.

          Methods

          147 participants (81 ALS patients and 66 healthy controls) were divided into a training sample and a validation sample. Patients in the validation sample underwent follow-up imaging longitudinally. After removing age-related variability, indices of grey and white matter integrity in ALS-specific pathognomonic brain regions were included in a cross-validated binary logistic regression model to determine the probability of individual scans indicating ALS. The following anatomical regions were assessed for diagnostic classification: average grey matter density of the left and right precentral gyrus, the average fractional anisotropy and radial diffusivity of the left and right superior corona radiata, inferior corona radiata, internal capsule, mesencephalic crus of the cerebral peduncles, pontine segment of the corticospinal tract, and the average diffusivity values of the genu, corpus and splenium of the corpus callosum.

          Results

          Using a 50% probability cut-off value of suffering from ALS, the model was able to discriminate ALS patients and HC with good sensitivity (80.0%) and moderate accuracy (70.0%) in the training sample and superior sensitivity (85.7%) and accuracy (78.4%) in the independent validation sample.

          Conclusions

          This diagnostic classification study endeavours to advance ALS biomarker research towards pragmatic clinical applications by providing an approach of automated individual-data interpretation based on group-level observations.

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          Most cited references23

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          Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review.

          Graziella Orrù, William Pettersson-Yeo, Andre F. Marquand … (2012)
          Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions. Copyright © 2012 Elsevier Ltd. All rights reserved.
          Article 0 comments Cited 342 times – based on 0 reviews     Review now
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            Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter.

            Katrin Amunts, Xin Li, Jiangyang Zhang … (2008)
            Structural delineation and assignment are the fundamental steps in understanding the anatomy of the human brain. The white matter has been structurally defined in the past only at its core regions (deep white matter). However, the most peripheral white matter areas, which are interleaved between the cortex and the deep white matter, have lacked clear anatomical definitions and parcellations. We used axonal fiber alignment information from diffusion tensor imaging (DTI) to delineate the peripheral white matter, and investigated its relationship with the cortex and the deep white matter. Using DTI data from 81 healthy subjects, we identified nine common, blade-like anatomical regions, which were further parcellated into 21 subregions based on the cortical anatomy. Four short association fiber tracts connecting adjacent gyri (U-fibers) were also identified reproducibly among the healthy population. We anticipate that this atlas will be useful resource for atlas-based white matter anatomical studies.
            Article 0 comments Cited 178 times – based on 0 reviews     Review now
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              Machine learning and radiology.

              Shijun Wang, Ronald Summers (2012)
              In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.
              Article 0 comments Cited 161 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Kewei Chen: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 1 December 2016
                Publication date Collection: 2016
                Volume: 11
                Issue: 12
                Electronic Location Identifier: e0167331
                Affiliations
                [001]Quantitative Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Ireland
                Banner Alzheimer's Institute, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: CS OH PB.

                • Data curation: CS PB.

                • Formal analysis: CS PB.

                • Funding acquisition: OH PB.

                • Investigation: CS OH PB.

                • Methodology: CS OH PB.

                • Project administration: OH PB.

                • Resources: OH PB.

                • Software: CS PB.

                • Supervision: OH PB.

                • Validation: CS OH PB.

                • Visualization: CS PB.

                • Writing – original draft: CS PB.

                • Writing – review & editing: CS OH PB.

                Article
                Publisher ID: PONE-D-16-22889
                DOI: 10.1371/journal.pone.0167331
                PMC ID: 5132189
                PubMed ID: 27907080
                SO-VID: 08646753-8c2c-48b8-bccc-2a5744d78694
                Copyright © © 2016 Schuster et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 7 June 2016
                Date accepted : 12 November 2016
                Page count
                Figures: 5, Tables: 5, Pages: 15
                Funding
                Funded by: Irish Institute of Clinical Neuroscience (IICN)—Novartis Ireland Research Grant
                Award Recipient :
                Funded by: The Iris O'Brien Foundation Ireland
                Funded by: The Perrigo Clinician-Scientist Research Fellowship
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001590, Health Research Board;
                Funded by: Research Motor Neuron (RMN-Ireland) foundation
                Funded by: EU-Joint Programme for Neurodegeneration (JPND) SOPHIA
                This work was supported by the Irish Institute of Clinical Neuroscience (IICN)—Novartis Ireland Research Grant, The Iris O'Brien Foundation, The Perrigo Clinician-Scientist Research Fellowship, the Health Research Board and the Research Motor Neuron (RMN-Ireland) foundation. Professor Hardiman’s research group has also received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° [259867] (EUROMOTOR), the EU-Joint Programme for Neurodegeneration (JPND) SOPHIA project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Medicine and Health Sciences
                Subject: Neurology
                Subject: Neurodegenerative Diseases
                Subject: Motor Neuron Diseases
                Subject: Amyotrophic Lateral Sclerosis
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Nervous System
                Subject: Central Nervous System
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Nervous System
                Subject: Central Nervous System
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Brain
                Subject: Corpus Callosum
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Brain
                Subject: Corpus Callosum
                Subject: Biology and Life Sciences
                Subject: Biochemistry
                Subject: Biomarkers
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Brain Mapping
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Custom metadata
                Data Availability While our local data management policies and ethics approval prohibit the authors from making the minimal data set publicly available, we confirm that data will be available upon request to all interested researchers by contacting the corresponding author.

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