A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods
Objective This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline 18 F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology. Methods The in...
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| Veröffentlicht in: | Annals of nuclear medicine 2021-09, Vol.35 (9), p.1030-1037 |
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| creator | Karahan Şen, Nazlı Pınar Aksu, Ayşegül Çapa Kaya, Gamze |
| description | Objective
This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline
18
F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology.
Methods
The initial staging
18
F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann–Whitney
U
test) was used for each feature, in pairwise comparisons of independent variables. A
p
value of |
| doi_str_mv | 10.1007/s12149-021-01638-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2539525569</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2539525569</sourcerecordid><originalsourceid>FETCH-LOGICAL-c399t-4554c623f91a3730ccb19235b8668e352e9e4b4aeff999e5375c54fd51c1e6413</originalsourceid><addsrcrecordid>eNp9kc1u1DAUhS0EotPCC7BAltiwCfV_YnbVqLSVKtHFsI48nuuJqyQebKd_78A743QGkFhUutJdnO-cK_sg9IGSL5SQ-jRRRoWuCKMVoYo31dMrtKCNEpUSnL9GC6KpqGra1EfoOKVbQlgjG_YWHXFBiRKsWaBfZ3jjnYMIY8bhDuKdh3scHE7ZbP24xTfnq9PlCvvBbCFhP-Kdyb7ACd_73GFIYdcVyfTYmtFC_IpzBziGHuaUDA95ikU0o-kfkz-4BmM7PwLuwcRxvjJA7sImvUNvnOkTvD_sE_Tj2_lqeVldf7-4Wp5dV5ZrnSshpbCKcaep4TUn1q6pZlyuG6Ua4JKBBrEWBpzTWoPktbRSuI2kloISlJ-gz_vcXQw_J0i5HXyy0PdmhDCllkmuJZNS6YJ--g-9DVMsr5kp2czzHMj2lI0hpQiu3cXyZfGxpaSdy2r3ZbWlrPa5rPapmD4eoqf1AJu_lj_tFIDvgVSkcQvx3-0XYn8DfQug3w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2558558541</pqid></control><display><type>article</type><title>A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Karahan Şen, Nazlı Pınar ; Aksu, Ayşegül ; Çapa Kaya, Gamze</creator><creatorcontrib>Karahan Şen, Nazlı Pınar ; Aksu, Ayşegül ; Çapa Kaya, Gamze</creatorcontrib><description>Objective
This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline
18
F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology.
Methods
The initial staging
18
F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann–Whitney
U
test) was used for each feature, in pairwise comparisons of independent variables. A
p
value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with
p
< 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms.
Results
In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination.
Conclusion
Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.</description><identifier>ISSN: 0914-7187</identifier><identifier>ISSN: 1864-6433</identifier><identifier>EISSN: 1864-6433</identifier><identifier>DOI: 10.1007/s12149-021-01638-z</identifier><identifier>PMID: 34106428</identifier><language>eng</language><publisher>Singapore: Springer Singapore</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Algorithms ; Bayesian analysis ; Cancer ; Computed tomography ; Correlation coefficients ; Esophageal cancer ; Esophageal Neoplasms - diagnostic imaging ; Esophageal Neoplasms - pathology ; Esophagus ; Female ; Fluorine isotopes ; Fluorodeoxyglucose F18 ; Humans ; Image Processing, Computer-Assisted - methods ; Imaging ; Independent variables ; Learning algorithms ; Machine Learning ; Male ; Medical imaging ; Medicine ; Medicine & Public Health ; Middle Aged ; Neoplasm Staging ; Nuclear Medicine ; Original Article ; Positron emission ; Positron emission tomography ; Positron Emission Tomography Computed Tomography ; Radiology ; Retrospective Studies ; Survival ; Survival Analysis ; Texture ; Tomography</subject><ispartof>Annals of nuclear medicine, 2021-09, Vol.35 (9), p.1030-1037</ispartof><rights>The Japanese Society of Nuclear Medicine 2021</rights><rights>2021. The Japanese Society of Nuclear Medicine.</rights><rights>The Japanese Society of Nuclear Medicine 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-4554c623f91a3730ccb19235b8668e352e9e4b4aeff999e5375c54fd51c1e6413</citedby><cites>FETCH-LOGICAL-c399t-4554c623f91a3730ccb19235b8668e352e9e4b4aeff999e5375c54fd51c1e6413</cites><orcidid>0000-0002-1562-9085</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12149-021-01638-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12149-021-01638-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34106428$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Karahan Şen, Nazlı Pınar</creatorcontrib><creatorcontrib>Aksu, Ayşegül</creatorcontrib><creatorcontrib>Çapa Kaya, Gamze</creatorcontrib><title>A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods</title><title>Annals of nuclear medicine</title><addtitle>Ann Nucl Med</addtitle><addtitle>Ann Nucl Med</addtitle><description>Objective
This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline
18
F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology.
Methods
The initial staging
18
F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann–Whitney
U
test) was used for each feature, in pairwise comparisons of independent variables. A
p
value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with
p
< 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms.
Results
In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination.
