Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra
Object This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spec...
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creator | Fuster-Garcia, Elies Navarro, Clara Vicente, Javier Tortajada, Salvador García-Gómez, Juan M. Sáez, Carlos Calvar, Jorge Griffiths, John Julià-Sapé, Margarida Howe, Franklyn A. Pujol, Jesús Peet, Andrew C. Heerschap, Arend Moreno-Torres, Àngel Martínez-Bisbal, M. C. Martínez-Granados, Beatriz Wesseling, Pieter Semmler, Wolfhard Capellades, Jaume Majós, Carles Alberich-Bayarri, Àngel Capdevila, Antoni Monleón, Daniel Martí-Bonmatí, Luis Arús, Carles Celda, Bernardo Robles, Montserrat |
description | Object
This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases.
Materials and methods
Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed.
Results
Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns.
Conclusion
These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T
1
H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments. |
doi_str_mv | 10.1007/s10334-010-0241-8 |
format | Article |
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This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases.
Materials and methods
Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed.
Results
Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns.
Conclusion
These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T
1
H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.</description><identifier>ISSN: 0968-5243</identifier><identifier>EISSN: 1352-8661</identifier><identifier>DOI: 10.1007/s10334-010-0241-8</identifier><identifier>PMID: 21249420</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Biomedical Engineering and Bioengineering ; Brain Neoplasms - diagnosis ; Brain Neoplasms - metabolism ; Computer Appl. in Life Sciences ; Databases, Factual ; Health Informatics ; Humans ; Imaging ; Magnetic Resonance Spectroscopy - methods ; Medicine ; Medicine & Public Health ; Pattern Recognition, Automated - methods ; Protons ; Radiology ; Research Article ; Sensitivity and Specificity ; Solid State Physics</subject><ispartof>Magma (New York, N.Y.), 2011-02, Vol.24 (1), p.35-42</ispartof><rights>ESMRMB 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3018-5facb401fd183e3cb0c7c5f8791575a2d6d655470e0041cab6a326e9eab33dfc3</citedby><cites>FETCH-LOGICAL-c3018-5facb401fd183e3cb0c7c5f8791575a2d6d655470e0041cab6a326e9eab33dfc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10334-010-0241-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10334-010-0241-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21249420$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fuster-Garcia, Elies</creatorcontrib><creatorcontrib>Navarro, Clara</creatorcontrib><creatorcontrib>Vicente, Javier</creatorcontrib><creatorcontrib>Tortajada, Salvador</creatorcontrib><creatorcontrib>García-Gómez, Juan M.</creatorcontrib><creatorcontrib>Sáez, Carlos</creatorcontrib><creatorcontrib>Calvar, Jorge</creatorcontrib><creatorcontrib>Griffiths, John</creatorcontrib><creatorcontrib>Julià-Sapé, Margarida</creatorcontrib><creatorcontrib>Howe, Franklyn A.</creatorcontrib><creatorcontrib>Pujol, Jesús</creatorcontrib><creatorcontrib>Peet, Andrew C.</creatorcontrib><creatorcontrib>Heerschap, Arend</creatorcontrib><creatorcontrib>Moreno-Torres, Àngel</creatorcontrib><creatorcontrib>Martínez-Bisbal, M. C.</creatorcontrib><creatorcontrib>Martínez-Granados, Beatriz</creatorcontrib><creatorcontrib>Wesseling, Pieter</creatorcontrib><creatorcontrib>Semmler, Wolfhard</creatorcontrib><creatorcontrib>Capellades, Jaume</creatorcontrib><creatorcontrib>Majós, Carles</creatorcontrib><creatorcontrib>Alberich-Bayarri, Àngel</creatorcontrib><creatorcontrib>Capdevila, Antoni</creatorcontrib><creatorcontrib>Monleón, Daniel</creatorcontrib><creatorcontrib>Martí-Bonmatí, Luis</creatorcontrib><creatorcontrib>Arús, Carles</creatorcontrib><creatorcontrib>Celda, Bernardo</creatorcontrib><creatorcontrib>Robles, Montserrat</creatorcontrib><title>Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra</title><title>Magma (New York, N.Y.)</title><addtitle>Magn Reson Mater Phy</addtitle><addtitle>MAGMA</addtitle><description>Object
This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases.
