Magnetic resonance imaging in the diagnosis of white matter signal abnormalities

Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a str...

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Veröffentlicht in:The neuroradiology journal 2018-08, Vol.31 (4), p.362-371
Hauptverfasser: Datar, Ravi, Prasad, Asuri Narayan, Tay, Keng Yeow, Rupar, Charles Anthony, Ohorodnyk, Pavlo, Miller, Michael, Prasad, Chitra
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container_end_page 371
container_issue 4
container_start_page 362
container_title The neuroradiology journal
container_volume 31
creator Datar, Ravi
Prasad, Asuri Narayan
Tay, Keng Yeow
Rupar, Charles Anthony
Ohorodnyk, Pavlo
Miller, Michael
Prasad, Chitra
description Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen’s kappa statistic for inter-radiologist agreement was 0.733, suggesting “good” agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach.
doi_str_mv 10.1177/1971400918764016
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During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen’s kappa statistic for inter-radiologist agreement was 0.733, suggesting “good” agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. 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During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen’s kappa statistic for inter-radiologist agreement was 0.733, suggesting “good” agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Brain - diagnostic imaging</subject><subject>Brain Diseases - diagnostic imaging</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Diagnosis, Differential</subject><subject>Female</subject><subject>General Neuroimaging</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Infant</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Myelin Sheath</subject><subject>Pilot Projects</subject><subject>Retrospective Studies</subject><subject>Sensitivity and Specificity</subject><subject>White Matter - diagnostic imaging</subject><subject>Young Adult</subject><issn>1971-4009</issn><issn>2385-1996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kM9LwzAUx4MobujuniT_QDWvSdPkIsjwF0z0oOeQJmmX0aYj6RT_ezumQwXf5R2-P97jg9AZkAuAsrwEWQIjRIIoOSPAD9A0p6LIQEp-iKZbOdvqEzRLaUXGoUIWTByjSS4LKBkRU_T8qJvgBm9wdKkPOhiHfacbHxrsAx6WDls_WvrkE-5r_L70g8OdHgYXcfJN0C3WVehjp1s_eJdO0VGt2-RmX_sEvd7evMzvs8XT3cP8epEZRtmQlVbwghesqiUTROYVADGgKa1zaawtuHVFTYUBI6zIS8pYnTNLWU0Yp7Kq6Am62vWuN1XnrHFhiLpV6zh-Hz9Ur736rQS_VE3_pjgAMMrHArIrMLFPKbp6nwWitoDVX8Bj5PznzX3gG-doyHaGpBunVv0mjnzS_4Wf26SEAA</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Datar, Ravi</creator><creator>Prasad, Asuri Narayan</creator><creator>Tay, Keng Yeow</creator><creator>Rupar, Charles Anthony</creator><creator>Ohorodnyk, Pavlo</creator><creator>Miller, Michael</creator><creator>Prasad, Chitra</creator><general>SAGE Publications</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>5PM</scope></search><sort><creationdate>20180801</creationdate><title>Magnetic resonance imaging in the diagnosis of white matter signal abnormalities</title><author>Datar, Ravi ; Prasad, Asuri Narayan ; Tay, Keng Yeow ; Rupar, Charles Anthony ; Ohorodnyk, Pavlo ; Miller, Michael ; Prasad, Chitra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c434t-7d865654bf948092b110c1a33f29cdd56de5f38c1c8d827344f24d34f04639bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Brain - diagnostic imaging</topic><topic>Brain Diseases - diagnostic imaging</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Diagnosis, Differential</topic><topic>Female</topic><topic>General Neuroimaging</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Infant</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Myelin Sheath</topic><topic>Pilot Projects</topic><topic>Retrospective Studies</topic><topic>Sensitivity and Specificity</topic><topic>White Matter - diagnostic imaging</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Datar, Ravi</creatorcontrib><creatorcontrib>Prasad, Asuri Narayan</creatorcontrib><creatorcontrib>Tay, Keng Yeow</creatorcontrib><creatorcontrib>Rupar, Charles Anthony</creatorcontrib><creatorcontrib>Ohorodnyk, Pavlo</creatorcontrib><creatorcontrib>Miller, Michael</creatorcontrib><creatorcontrib>Prasad, Chitra</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The neuroradiology journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Datar, Ravi</au><au>Prasad, Asuri Narayan</au><au>Tay, Keng Yeow</au><au>Rupar, Charles Anthony</au><au>Ohorodnyk, Pavlo</au><au>Miller, Michael</au><au>Prasad, Chitra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Magnetic resonance imaging in the diagnosis of white matter signal abnormalities</atitle><jtitle>The neuroradiology journal</jtitle><addtitle>Neuroradiol J</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>31</volume><issue>4</issue><spage>362</spage><epage>371</epage><pages>362-371</pages><issn>1971-4009</issn><eissn>2385-1996</eissn><abstract>Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen’s kappa statistic for inter-radiologist agreement was 0.733, suggesting “good” agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>29517408</pmid><doi>10.1177/1971400918764016</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record>
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ispartof The neuroradiology journal, 2018-08, Vol.31 (4), p.362-371
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subjects Adolescent
Adult
Aged
Algorithms
Brain - diagnostic imaging
Brain Diseases - diagnostic imaging
Child
Child, Preschool
Diagnosis, Differential
Female
General Neuroimaging
Humans
Image Interpretation, Computer-Assisted - methods
Infant
Magnetic Resonance Imaging - methods
Male
Middle Aged
Myelin Sheath
Pilot Projects
Retrospective Studies
Sensitivity and Specificity
White Matter - diagnostic imaging
Young Adult
title Magnetic resonance imaging in the diagnosis of white matter signal abnormalities
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