Compatibility between 3T super(1)H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T super(1)H 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 spe...
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creator | Fuster-Garcia, Elies Navarro, Clara Vicente, Javier Tortajada, Salvador Garcia-Gomez, Juan M Saez, Carlos Calvar, Jorge Griffiths, John Julia-Sape, Margarida Howe, Franklyn A Pujol, Jesus Peet, Andrew C Heerschap, Arend Moreno-Torres, Angel Martinez-Bisbal, M C Martinez-Granados, Beatriz Wesseling, Pieter Semmler, Wolfhard Capellades, Jaume Majos, Carles Alberich-Bayarri, Angel Capdevila, Antoni Monleon, Daniel Marti-Bonmati, Luis Arus, 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 1551.5T and 373T 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 plus or minus 0.03 for 1.5T and 0.88 plus or minus 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 super(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 |
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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 1551.5T and 373T 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 plus or minus 0.03 for 1.5T and 0.88 plus or minus 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 super(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><language>eng</language><subject>Brain ; Classifiers ; Diagnosis ; Diagnostic systems ; Quality ; Spectra ; Tumours</subject><ispartof>Magma (New York, N.Y.), 2011-02, Vol.24 (1), p.35-42</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Fuster-Garcia, Elies</creatorcontrib><creatorcontrib>Navarro, Clara</creatorcontrib><creatorcontrib>Vicente, Javier</creatorcontrib><creatorcontrib>Tortajada, Salvador</creatorcontrib><creatorcontrib>Garcia-Gomez, Juan M</creatorcontrib><creatorcontrib>Saez, Carlos</creatorcontrib><creatorcontrib>Calvar, Jorge</creatorcontrib><creatorcontrib>Griffiths, John</creatorcontrib><creatorcontrib>Julia-Sape, Margarida</creatorcontrib><creatorcontrib>Howe, Franklyn A</creatorcontrib><creatorcontrib>Pujol, Jesus</creatorcontrib><creatorcontrib>Peet, Andrew C</creatorcontrib><creatorcontrib>Heerschap, Arend</creatorcontrib><creatorcontrib>Moreno-Torres, Angel</creatorcontrib><creatorcontrib>Martinez-Bisbal, M C</creatorcontrib><creatorcontrib>Martinez-Granados, Beatriz</creatorcontrib><creatorcontrib>Wesseling, Pieter</creatorcontrib><creatorcontrib>Semmler, Wolfhard</creatorcontrib><creatorcontrib>Capellades, Jaume</creatorcontrib><creatorcontrib>Majos, Carles</creatorcontrib><creatorcontrib>Alberich-Bayarri, Angel</creatorcontrib><creatorcontrib>Capdevila, Antoni</creatorcontrib><creatorcontrib>Monleon, Daniel</creatorcontrib><creatorcontrib>Marti-Bonmati, Luis</creatorcontrib><creatorcontrib>Arus, Carles</creatorcontrib><creatorcontrib>Celda, Bernardo</creatorcontrib><creatorcontrib>Robles, Montserrat</creatorcontrib><title>Compatibility between 3T super(1)H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T super(1)H SV-MRS spectra</title><title>Magma (New York, N.Y.)</title><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 1551.5T and 373T 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 plus or minus 0.03 for 1.5T and 0.88 plus or minus 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 super(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>Brain</subject><subject>Classifiers</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Quality</subject><subject>Spectra</subject><subject>Tumours</subject><issn>0968-5243</issn><issn>1352-8661</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9j89Kw0AYxBdRsFYfwNverIet--1mN8lRilpBEWzxWvZfJJJkY74N0ifxdW3Vo3gaBuY3wxByDnwOnOdXCFzKjHHgjIsMWHFAJiCVYIXWcEgmvNQFUyKTx-QE8Y1zAYrLCflcxLY3qbZ1U6cttSF9hNBRuaY49mGYweWSrl7Y4_OKepMMNZ2nZkyx3TGO2sHUHU1jG8eB-tq8dhFr3KN9HBLFLabQIrUGg6ex-67YG6SxojBXf6xgH1wazCk5qkyD4exXp2R9e7NeLNnD09394vqB9Xp3TioQsvK5L4OQhgtjQGrQ2nHrrBYgoax2p7Mil9b5shKFMj53ypfWal0KOSUXP7X9EN_HgGnT1uhC05guxBE3hQalheL75OzfJOgchJZKgfwCzS13lg</recordid><startdate>20110201</startdate><enddate>20110201</enddate><creator>Fuster-Garcia, Elies</creator><creator>Navarro, Clara</creator><creator>Vicente, Javier</creator><creator>Tortajada, Salvador</creator><creator>Garcia-Gomez, Juan M</creator><creator>Saez, Carlos</creator><creator>Calvar, Jorge</creator><creator>Griffiths, John</creator><creator>Julia-Sape, Margarida</creator><creator>Howe, Franklyn A</creator><creator>Pujol, Jesus</creator><creator>Peet, Andrew