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Analysis of peritumoral hyperintensity on pre-operative T2-weighted MR images in glioblastoma: Additive prognostic value of Minkowski functionals
The extent of peritumoral tumor cell infiltrations in glioblastoma contributes to poor prognosis. We aimed to assess additive prognostic value of Minkowski functionals in analyzing heterogeneity of peritumoral hyperintensity on T2WI in glioblastoma patients. Clinical data (age, sex, extent of surgic...
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Published in: | PloS one 2019-05, Vol.14 (5), p.e0217785-e0217785 |
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description | The extent of peritumoral tumor cell infiltrations in glioblastoma contributes to poor prognosis. We aimed to assess additive prognostic value of Minkowski functionals in analyzing heterogeneity of peritumoral hyperintensity on T2WI in glioblastoma patients.
Clinical data (age, sex, extent of surgical resection), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and pre-operative T2WI of 113 pathologically confirmed glioblastoma patients (from our institution, n = 61; from the Cancer Imaging Archive, n = 52) were retrospectively reviewed. The patients were randomly grouped into a training set (n = 80) and a test set (n = 33). Peritumoral T2 hyperintensity was manually segmented and Minkowski functionals-a texture analysis method capturing heterogeneity of MR images-were computed as a function of 11 grayscale thresholds. The Cox proportional hazards models were fitted with clinical variables, Minkowski functionals features as well as both combined. The risk prediction performances of the Minkowski functionals and combined models were validated on a separate test dataset. The sex-specific survival difference of the entire cohort was analyzed according to MGMT methylation status via Kaplan-Meier survival curves.
Thirty-three Minkowski features (11 area, 11 perimeter and 11 genus) for each patient were acquired giving a total of 3729 features. Cox regression models fitted with clinical data, Minkowski features, and both combined had incremental concordance indices of 0.577 (P = 0.02), 0.706 (P = 0.02) and 0.714 (P = 0.01) respectively. The prediction error rate of the combined model-having clinical and Minkowski features-was lower than that of Minkowski functionals model (0.135 and 0.161, respectively) when validated on a test dataset. No sex-specific survival difference was found according to MGMT methylation status (male, P = 0.2; female, P = 0.22).
Minkowski functionals features computed from peritumoral hyperintensity can capture heterogeneity of glioblastoma on T2WI and have additive prognostic value in predicting survival, demonstrating their potential in complementing currently available prognostic parameters. |
doi_str_mv | 10.1371/journal.pone.0217785 |
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Clinical data (age, sex, extent of surgical resection), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and pre-operative T2WI of 113 pathologically confirmed glioblastoma patients (from our institution, n = 61; from the Cancer Imaging Archive, n = 52) were retrospectively reviewed. The patients were randomly grouped into a training set (n = 80) and a test set (n = 33). Peritumoral T2 hyperintensity was manually segmented and Minkowski functionals-a texture analysis method capturing heterogeneity of MR images-were computed as a function of 11 grayscale thresholds. The Cox proportional hazards models were fitted with clinical variables, Minkowski functionals features as well as both combined. The risk prediction performances of the Minkowski functionals and combined models were validated on a separate test dataset. The sex-specific survival difference of the entire cohort was analyzed according to MGMT methylation status via Kaplan-Meier survival curves.
Thirty-three Minkowski features (11 area, 11 perimeter and 11 genus) for each patient were acquired giving a total of 3729 features. Cox regression models fitted with clinical data, Minkowski features, and both combined had incremental concordance indices of 0.577 (P = 0.02), 0.706 (P = 0.02) and 0.714 (P = 0.01) respectively. The prediction error rate of the combined model-having clinical and Minkowski features-was lower than that of Minkowski functionals model (0.135 and 0.161, respectively) when validated on a test dataset. No sex-specific survival difference was found according to MGMT methylation status (male, P = 0.2; female, P = 0.22).
