Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness
This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. We found that several monofract...
Gespeichert in:
| Veröffentlicht in: | Biomarkers in medicine 2021-08, Vol.15 (12), p.929-940 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 940 |
|---|---|
| container_issue | 12 |
| container_start_page | 929 |
| container_title | Biomarkers in medicine |
| container_volume | 15 |
| creator | Djuričić, Goran J Rajković, Nemanja Milošević, Nebojša Sopta, Jelena P Borić, Igor Dučić, Siniša Apostolović, Milan Radulovic, Marko |
| description | This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs.
Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy.
We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ′(G) provided the best predictive association (area under the ROC curve = 0.88; p |
| doi_str_mv | 10.2217/bmm-2020-0876 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2549690362</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2549690362</sourcerecordid><originalsourceid>FETCH-LOGICAL-c382t-268bc17be73bb424c1deb1fc4e59237e65497bb471eb47891e5857dfff1c0cfb3</originalsourceid><addsrcrecordid>eNp1kE1LxDAQhoMo7rp69Co9eqnmo23aoyzqLqwIoufQphOMNE3ttML-e1O67s3LTGby8MI8hFwzesc5k_eVczGnnMY0l9kJWTKZ8pgKyU6P70wsyAXiF6WplBk_JwuRcJFxUSzJZu1dNw7lYH1bNlEZyh4tRt5EL29bjLoeaquHsMABPJa99q6M9Cc43wN2vkX7Ay0gXpIzUzYIV4e-Ih9Pj-_rTbx7fd6uH3axFjkfYp7llWayAimqKuGJZjVUzOgE0oILCVmaFDL8SAah5AWDNE9lbYxhmmpTiRW5nXO73n-PgINyFjU0TdmCH1HxEJAVdDpvReIZ1b1H7MGorreu7PeKUTXJU0GemuSpSV7gbw7RY-WgPtJ_tgJQzIAZhzGcry20GtQ8uUmUbeGf8F8yDX-a</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2549690362</pqid></control><display><type>article</type><title>Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness</title><source>PubMed Central</source><creator>Djuričić, Goran J ; Rajković, Nemanja ; Milošević, Nebojša ; Sopta, Jelena P ; Borić, Igor ; Dučić, Siniša ; Apostolović, Milan ; Radulovic, Marko</creator><creatorcontrib>Djuričić, Goran J ; Rajković, Nemanja ; Milošević, Nebojša ; Sopta, Jelena P ; Borić, Igor ; Dučić, Siniša ; Apostolović, Milan ; Radulovic, Marko</creatorcontrib><description>This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs.
Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy.
We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ′(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by Y-axis intersection of the regression line for box fractal dimension, r² for FD
and tumor circularity.
This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness.
Fractal analysis of MRI scans was shown to predict the chemosensitivity of osteosarcoma. These findings may eventually lead to improved patient survival by enabling personalized cytotoxic chemotherapy prescription.</description><identifier>ISSN: 1752-0363</identifier><identifier>EISSN: 1752-0371</identifier><identifier>DOI: 10.2217/bmm-2020-0876</identifier><identifier>PMID: 34236239</identifier><language>eng</language><publisher>England: Future Medicine Ltd</publisher><subject>cancer ; computational image analysis ; cytotoxic chemotherapy ; fractal analysis ; medical image analysis ; MRI ; osteosarcoma ; prediction ; prognosis ; tumor circularity</subject><ispartof>Biomarkers in medicine, 2021-08, Vol.15 (12), p.929-940</ispartof><rights>2021 Future Medicine Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c382t-268bc17be73bb424c1deb1fc4e59237e65497bb471eb47891e5857dfff1c0cfb3</citedby><cites>FETCH-LOGICAL-c382t-268bc17be73bb424c1deb1fc4e59237e65497bb471eb47891e5857dfff1c0cfb3</cites><orcidid>0000-0001-8448-9234 ; 0000-0003-0607-2462 ; 0000-0002-8066-5701 ; 0000-0002-6496-2220 ; 0000-0002-2314-7457 ; 0000-0002-0589-1848 ; 0000-0002-5554-043X ; 0000-0002-3845-3780</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34236239$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Djuričić, Goran J</creatorcontrib><creatorcontrib>Rajković, Nemanja</creatorcontrib><creatorcontrib>Milošević, Nebojša</creatorcontrib><creatorcontrib>Sopta, Jelena P</creatorcontrib><creatorcontrib>Borić, Igor</creatorcontrib><creatorcontrib>Dučić, Siniša</creatorcontrib><creatorcontrib>Apostolović, Milan</creatorcontrib><creatorcontrib>Radulovic, Marko</creatorcontrib><title>Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness</title><title>Biomarkers in medicine</title><addtitle>Biomark Med</addtitle><description>This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs.
Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy.
We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ′(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by Y-axis intersection of the regression line for box fractal dimension, r² for FD
and tumor circularity.
This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness.
