Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning

Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease pro...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one 2023-05, Vol.18 (5), p.e0285422-e0285422
Hauptverfasser: Lupi, Amalia, Spolaor, Simone, Favero, Alessandro, Bello, Luca, Stramare, Roberto, Pegoraro, Elena, Nobile, Marco Salvatore
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page e0285422
container_issue 5
container_start_page e0285422
container_title PloS one
container_volume 18
creator Lupi, Amalia
Spolaor, Simone
Favero, Alessandro
Bello, Luca
Stramare, Roberto
Pegoraro, Elena
Nobile, Marco Salvatore
description Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N). MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation. Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1. Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.
doi_str_mv 10.1371/journal.pone.0285422
format Article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2811188952</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A748501453</galeid><doaj_id>oai_doaj_org_article_d12997091eb74309bf6d538adccbf664</doaj_id><sourcerecordid>A748501453</sourcerecordid><originalsourceid>FETCH-LOGICAL-c642t-369c913513a54f7c66a413fffefc325223ca4cc0c5d3c788e9f89e095619a98a3</originalsourceid><addsrcrecordid>eNqNk11v0zAUhiMEYmPwDxBEQkJw0RLbcRJfoWnio9KmSWxwa506J2kq1y7-mBi_HpdmU4N2gXwR6-R5X9uvfbLsJSnmhNXkw9pGZ0DPt9bgvKANLyl9lB0TweisogV7fDA_yp55vy4KzpqqepodJT3nVUmOs_VF9EpjvoHeYBhU7tBbA0ZhrlbgQAV0w28IgzW57fKr68UFyUNcRg0uh7532ENI8lu7hbC6zaMfTJ9H4-MW3c3gsc01gjOp-jx70oH2-GL8nmTfP3-6Pvs6O7_8sjg7PZ-pqqRhxiqhBGGcMOBlV6uqgpKwruuwU4xySpmCUqlC8ZapumlQdI3AQvCKCBANsJPs9d53q62XY0xe0oYQ0jSC00Qs9kRrYS23btiAu5UWBvm3YF0vwaUwNMqWUCHqQhBc1iUrxLKr2pQitEqlaVUmr4_janG5wVahCQ70xHT6xwwr2dsbSQqS9HWTHN6NDs7-jOiD3AxeodZg0Mb9xnklarpD3_yDPny8keohnWAwnU0Lq52pPK3Lhhek5CxR8weoNFrcDCo9qm5I9Yng_USQmIC_Qg_Re7m4-vb_7OWPKfv2gF0h6LDyVsfdm_NTsNyDylnvHXb3KZNC7nriLg256wk59kSSvTq8oXvRXROwP-1uB7c</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2811188952</pqid></control><display><type>article</type><title>Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Lupi, Amalia ; Spolaor, Simone ; Favero, Alessandro ; Bello, Luca ; Stramare, Roberto ; Pegoraro, Elena ; Nobile, Marco Salvatore</creator><contributor>Asakura, Atsushi</contributor><creatorcontrib>Lupi, Amalia ; Spolaor, Simone ; Favero, Alessandro ; Bello, Luca ; Stramare, Roberto ; Pegoraro, Elena ; Nobile, Marco Salvatore ; Asakura, Atsushi</creatorcontrib><description>Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N). MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation. Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1. Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0285422</identifier><identifier>PMID: 37155641</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aggregates ; Analysis ; Assessments ; Biology and Life Sciences ; Clustering ; Congenital diseases ; Datasets ; Diagnosis ; Edema ; Edema - diagnostic imaging ; Edema - pathology ; Evaluation ; Humans ; Machine learning ; Magnetic resonance ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Magnetic Resonance Spectroscopy ; Medical imaging ; Medicine and Health Sciences ; Muscle diseases ; Muscle, Skeletal - diagnostic imaging ; Muscle, Skeletal - pathology ; Muscles ; Musculoskeletal system ; Mutation ; Myopathies, Structural, Congenital - diagnostic imaging ; Myopathies, Structural, Congenital - genetics ; Myopathy ; Neoplasm Proteins ; Neural networks ; Patients ; People and Places ; Radiomics ; Research and Analysis Methods ; Self organizing maps ; Skeletal muscle ; STIM1 protein ; Stromal Interaction Molecule 1 - genetics ; Unsupervised learning ; Unsupervised Machine Learning</subject><ispartof>PloS one, 2023-05, Vol.18 (5), p.e0285422-e0285422</ispartof><rights>Copyright: © 2023 Lupi et al. 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.