Minkowski functionals features computed from peritumoral hyperintensity can capture heterogeneity of glioblastoma on T2WI and have additive prognostic value in predicting survival, demonstrating their potential in complementing currently available prognostic parameters.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0217785</identifier><identifier>PMID: 31150499</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aged ; Biomarkers, Tumor - genetics ; Biopsy ; Brain cancer ; Computation ; Deoxyribonucleic acid ; Disease-Free Survival ; DNA ; DNA methylation ; DNA Methylation - genetics ; DNA methyltransferase ; DNA Modification Methylases - genetics ; DNA Repair Enzymes - genetics ; Edema ; Female ; Glioblastoma ; Glioblastoma - diagnostic imaging ; Glioblastoma - genetics ; Glioblastoma - surgery ; Glioblastoma - therapy ; Hazards ; Heterogeneity ; Hospitals ; Humans ; Kaplan-Meier Estimate ; Magnetic Resonance Imaging ; Male ; Medical imaging ; Medical prognosis ; Medicine ; Medicine and Health Sciences ; Methylation ; Methylguanine ; Middle Aged ; Mutation ; O6-methylguanine-DNA methyltransferase ; Physical Sciences ; Predictions ; Prognosis ; Promoter Regions, Genetic ; Proportional Hazards Models ; Regression analysis ; Regression models ; Research and Analysis Methods ; Retrospective Studies ; Sex ; Sex Characteristics ; Statistical models ; Survival ; Survival Analysis ; Systematic review ; Temozolomide ; Tumor Suppressor Proteins - genetics</subject><ispartof>PloS one, 2019-05, Vol.14 (5), p.e0217785-e0217785</ispartof><rights>2019 Choi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Choi et al 2019 Choi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-aaa96292710902af927f4f76dd4339e080b84930b16f2f6ee3fafd5652a24963</citedby><cites>FETCH-LOGICAL-c526t-aaa96292710902af927f4f76dd4339e080b84930b16f2f6ee3fafd5652a24963</cites><orcidid>0000-0001-6081-7360 ; 0000-0003-1674-7101</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2233259022/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2233259022?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25752,27923,27924,37011,37012,44589,53790,53792,74897</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31150499$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Lin, Pan</contributor><creatorcontrib>Choi, Yangsean</creatorcontrib><creatorcontrib>Ahn, Kook Jin</creatorcontrib><creatorcontrib>Nam, Yoonho</creatorcontrib><creatorcontrib>Jang, Jinhee</creatorcontrib><creatorcontrib>Shin, Na-Young</creatorcontrib><creatorcontrib>Choi, Hyun Seok</creatorcontrib><creatorcontrib>Jung, So-Lyung</creatorcontrib><creatorcontrib>Kim, Bum-Soo</creatorcontrib><title>Analysis of peritumoral hyperintensity on pre-operative T2-weighted MR images in glioblastoma: Additive prognostic value of Minkowski functionals</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The extent of peritumoral tumor cell infiltrations in glioblastoma contributes to poor prognosis. We aimed to assess additive prognostic value of Minkowski functionals in analyzing heterogeneity of peritumoral hyperintensity on T2WI in glioblastoma patients.
Clinical data (age, sex, extent of surgical resection), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and pre-operative T2WI of 113 pathologically confirmed glioblastoma patients (from our institution, n = 61; from the Cancer Imaging Archive, n = 52) were retrospectively reviewed. The patients were randomly grouped into a training set (n = 80) and a test set (n = 33). Peritumoral T2 hyperintensity was manually segmented and Minkowski functionals-a texture analysis method capturing heterogeneity of MR images-were computed as a function of 11 grayscale thresholds. The Cox proportional hazards models were fitted with clinical variables, Minkowski functionals features as well as both combined. The risk prediction performances of the Minkowski functionals and combined models were validated on a separate test dataset. The sex-specific survival difference of the entire cohort was analyzed according to MGMT methylation status via Kaplan-Meier survival curves.
Thirty-three Minkowski features (11 area, 11 perimeter and 11 genus) for each patient were acquired giving a total of 3729 features. Cox regression models fitted with clinical data, Minkowski features, and both combined had incremental concordance indices of 0.577 (P = 0.02), 0.706 (P = 0.02) and 0.714 (P = 0.01) respectively. The prediction error rate of the combined model-having clinical and Minkowski features-was lower than that of Minkowski functionals model (0.135 and 0.161, respectively) when validated on a test dataset. No sex-specific survival difference was found according to MGMT methylation status (male, P = 0.2; female, P = 0.22).
