Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy
Background: Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study...
Gespeichert in:
Veröffentlicht in: | IEEE journal of biomedical and health informatics 2024-02, Vol.28 (2), p.835-845 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 845 |
---|---|
container_issue | 2 |
container_start_page | 835 |
container_title | IEEE journal of biomedical and health informatics |
container_volume | 28 |
creator | Yan, Dong Li, Qiang Lin, Chia-Wei Shieh, Jeng-Yi Weng, Wen-Chin Tsui, Po-Hsiang |
description | Background: Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD. Methods: Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC). Results: Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 ( p < 0.001 compared with conventional radiomics analysis). Conclusions: The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment. |
doi_str_mv | 10.1109/JBHI.2023.3330578 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_pubmed_primary_37930927</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10310097</ieee_id><sourcerecordid>2886937272</sourcerecordid><originalsourceid>FETCH-LOGICAL-c350t-bec9c0e70b400e9b4533c0174bd49cf145ead640e5e8b8a73799fb444cc122943</originalsourceid><addsrcrecordid>eNpdkVtrFDEYhoMottT-AEEk4I03s-Y0OxPv1tZ2VyriYa9DJvNNmzKTrDkI8y_8yc2yWxVzkxCe9_0SHoReUrKglMh3nz6sNwtGGF9wzkndtE_QKaPLtmKMtE8fz1SKE3Qe4z0pqy1XcvkcnfBGciJZc4p-r-cu2B5_3X7H33Rv_WRNfI9X-HMek518r8dq4xLcBp2gYFm7ZJNO9hfg7ZiCjj67_m8UDz7gVYwQo3W3eDV1edTJhxlfZWeS9Q5bhy-zuQPnoEyJpgABX84xBb-7m1-gZ4MeI5wf9zO0vfr442Jd3Xy53lysbirDa5KqDow0BBrSCUJAdqLm3BDaiK4X0gxU1KD7pSBQQ9u1uik_lkMnhDCGMiYFP0NvD7274H9miElNNhoYR-3A56hY2y4lb1jDCvrmP_Te5-DK6xSTjFPGGa0LRQ-UCT7GAIPaBTvpMCtK1N6Y2htTe2PqaKxkXh-bczdB_yfx6KcArw6ABYB_CjklRDb8AcLqm5o</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2923123215</pqid></control><display><type>article</type><title>Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy</title><source>IEEE Electronic Library (IEL)</source><creator>Yan, Dong ; Li, Qiang ; Lin, Chia-Wei ; Shieh, Jeng-Yi ; Weng, Wen-Chin ; Tsui, Po-Hsiang</creator><creatorcontrib>Yan, Dong ; Li, Qiang ; Lin, Chia-Wei ; Shieh, Jeng-Yi ; Weng, Wen-Chin ; Tsui, Po-Hsiang</creatorcontrib><description>Background: Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD. Methods: Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC). Results: Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 ( p < 0.001 compared with conventional radiomics analysis). Conclusions: The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment.</description><identifier>ISSN: 2168-2194</identifier><identifier>ISSN: 2168-2208</identifier><identifier>EISSN: 2168-2208</identifier><identifier>DOI: 10.1109/JBHI.2023.3330578</identifier><identifier>PMID: 37930927</identifier><identifier>CODEN: IJBHA9</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Duchenne muscular dystrophy ; Duchenne's muscular dystrophy ; Dystrophy ; Entropy ; Entropy (Information theory) ; envelope statistics ; Feature extraction ; Hospitals ; Humans ; Image classification ; Image contrast ; Imaging ; Medical imaging ; Muscle, Skeletal - diagnostic imaging ; Muscular dystrophy ; Muscular Dystrophy, Duchenne - diagnostic imaging ; Nakagami distribution ; parametric imaging ; Quantitative ultrasound ; Radiomics ; ROC Curve ; Statistical analysis ; Support vector machines ; Ultrasonic imaging ; Ultrasonography - methods ; Ultrasound</subject><ispartof>IEEE journal of biomedical and health informatics, 2024-02, Vol.28 (2), p.835-845</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c350t-bec9c0e70b400e9b4533c0174bd49cf145ead640e5e8b8a73799fb444cc122943</citedby><cites>FETCH-LOGICAL-c350t-bec9c0e70b400e9b4533c0174bd49cf145ead640e5e8b8a73799fb444cc122943</cites><orcidid>0000-0001-7129-1456 ; 0000-0002-6577-0116 ; 0000-0002-3906-4269 ; 0000-0002-6923-9138 ; 