Fractal analysis of MRI scans was shown to predict the chemosensitivity of osteosarcoma. These findings may eventually lead to improved patient survival by enabling personalized cytotoxic chemotherapy prescription.</description><subject>cancer</subject><subject>computational image analysis</subject><subject>cytotoxic chemotherapy</subject><subject>fractal analysis</subject><subject>medical image analysis</subject><subject>MRI</subject><subject>osteosarcoma</subject><subject>prediction</subject><subject>prognosis</subject><subject>tumor circularity</subject><issn>1752-0363</issn><issn>1752-0371</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kE1LxDAQhoMo7rp69Co9eqnmo23aoyzqLqwIoufQphOMNE3ttML-e1O67s3LTGby8MI8hFwzesc5k_eVczGnnMY0l9kJWTKZ8pgKyU6P70wsyAXiF6WplBk_JwuRcJFxUSzJZu1dNw7lYH1bNlEZyh4tRt5EL29bjLoeaquHsMABPJa99q6M9Cc43wN2vkX7Ay0gXpIzUzYIV4e-Ih9Pj-_rTbx7fd6uH3axFjkfYp7llWayAimqKuGJZjVUzOgE0oILCVmaFDL8SAah5AWDNE9lbYxhmmpTiRW5nXO73n-PgINyFjU0TdmCH1HxEJAVdDpvReIZ1b1H7MGorreu7PeKUTXJU0GemuSpSV7gbw7RY-WgPtJ_tgJQzIAZhzGcry20GtQ8uUmUbeGf8F8yDX-a</recordid><startdate>20210801</startdate><enddate>20210801</enddate><creator>Djuričić, Goran J</creator><creator>Rajković, Nemanja</creator><creator>Milošević, Nebojša</creator><creator>Sopta, Jelena P</creator><creator>Borić, Igor</creator><creator>Dučić, Siniša</creator><creator>Apostolović, Milan</creator><creator>Radulovic, Marko</creator><general>Future Medicine Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8448-9234</orcidid><orcidid>https://orcid.org/0000-0003-0607-2462</orcidid><orcidid>https://orcid.org/0000-0002-8066-5701</orcidid><orcidid>https://orcid.org/0000-0002-6496-2220</orcidid><orcidid>https://orcid.org/0000-0002-2314-7457</orcidid><orcidid>https://orcid.org/0000-0002-0589-1848</orcidid><orcidid>https://orcid.org/0000-0002-5554-043X</orcidid><orcidid>https://orcid.org/0000-0002-3845-3780</orcidid></search><sort><creationdate>20210801</creationdate><title>Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness</title><author>Djuričić, Goran J ; Rajković, Nemanja ; Milošević, Nebojša ; Sopta, Jelena P ; Borić, Igor ; Dučić, Siniša ; Apostolović, Milan ; Radulovic, Marko</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c382t-268bc17be73bb424c1deb1fc4e59237e65497bb471eb47891e5857dfff1c0cfb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>cancer</topic><topic>computational image analysis</topic><topic>cytotoxic chemotherapy</topic><topic>fractal analysis</topic><topic>medical image analysis</topic><topic>MRI</topic><topic>osteosarcoma</topic><topic>prediction</topic><topic>prognosis</topic><topic>tumor circularity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Djuričić, Goran J</creatorcontrib><creatorcontrib>Rajković, Nemanja</creatorcontrib><creatorcontrib>Milošević, Nebojša</creatorcontrib><creatorcontrib>Sopta, Jelena P</creatorcontrib><creatorcontrib>Borić, Igor</creatorcontrib><creatorcontrib>Dučić, Siniša</creatorcontrib><creatorcontrib>Apostolović, Milan</creatorcontrib><creatorcontrib>Radulovic, Marko</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Biomarkers in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Djuričić, Goran J</au><au>Rajković, Nemanja</au><au>Milošević, Nebojša</au><au>Sopta, Jelena P</au><au>Borić, Igor</au><au>Dučić, Siniša</au><au>Apostolović, Milan</au><au>Radulovic, Marko</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness</atitle><jtitle>Biomarkers in medicine</jtitle><addtitle>Biomark Med</addtitle><date>2021-08-01</date><risdate>2021</risdate><volume>15</volume><issue>12</issue><spage>929</spage><epage>940</epage><pages>929-940</pages><issn>1752-0363</issn><eissn>1752-0371</eissn><abstract>This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs.
Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy.
We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ′(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by Y-axis intersection of the regression line for box fractal dimension, r² for FD
and tumor circularity.
This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness.
Fractal analysis of MRI scans was shown to predict the chemosensitivity of osteosarcoma. These findings may eventually lead to improved patient survival by enabling personalized cytotoxic chemotherapy prescription.</abstract><cop>England</cop><pub>Future Medicine Ltd</pub><pmid>34236239</pmid><doi>10.2217/bmm-2020-0876</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-8448-9234</orcidid><orcidid>https://orcid.org/0000-0003-0607-2462</orcidid><orcidid>https://orcid.org/0000-0002-8066-5701</orcidid><orcidid>https://orcid.org/0000-0002-6496-2220</orcidid><orcidid>https://orcid.org/0000-0002-2314-7457</orcidid><orcidid>https://orcid.org/0000-0002-0589-1848</orcidid><orcidid>https://orcid.org/0000-0002-5554-043X</orcidid><orcidid>https://orcid.org/0000-0002-3845-3780</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1752-0363 |
| ispartof | Biomarkers in medicine, 2021-08, Vol.15 (12), p.929-940 |
| issn | 1752-0363 1752-0371 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2549690362 |
| source | PubMed Central |
| subjects | cancer computational image analysis cytotoxic chemotherapy fractal analysis medical image analysis MRI osteosarcoma prediction prognosis tumor circularity |
| title | Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T06%3A45%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Computational%20analysis%20of%20MRIs%20predicts%20osteosarcoma%20chemoresponsiveness&rft.jtitle=Biomarkers%20in%20medicine&rft.au=Djuri%C4%8Di%C4%87,%20Goran%20J&rft.date=2021-08-01&rft.volume=15&rft.issue=12&rft.spage=929&rft.epage=940&rft.pages=929-940&rft.issn=1752-0363&rft.eissn=1752-0371&rft_id=info:doi/10.2217/bmm-2020-0876&rft_dat=%3Cproquest_cross%3E2549690362%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2549690362&rft_id=info:pmid/34236239&rfr_iscdi=true |