Materials and methods
Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed.
Results
Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns.
Conclusion
These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T
1
H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.</description><subject>Biomedical Engineering and Bioengineering</subject><subject>Brain Neoplasms - diagnosis</subject><subject>Brain Neoplasms - metabolism</subject><subject>Computer Appl. in Life Sciences</subject><subject>Databases, Factual</subject><subject>Health Informatics</subject><subject>Humans</subject><subject>Imaging</subject><subject>Magnetic Resonance Spectroscopy - methods</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Protons</subject><subject>Radiology</subject><subject>Research Article</subject><subject>Sensitivity and Specificity</subject><subject>Solid State Physics</subject><issn>0968-5243</issn><issn>1352-8661</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc9u1DAQhy0EotvCA3BBvnFKmbHjJHtEK6CVipDotlfL_1Kl2sTB4wjtK_Qp-iw8GV62wI3TaDTffNLMj7E3COcI0L4nBCnrChAqEDVW3TO2QqlE1TUNPmcrWDddpUQtT9gp0T2AQAXyJTsRKOp1LWDFHjZxnE0e7LAb8p7bkH-EMHG5_fmIF_z6tvry7Zp7kw03k-dmyXEstOM2mWHieRnjkrgfzN0UaSBOyzzHlDntKYeRuDUUPI_Tb8WhIR57judqy__paQ4uJ_OKvejNjsLrp3rGbj593G4uqquvny83H64qJwHLPb1xtgbsPXYySGfBtU71XbtG1SojfOMbpeoWAkCNztjGSNGEdTBWSt87ecbeHb1zit-XQFmPA7mw25kpxIV0p0A1QjWykHgkXYpEKfR6TsNo0l4j6EMC-piALgnoQwK6Kztvn-yLHYP_u_Hn5QUQR4DKaLoLSd-XH07l4v9YfwFsKJFj</recordid><startdate>201102</startdate><enddate>201102</enddate><creator>Fuster-Garcia, Elies</creator><creator>Navarro, Clara</creator><creator>Vicente, Javier</creator><creator>Tortajada, Salvador</creator><creator>García-Gómez, Juan M.</creator><creator>Sáez, Carlos</creator><creator>Calvar, Jorge</creator><creator>Griffiths, John</creator><creator>Julià-Sapé, Margarida</creator><creator>Howe, Franklyn A.</creator><creator>Pujol, Jesús</creator><creator>Peet, Andrew C.</creator><creator>Heerschap, Arend</creator><creator>Moreno-Torres, Àngel</creator><creator>Martínez-Bisbal, M. C.</creator><creator>Martínez-Granados, Beatriz</creator><creator>Wesseling, Pieter</creator><creator>Semmler, Wolfhard</creator><creator>Capellades, Jaume</creator><creator>Majós, Carles</creator><creator>Alberich-Bayarri, Àngel</creator><creator>Capdevila, Antoni</creator><creator>Monleón, Daniel</creator><creator>Martí-Bonmatí, Luis</creator><creator>Arús, Carles</creator><creator>Celda, Bernardo</creator><creator>Robles, Montserrat</creator><general>Springer-Verlag</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>7X8</scope></search><sort><creationdate>201102</creationdate><title>Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra</title><author>Fuster-Garcia, Elies ; Navarro, Clara ; Vicente, Javier ; Tortajada, Salvador ; García-Gómez, Juan M. ; Sáez, Carlos ; Calvar, Jorge ; Griffiths, John ; Julià-Sapé, Margarida ; Howe, Franklyn A. ; Pujol, Jesús ; Peet, Andrew C. ; Heerschap, Arend ; Moreno-Torres, Àngel ; Martínez-Bisbal, M. C. ; Martínez-Granados, Beatriz ; Wesseling, Pieter ; Semmler, Wolfhard ; Capellades, Jaume ; Majós, Carles ; Alberich-Bayarri, Àngel ; Capdevila, Antoni ; Monleón, Daniel ; Martí-Bonmatí, Luis ; Arús, Carles ; Celda, Bernardo ; Robles, Montserrat</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3018-5facb401fd183e3cb0c7c5f8791575a2d6d655470e0041cab6a326e9eab33dfc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Biomedical Engineering and Bioengineering</topic><topic>Brain Neoplasms - diagnosis</topic><topic>Brain Neoplasms - metabolism</topic><topic>Computer Appl. in Life Sciences</topic><topic>Databases, Factual</topic><topic>Health Informatics</topic><topic>Humans</topic><topic>Imaging</topic><topic>Magnetic Resonance Spectroscopy - methods</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Protons</topic><topic>Radiology</topic><topic>Research Article</topic><topic>Sensitivity and Specificity</topic><topic>Solid State Physics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fuster-Garcia, Elies</creatorcontrib><creatorcontrib>Navarro, Clara</creatorcontrib><creatorcontrib>Vicente, Javier</creatorcontrib><creatorcontrib>Tortajada, Salvador</creatorcontrib><creatorcontrib>García-Gómez, Juan M.