0000-0002-5604-1800 ; 0000-0002-2654-3324</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10310097$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10310097$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37930927$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yan, Dong</creatorcontrib><creatorcontrib>Li, Qiang</creatorcontrib><creatorcontrib>Lin, Chia-Wei</creatorcontrib><creatorcontrib>Shieh, Jeng-Yi</creatorcontrib><creatorcontrib>Weng, Wen-Chin</creatorcontrib><creatorcontrib>Tsui, Po-Hsiang</creatorcontrib><title>Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy</title><title>IEEE journal of biomedical and health informatics</title><addtitle>JBHI</addtitle><addtitle>IEEE J Biomed Health Inform</addtitle><description>Background: Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD. Methods: Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC). Results: Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 ( p < 0.001 compared with conventional radiomics analysis). Conclusions: The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment.</description><subject>Duchenne muscular dystrophy</subject><subject>Duchenne's muscular dystrophy</subject><subject>Dystrophy</subject><subject>Entropy</subject><subject>Entropy (Information theory)</subject><subject>envelope statistics</subject><subject>Feature extraction</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Image classification</subject><subject>Image contrast</subject><subject>Imaging</subject><subject>Medical imaging</subject><subject>Muscle, Skeletal - diagnostic imaging</subject><subject>Muscular dystrophy</subject><subject>Muscular Dystrophy, Duchenne - diagnostic imaging</subject><subject>Nakagami distribution</subject><subject>parametric imaging</subject><subject>Quantitative ultrasound</subject><subject>Radiomics</subject><subject>ROC Curve</subject><subject>Statistical analysis</subject><subject>Support vector machines</subject><subject>Ultrasonic imaging</subject><subject>Ultrasonography - methods</subject><subject>Ultrasound</subject><issn>2168-2194</issn><issn>2168-2208</issn><issn>2168-2208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkVtrFDEYhoMottT-AEEk4I03s-Y0OxPv1tZ2VyriYa9DJvNNmzKTrDkI8y_8yc2yWxVzkxCe9_0SHoReUrKglMh3nz6sNwtGGF9wzkndtE_QKaPLtmKMtE8fz1SKE3Qe4z0pqy1XcvkcnfBGciJZc4p-r-cu2B5_3X7H33Rv_WRNfI9X-HMek518r8dq4xLcBp2gYFm7ZJNO9hfg7ZiCjj67_m8UDz7gVYwQo3W3eDV1edTJhxlfZWeS9Q5bhy-zuQPnoEyJpgABX84xBb-7m1-gZ4MeI5wf9zO0vfr442Jd3Xy53lysbirDa5KqDow0BBrSCUJAdqLm3BDaiK4X0gxU1KD7pSBQQ9u1uik_lkMnhDCGMiYFP0NvD7274H9miElNNhoYR-3A56hY2y4lb1jDCvrmP_Te5-DK6xSTjFPGGa0LRQ-UCT7GAIPaBTvpMCtK1N6Y2htTe2PqaKxkXh-bczdB_yfx6KcArw6ABYB_CjklRDb8AcLqm5o</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Yan, Dong</creator><creator>Li, Qiang</creator><creator>Lin, Chia-Wei</creator><creator>Shieh, Jeng-Yi</creator><creator>Weng, Wen-Chin</creator><creator>Tsui, Po-Hsiang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7129-1456</orcidid><orcidid>https://orcid.org/0000-0002-6577-0116</orcidid><orcidid>https://orcid.org/0000-0002-3906-4269</orcidid><orcidid>https://orcid.org/0000-0002-6923-9138</orcidid><orcidid>https://orcid.org/0000-0002-5604-1800</orcidid><orcidid>https://orcid.org/0000-0002-2654-3324</orcidid></search><sort><creationdate>20240201</creationdate><title>Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy</title><author>Yan, Dong ; Li, Qiang ; Lin, Chia-Wei ; Shieh, Jeng-Yi ; Weng, Wen-Chin ; Tsui, Po-Hsiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c350t-bec9c0e70b400e9b4533c0174bd49cf145ead640e5e8b8a73799fb444cc122943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Duchenne muscular dystrophy</topic><topic>Duchenne's muscular dystrophy</topic><topic>Dystrophy</topic><topic>Entropy</topic><topic>Entropy (Information theory)</topic><topic>envelope statistics</topic><topic>Feature