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Lupi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Lupi et al 2023 Lupi et al</rights><rights>2023 Lupi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c642t-369c913513a54f7c66a413fffefc325223ca4cc0c5d3c788e9f89e095619a98a3</cites><orcidid>0000-0002-0743-9214 ; 0000-0002-3383-367X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166478/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166478/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37155641$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Asakura, Atsushi</contributor><creatorcontrib>Lupi, Amalia</creatorcontrib><creatorcontrib>Spolaor, Simone</creatorcontrib><creatorcontrib>Favero, Alessandro</creatorcontrib><creatorcontrib>Bello, Luca</creatorcontrib><creatorcontrib>Stramare, Roberto</creatorcontrib><creatorcontrib>Pegoraro, Elena</creatorcontrib><creatorcontrib>Nobile, Marco Salvatore</creatorcontrib><title>Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N). MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation. Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1. Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.</description><subject>Aggregates</subject><subject>Analysis</subject><subject>Assessments</subject><subject>Biology and Life Sciences</subject><subject>Clustering</subject><subject>Congenital diseases</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Edema</subject><subject>Edema - diagnostic imaging</subject><subject>Edema - pathology</subject><subject>Evaluation</subject><subject>Humans</subject><subject>Machine learning</subject><subject>Magnetic resonance</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Magnetic Resonance Spectroscopy</subject><subject>Medical imaging</subject><subject>Medicine and Health Sciences</subject><subject>Muscle diseases</subject><subject>Muscle, Skeletal - diagnostic imaging</subject><subject>Muscle, Skeletal - pathology</subject><subject>Muscles</subject><subject>Musculoskeletal system</subject><subject>Mutation</subject><subject>Myopathies, Structural, Congenital - diagnostic imaging</subject><subject>Myopathies, Structural, Congenital - genetics</subject><subject>Myopathy</subject><subject>Neoplasm Proteins</subject><subject>Neural networks</subject><subject>Patients</subject><subject>People and Places</subject><subject>Radiomics</subject><subject>Research and Analysis Methods</subject><subject>Self organizing maps</subject><subject>Skeletal muscle</subject><subject>STIM1 protein</subject><subject>Stromal Interaction Molecule 1 - genetics</subject><subject>Unsupervised learning</subject><subject>Unsupervised Machine Learning</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk11v0zAUhiMEYmPwDxBEQkJw0RLbcRJfoWnio9KmSWxwa506J2kq1y7-mBi_HpdmU4N2gXwR6-R5X9uvfbLsJSnmhNXkw9pGZ0DPt9bgvKANLyl9lB0TweisogV7fDA_yp55vy4KzpqqepodJT3nVUmOs_VF9EpjvoHeYBhU7tBbA0ZhrlbgQAV0w28IgzW57fKr68UFyUNcRg0uh7532ENI8lu7hbC6zaMfTJ9H4-MW3c3gsc01gjOp-jx70oH2-GL8nmTfP3-6Pvs6O7_8sjg7PZ-pqqRhxiqhBGGcMOBlV6uqgpKwruuwU4xySpmCUqlC8ZapumlQdI3AQvCKCBANsJPs9d53q62XY0xe0oYQ0jSC00Qs9kRrYS23btiAu5UWBvm3YF0vwaUwNMqWUCHqQhBc1iUrxLKr2pQitEqlaVUmr4_janG5wVahCQ70xHT6xwwr2dsbSQqS9HWTHN6NDs7-jOiD3AxeodZg0Mb9xnklarpD3_yDPny8keohnWAwnU0Lq52pPK3Lhhek5CxR8weoNFrcDCo9qm5I9Yng_USQmIC_Qg_Re7m4-vb_7OWPKfv2gF0h6LDyVsfdm_NTsNyDylnvHXb3KZNC7nriLg256wk59kSSvTq8oXvRXROwP-1uB7c</recordid><startdate>20230508</startdate><enddate>20230508</enddate><creator>Lupi, Amalia</creator><creator>Spolaor, Simone</creator><creator>Favero, Alessandro</creator><creator>Bello, Luca</creator><creator>Stramare, Roberto</creator><creator>Pegoraro, Elena</creator><creator>Nobile, Marco Salvatore</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0743-9214</orcidid><orcidid>https://orcid.org/0000-0002-3383-367X</orcidid></search><sort><creationdate>20230508</creationdate><title>Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning</title><author>Lupi, Amalia ; Spolaor, Simone ; Favero, Alessandro ; Bello, Luca ; Stramare, Roberto ; Pegoraro, Elena ; Nobile, Marco Salvatore</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c642t-369c913513a54f7c66a413fffefc325223ca4cc0c5d3c788e9f89e095619a98a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Aggregates</topic><topic>Analysis</topic><topic>Assessments</topic><topic>Biology and Life Sciences</topic><topic>Clustering</topic><topic>Congenital