C</creator><creator>Heerschap, Arend</creator><creator>Moreno-Torres, Angel</creator><creator>Martinez-Bisbal, M C</creator><creator>Martinez-Granados, Beatriz</creator><creator>Wesseling, Pieter</creator><creator>Semmler, Wolfhard</creator><creator>Capellades, Jaume</creator><creator>Majos, Carles</creator><creator>Alberich-Bayarri, Angel</creator><creator>Capdevila, Antoni</creator><creator>Monleon, Daniel</creator><creator>Marti-Bonmati, Luis</creator><creator>Arus, Carles</creator><creator>Celda, Bernardo</creator><creator>Robles, Montserrat</creator><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>7QO</scope><scope>7TK</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20110201</creationdate><title>Compatibility between 3T super(1)H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T super(1)H SV-MRS spectra</title><author>Fuster-Garcia, Elies ; Navarro, Clara ; Vicente, Javier ; Tortajada, Salvador ; Garcia-Gomez, Juan M ; Saez, Carlos ; Calvar, Jorge ; Griffiths, John ; Julia-Sape, Margarida ; Howe, Franklyn A ; Pujol, Jesus ; Peet, Andrew C ; Heerschap, Arend ; Moreno-Torres, Angel ; Martinez-Bisbal, M C ; Martinez-Granados, Beatriz ; Wesseling, Pieter ; Semmler, Wolfhard ; Capellades, Jaume ; Majos, Carles ; Alberich-Bayarri, Angel ; Capdevila, Antoni ; Monleon, Daniel ; Marti-Bonmati, Luis ; Arus, Carles ; Celda, Bernardo ; Robles, Montserrat</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p652-35123fd7d9e23a02aa136166c0bcb621319f5244873bcd9f285ad7c5d9bb66923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Brain</topic><topic>Classifiers</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Quality</topic><topic>Spectra</topic><topic>Tumours</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>Garcia-Gomez, Juan M</creatorcontrib><creatorcontrib>Saez, Carlos</creatorcontrib><creatorcontrib>Calvar, Jorge</creatorcontrib><creatorcontrib>Griffiths, John</creatorcontrib><creatorcontrib>Julia-Sape, Margarida</creatorcontrib><creatorcontrib>Howe, Franklyn A</creatorcontrib><creatorcontrib>Pujol, Jesus</creatorcontrib><creatorcontrib>Peet, Andrew C</creatorcontrib><creatorcontrib>Heerschap, Arend</creatorcontrib><creatorcontrib>Moreno-Torres, Angel</creatorcontrib><creatorcontrib>Martinez-Bisbal, M C</creatorcontrib><creatorcontrib>Martinez-Granados, Beatriz</creatorcontrib><creatorcontrib>Wesseling, Pieter</creatorcontrib><creatorcontrib>Semmler, Wolfhard</creatorcontrib><creatorcontrib>Capellades, Jaume</creatorcontrib><creatorcontrib>Majos, Carles</creatorcontrib><creatorcontrib>Alberich-Bayarri, Angel</creatorcontrib><creatorcontrib>Capdevila, Antoni</creatorcontrib><creatorcontrib>Monleon, Daniel</creatorcontrib><creatorcontrib>Marti-Bonmati, Luis</creatorcontrib><creatorcontrib>Arus, Carles</creatorcontrib><creatorcontrib>Celda, Bernardo</creatorcontrib><creatorcontrib>Robles, Montserrat</creatorcontrib><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</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>Garcia-Gomez, Juan M</au><au>Saez, Carlos</au><au>Calvar, Jorge</au><au>Griffiths, John</au><au>Julia-Sape, Margarida</au><au>Howe, Franklyn A</au><au>Pujol, Jesus</au><au>Peet, Andrew C</au><au>Heerschap, Arend</au><au>Moreno-Torres, Angel</au><au>Martinez-Bisbal, M C</au><au>Martinez-Granados, Beatriz</au><au>Wesseling, Pieter</au><au>Semmler, Wolfhard</au><au>Capellades, Jaume</au><au>Majos, Carles</au><au>Alberich-Bayarri, Angel</au><au>Capdevila, Antoni</au><au>Monleon, Daniel</au><au>Marti-Bonmati, Luis</au><au>Arus, Carles</au><au>Celda, Bernardo</au><au>Robles, Montserrat</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Compatibility between 3T super(1)H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T super(1)H SV-MRS spectra</atitle><jtitle>Magma (New York, N.Y.)</jtitle><date>2011-02-01</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 1551.5T and 373T 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 plus or minus 0.03 for 1.5T and 0.88 plus or minus 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 super(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><doi>10.1007/s10334-010-0241-8</doi><tpages>8</tpages></addata></record> |
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subjects | Brain Classifiers Diagnosis Diagnostic systems Quality Spectra Tumours |
title | Compatibility between 3T super(1)H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T super(1)H SV-MRS spectra |
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