extraction</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Image classification</topic><topic>Image contrast</topic><topic>Imaging</topic><topic>Medical imaging</topic><topic>Muscle, Skeletal - diagnostic imaging</topic><topic>Muscular dystrophy</topic><topic>Muscular Dystrophy, Duchenne - diagnostic imaging</topic><topic>Nakagami distribution</topic><topic>parametric imaging</topic><topic>Quantitative ultrasound</topic><topic>Radiomics</topic><topic>ROC Curve</topic><topic>Statistical analysis</topic><topic>Support vector machines</topic><topic>Ultrasonic imaging</topic><topic>Ultrasonography - methods</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yan, Dong</creatorcontrib><creatorcontrib>Li, Qiang</creatorcontrib><creatorcontrib>Lin, Chia-Wei</creatorcontrib><creatorcontrib>Shieh, Jeng-Yi</creatorcontrib><creatorcontrib>Weng, Wen-Chin</creatorcontrib><creatorcontrib>Tsui, Po-Hsiang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE journal of biomedical and health informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yan, Dong</au><au>Li, Qiang</au><au>Lin, Chia-Wei</au><au>Shieh, Jeng-Yi</au><au>Weng, Wen-Chin</au><au>Tsui, Po-Hsiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy</atitle><jtitle>IEEE journal of biomedical and health informatics</jtitle><stitle>JBHI</stitle><addtitle>IEEE J Biomed Health Inform</addtitle><date>2024-02-01</date><risdate>2024</risdate><volume>28</volume><issue>2</issue><spage>835</spage><epage>845</epage><pages>835-845</pages><issn>2168-2194</issn><issn>2168-2208</issn><eissn>2168-2208</eissn><coden>IJBHA9</coden><abstract>Background: Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD. Methods: Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC). Results: Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 ( p < 0.001 compared with conventional radiomics analysis). Conclusions: The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>37930927</pmid><doi>10.1109/JBHI.2023.3330578</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-7129-1456</orcidid><orcidid>https://orcid.org/0000-0002-6577-0116</orcidid><orcidid>https://orcid.org/0000-0002-3906-4269</orcidid><orcidid>https://orcid.org/0000-0002-6923-9138</orcidid><orcidid>https://orcid.org/0000-0002-5604-1800</orcidid><orcidid>https://orcid.org/0000-0002-2654-3324</orcidid></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 2168-2194 |
ispartof | IEEE journal of biomedical and health informatics, 2024-02, Vol.28 (2), p.835-845 |
issn | 2168-2194 2168-2208 2168-2208 |
language | eng |
recordid | cdi_pubmed_primary_37930927 |
source | IEEE Electronic Library (IEL) |
subjects | Duchenne muscular dystrophy Duchenne's muscular dystrophy Dystrophy Entropy Entropy (Information theory) envelope statistics Feature extraction Hospitals Humans Image classification Image contrast Imaging Medical imaging Muscle, Skeletal - diagnostic imaging Muscular dystrophy Muscular Dystrophy, Duchenne - diagnostic imaging Nakagami distribution parametric imaging Quantitative ultrasound Radiomics ROC Curve Statistical analysis Support vector machines Ultrasonic imaging Ultrasonography - methods Ultrasound |
title | Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T22%3A51%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Hybrid%20QUS%20Radiomics:%20A%20Multimodal-Integrated%20Quantitative%20Ultrasound%20Radiomics%20for%20Assessing%20Ambulatory%20Function%20in%20Duchenne%20Muscular%20Dystrophy&rft.jtitle=IEEE%20journal%20of%20biomedical%20and%20health%20informatics&rft.au=Yan,%20Dong&rft.date=2024-02-01&rft.volume=28&rft.issue=2&rft.spage=835&rft.epage=845&rft.pages=835-845&rft.issn=2168-2194&rft.eissn=2168-2208&rft.coden=IJBHA9&rft_id=info:doi/10.1109/JBHI.2023.3330578&rft_dat=%3Cproquest_RIE%3E2886937272%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2923123215&rft_id=info:pmid/37930927&rft_ieee_id=10310097&rfr_iscdi=true |