diseases</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Edema</topic><topic>Edema - diagnostic imaging</topic><topic>Edema - pathology</topic><topic>Evaluation</topic><topic>Humans</topic><topic>Machine learning</topic><topic>Magnetic resonance</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Magnetic Resonance Spectroscopy</topic><topic>Medical imaging</topic><topic>Medicine and Health Sciences</topic><topic>Muscle diseases</topic><topic>Muscle, Skeletal - diagnostic imaging</topic><topic>Muscle, Skeletal - pathology</topic><topic>Muscles</topic><topic>Musculoskeletal system</topic><topic>Mutation</topic><topic>Myopathies, Structural, Congenital - diagnostic imaging</topic><topic>Myopathies, Structural, Congenital - genetics</topic><topic>Myopathy</topic><topic>Neoplasm Proteins</topic><topic>Neural networks</topic><topic>Patients</topic><topic>People and Places</topic><topic>Radiomics</topic><topic>Research and Analysis Methods</topic><topic>Self organizing maps</topic><topic>Skeletal muscle</topic><topic>STIM1 protein</topic><topic>Stromal Interaction Molecule 1 - genetics</topic><topic>Unsupervised learning</topic><topic>Unsupervised Machine Learning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lupi, Amalia</creatorcontrib><creatorcontrib>Spolaor, Simone</creatorcontrib><creatorcontrib>Favero, Alessandro</creatorcontrib><creatorcontrib>Bello, Luca</creatorcontrib><creatorcontrib>Stramare, Roberto</creatorcontrib><creatorcontrib>Pegoraro, Elena</creatorcontrib><creatorcontrib>Nobile, Marco Salvatore</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lupi, Amalia</au><au>Spolaor, Simone</au><au>Favero, Alessandro</au><au>Bello, Luca</au><au>Stramare, Roberto</au><au>Pegoraro, Elena</au><au>Nobile, Marco Salvatore</au><au>Asakura, Atsushi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-05-08</date><risdate>2023</risdate><volume>18</volume><issue>5</issue><spage>e0285422</spage><epage>e0285422</epage><pages>e0285422-e0285422</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N). MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation. Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1. Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37155641</pmid><doi>10.1371/journal.pone.0285422</doi><tpages>e0285422</tpages><orcidid>https://orcid.org/0000-0002-0743-9214</orcidid><orcidid>https://orcid.org/0000-0002-3383-367X</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2023-05, Vol.18 (5), p.e0285422-e0285422
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_2811188952
source MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry
subjects Aggregates
Analysis
Assessments
Biology and Life Sciences
Clustering
Congenital diseases
Datasets
Diagnosis
Edema
Edema - diagnostic imaging
Edema - pathology
Evaluation
Humans
Machine learning
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Magnetic Resonance Spectroscopy
Medical imaging
Medicine and Health Sciences
Muscle diseases
Muscle, Skeletal - diagnostic imaging
Muscle, Skeletal - pathology
Muscles
Musculoskeletal system
Mutation
Myopathies, Structural, Congenital - diagnostic imaging
Myopathies, Structural, Congenital - genetics
Myopathy
Neoplasm Proteins
Neural networks
Patients
People and Places
Radiomics
Research and Analysis Methods
Self organizing maps
Skeletal muscle
STIM1 protein
Stromal Interaction Molecule 1 - genetics
Unsupervised learning
Unsupervised Machine Learning
title Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T03%3A13%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Muscle%20magnetic%20resonance%20characterization%20of%20STIM1%20tubular%20aggregate%20myopathy%20using%20unsupervised%20learning&rft.jtitle=PloS%20one&rft.au=Lupi,%20Amalia&rft.date=2023-05-08&rft.volume=18&rft.issue=5&rft.spage=e0285422&rft.epage=e0285422&rft.pages=e0285422-e0285422&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0285422&rft_dat=%3Cgale_plos_%3EA748501453%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2811188952&rft_id=info:pmid/37155641&rft_galeid=A748501453&rft_doaj_id=oai_doaj_org_article_d12997091eb74309bf6d538adccbf664&rfr_iscdi=true