Minkowski functionals features computed from peritumoral hyperintensity can capture heterogeneity of glioblastoma on T2WI and have additive prognostic value in predicting survival, demonstrating their potential in complementing currently available prognostic parameters.</description><subject>Aged</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biopsy</subject><subject>Brain cancer</subject><subject>Computation</subject><subject>Deoxyribonucleic acid</subject><subject>Disease-Free Survival</subject><subject>DNA</subject><subject>DNA methylation</subject><subject>DNA Methylation - genetics</subject><subject>DNA methyltransferase</subject><subject>DNA Modification Methylases - genetics</subject><subject>DNA Repair Enzymes - genetics</subject><subject>Edema</subject><subject>Female</subject><subject>Glioblastoma</subject><subject>Glioblastoma - diagnostic imaging</subject><subject>Glioblastoma - genetics</subject><subject>Glioblastoma - surgery</subject><subject>Glioblastoma - therapy</subject><subject>Hazards</subject><subject>Heterogeneity</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Kaplan-Meier Estimate</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Methylation</subject><subject>Methylguanine</subject><subject>Middle Aged</subject><subject>Mutation</subject><subject>O6-methylguanine-DNA methyltransferase</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Prognosis</subject><subject>Promoter Regions, Genetic</subject><subject>Proportional Hazards Models</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Research and Analysis Methods</subject><subject>Retrospective Studies</subject><subject>Sex</subject><subject>Sex Characteristics</subject><subject>Statistical models</subject><subject>Survival</subject><subject>Survival Analysis</subject><subject>Systematic review</subject><subject>Temozolomide</subject><subject>Tumor Suppressor Proteins - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Choi, Yangsean</au><au>Ahn, Kook Jin</au><au>Nam, Yoonho</au><au>Jang, Jinhee</au><au>Shin, Na-Young</au><au>Choi, Hyun Seok</au><au>Jung, So-Lyung</au><au>Kim, Bum-Soo</au><au>Lin, Pan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of peritumoral hyperintensity on pre-operative T2-weighted MR images in glioblastoma: Additive prognostic value of Minkowski functionals</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-05-31</date><risdate>2019</risdate><volume>14</volume><issue>5</issue><spage>e0217785</spage><epage>e0217785</epage><pages>e0217785-e0217785</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The extent of peritumoral tumor cell infiltrations in glioblastoma contributes to poor prognosis. We aimed to assess additive prognostic value of Minkowski functionals in analyzing heterogeneity of peritumoral hyperintensity on T2WI in glioblastoma patients.
Clinical data (age, sex, extent of surgical resection), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and pre-operative T2WI of 113 pathologically confirmed glioblastoma patients (from our institution, n = 61; from the Cancer Imaging Archive, n = 52) were retrospectively reviewed. The patients were randomly grouped into a training set (n = 80) and a test set (n = 33). Peritumoral T2 hyperintensity was manually segmented and Minkowski functionals-a texture analysis method capturing heterogeneity of MR images-were computed as a function of 11 grayscale thresholds. The Cox proportional hazards models were fitted with clinical variables, Minkowski functionals features as well as both combined. The risk prediction performances of the Minkowski functionals and combined models were validated on a separate test dataset. The sex-specific survival difference of the entire cohort was analyzed according to MGMT methylation status via Kaplan-Meier survival curves.
Thirty-three Minkowski features (11 area, 11 perimeter and 11 genus) for each patient were acquired giving a total of 3729 features. Cox regression models fitted with clinical data, Minkowski features, and both combined had incremental concordance indices of 0.577 (P = 0.02), 0.706 (P = 0.02) and 0.714 (P = 0.01) respectively. The prediction error rate of the combined model-having clinical and Minkowski features-was lower than that of Minkowski functionals model (0.135 and 0.161, respectively) when validated on a test dataset. No sex-specific survival difference was found according to MGMT methylation status (male, P = 0.2; female, P = 0.22).
Minkowski functionals features computed from peritumoral hyperintensity can capture heterogeneity of glioblastoma on T2WI and have additive prognostic value in predicting survival, demonstrating their potential in complementing currently available prognostic parameters.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31150499</pmid><doi>10.1371/journal.pone.0217785</doi><orcidid>https://orcid.org/0000-0001-6081-7360</orcidid><orcidid>https://orcid.org/0000-0003-1674-7101</orcidid><oa>free_for_read</oa></addata></record> |
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identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2019-05, Vol.14 (5), p.e0217785-e0217785 |
issn | 1932-6203 1932-6203 |
language | eng |
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source | Publicly Available Content Database; PubMed Central |
subjects | Aged Biomarkers, Tumor - genetics Biopsy Brain cancer Computation Deoxyribonucleic acid Disease-Free Survival DNA DNA methylation DNA Methylation - genetics DNA methyltransferase DNA Modification Methylases - genetics DNA Repair Enzymes - genetics Edema Female Glioblastoma Glioblastoma - diagnostic imaging Glioblastoma - genetics Glioblastoma - surgery Glioblastoma - therapy Hazards Heterogeneity Hospitals Humans Kaplan-Meier Estimate Magnetic Resonance Imaging Male Medical imaging Medical prognosis Medicine Medicine and Health Sciences Methylation Methylguanine Middle Aged Mutation O6-methylguanine-DNA methyltransferase Physical Sciences Predictions Prognosis Promoter Regions, Genetic Proportional Hazards Models Regression analysis Regression models Research and Analysis Methods Retrospective Studies Sex Sex Characteristics Statistical models Survival Survival Analysis Systematic review Temozolomide Tumor Suppressor Proteins - genetics |
title | Analysis of peritumoral hyperintensity on pre-operative T2-weighted MR images in glioblastoma: Additive prognostic value of Minkowski functionals |
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