</creatorcontrib><creatorcontrib>Sáez, Carlos</creatorcontrib><creatorcontrib>Calvar, Jorge</creatorcontrib><creatorcontrib>Griffiths, John</creatorcontrib><creatorcontrib>Julià-Sapé, Margarida</creatorcontrib><creatorcontrib>Howe, Franklyn A.</creatorcontrib><creatorcontrib>Pujol, Jesús</creatorcontrib><creatorcontrib>Peet, Andrew C.</creatorcontrib><creatorcontrib>Heerschap, Arend</creatorcontrib><creatorcontrib>Moreno-Torres, Àngel</creatorcontrib><creatorcontrib>Martínez-Bisbal, M. C.</creatorcontrib><creatorcontrib>Martínez-Granados, Beatriz</creatorcontrib><creatorcontrib>Wesseling, Pieter</creatorcontrib><creatorcontrib>Semmler, Wolfhard</creatorcontrib><creatorcontrib>Capellades, Jaume</creatorcontrib><creatorcontrib>Majós, Carles</creatorcontrib><creatorcontrib>Alberich-Bayarri, Àngel</creatorcontrib><creatorcontrib>Capdevila, Antoni</creatorcontrib><creatorcontrib>Monleón, Daniel</creatorcontrib><creatorcontrib>Martí-Bonmatí, Luis</creatorcontrib><creatorcontrib>Arús, Carles</creatorcontrib><creatorcontrib>Celda, Bernardo</creatorcontrib><creatorcontrib>Robles, Montserrat</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Magma (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fuster-Garcia, Elies</au><au>Navarro, Clara</au><au>Vicente, Javier</au><au>Tortajada, Salvador</au><au>García-Gómez, Juan M.</au><au>Sáez, Carlos</au><au>Calvar, Jorge</au><au>Griffiths, John</au><au>Julià-Sapé, Margarida</au><au>Howe, Franklyn A.</au><au>Pujol, Jesús</au><au>Peet, Andrew C.</au><au>Heerschap, Arend</au><au>Moreno-Torres, Àngel</au><au>Martínez-Bisbal, M. C.</au><au>Martínez-Granados, Beatriz</au><au>Wesseling, Pieter</au><au>Semmler, Wolfhard</au><au>Capellades, Jaume</au><au>Majós, Carles</au><au>Alberich-Bayarri, Àngel</au><au>Capdevila, Antoni</au><au>Monleón, Daniel</au><au>Martí-Bonmatí, Luis</au><au>Arús, Carles</au><au>Celda, Bernardo</au><au>Robles, Montserrat</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra</atitle><jtitle>Magma (New York, N.Y.)</jtitle><stitle>Magn Reson Mater Phy</stitle><addtitle>MAGMA</addtitle><date>2011-02</date><risdate>2011</risdate><volume>24</volume><issue>1</issue><spage>35</spage><epage>42</epage><pages>35-42</pages><issn>0968-5243</issn><eissn>1352-8661</eissn><abstract>Object
This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases.
Materials and methods
Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed.
Results
Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns.
Conclusion
These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T
1
H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><pmid>21249420</pmid><doi>10.1007/s10334-010-0241-8</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Biomedical Engineering and Bioengineering Brain Neoplasms - diagnosis Brain Neoplasms - metabolism Computer Appl. in Life Sciences Databases, Factual Health Informatics Humans Imaging Magnetic Resonance Spectroscopy - methods Medicine Medicine & Public Health Pattern Recognition, Automated - methods Protons Radiology Research Article Sensitivity and Specificity Solid State Physics |
title | Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra |
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