Conclusion
Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Bayesian analysis</subject><subject>Cancer</subject><subject>Computed tomography</subject><subject>Correlation coefficients</subject><subject>Esophageal cancer</subject><subject>Esophageal Neoplasms - diagnostic imaging</subject><subject>Esophageal Neoplasms - pathology</subject><subject>Esophagus</subject><subject>Female</subject><subject>Fluorine isotopes</subject><subject>Fluorodeoxyglucose F18</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Imaging</subject><subject>Independent variables</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neoplasm Staging</subject><subject>Nuclear Medicine</subject><subject>Original Article</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>Positron Emission Tomography Computed Tomography</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Survival</subject><subject>Survival Analysis</subject><subject>Texture</subject><subject>Tomography</subject><issn>0914-7187</issn><issn>1864-6433</issn><issn>1864-6433</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1u1DAUhS0EotPCC7BAltiwCfV_YnbVqLSVKtHFsI48nuuJqyQebKd_78A743QGkFhUutJdnO-cK_sg9IGSL5SQ-jRRRoWuCKMVoYo31dMrtKCNEpUSnL9GC6KpqGra1EfoOKVbQlgjG_YWHXFBiRKsWaBfZ3jjnYMIY8bhDuKdh3scHE7ZbP24xTfnq9PlCvvBbCFhP-Kdyb7ACd_73GFIYdcVyfTYmtFC_IpzBziGHuaUDA95ikU0o-kfkz-4BmM7PwLuwcRxvjJA7sImvUNvnOkTvD_sE_Tj2_lqeVldf7-4Wp5dV5ZrnSshpbCKcaep4TUn1q6pZlyuG6Ua4JKBBrEWBpzTWoPktbRSuI2kloISlJ-gz_vcXQw_J0i5HXyy0PdmhDCllkmuJZNS6YJ--g-9DVMsr5kp2czzHMj2lI0hpQiu3cXyZfGxpaSdy2r3ZbWlrPa5rPapmD4eoqf1AJu_lj_tFIDvgVSkcQvx3-0XYn8DfQug3w</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Karahan Şen, Nazlı Pınar</creator><creator>Aksu, Ayşegül</creator><creator>Çapa Kaya, Gamze</creator><general>Springer Singapore</general><general>Springer Nature B.V</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>7QP</scope><scope>7TK</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1562-9085</orcidid></search><sort><creationdate>20210901</creationdate><title>A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods</title><author>Karahan Şen, Nazlı Pınar ; Aksu, Ayşegül ; Çapa Kaya, Gamze</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-4554c623f91a3730ccb19235b8668e352e9e4b4aeff999e5375c54fd51c1e6413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Bayesian analysis</topic><topic>Cancer</topic><topic>Computed tomography</topic><topic>Correlation coefficients</topic><topic>Esophageal cancer</topic><topic>Esophageal Neoplasms - diagnostic imaging</topic><topic>Esophageal Neoplasms - pathology</topic><topic>Esophagus</topic><topic>Female</topic><topic>Fluorine isotopes</topic><topic>Fluorodeoxyglucose F18</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Imaging</topic><topic>Independent variables</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neoplasm Staging</topic><topic>Nuclear Medicine</topic><topic>Original Article</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Positron Emission Tomography Computed Tomography</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Survival</topic><topic>Survival Analysis</topic><topic>Texture</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karahan Şen, Nazlı Pınar</creatorcontrib><creatorcontrib>Aksu, Ayşegül</creatorcontrib><creatorcontrib>Çapa Kaya, Gamze</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Annals of nuclear medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karahan Şen, Nazlı Pınar</au><au>Aksu, Ayşegül</au><au>Çapa Kaya, Gamze</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods</atitle><jtitle>Annals of nuclear medicine</jtitle><stitle>Ann Nucl Med</stitle><addtitle>Ann Nucl Med</addtitle><date>2021-09-01</date><risdate>2021</risdate><volume>35</volume><issue>9</issue><spage>1030</spage><epage>1037</epage><pages>1030-1037</pages><issn>0914-7187</issn><issn>1864-6433</issn><eissn>1864-6433</eissn><abstract>Objective
This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline
18
F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology.
Methods
The initial staging
18
F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann–Whitney
U
test) was used for each feature, in pairwise comparisons of independent variables. A
p
value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with
p
< 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms.
Results
In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination.
Conclusion
Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.</abstract><cop>Singapore</cop><pub>Springer Singapore</pub><pmid>34106428</pmid><doi>10.1007/s12149-021-01638-z</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-1562-9085</orcidid></addata></record> |
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| ispartof | Annals of nuclear medicine, 2021-09, Vol.35 (9), p.1030-1037 |
| issn | 0914-7187 1864-6433 1864-6433 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Adult Aged Aged, 80 and over Algorithms Bayesian analysis Cancer Computed tomography Correlation coefficients Esophageal cancer Esophageal Neoplasms - diagnostic imaging Esophageal Neoplasms - pathology Esophagus Female Fluorine isotopes Fluorodeoxyglucose F18 Humans Image Processing, Computer-Assisted - methods Imaging Independent variables Learning algorithms Machine Learning Male Medical imaging Medicine Medicine & Public Health Middle Aged Neoplasm Staging Nuclear Medicine Original Article Positron emission Positron emission tomography Positron Emission Tomography Computed Tomography Radiology Retrospective Studies Survival Survival Analysis Texture Tomography |
| title | A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods |
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