Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study
Differentiating pseudoprogression, a radiation-induced treatment effect, from tumor progression on imaging is a substantial challenge in glioblastoma management. Unfortunately, guidelines set by the Response Assessment in Neuro-Oncology criteria are based solely on bidirectional diametric measuremen...
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creator | Ismail, M Hill, V Statsevych, V Huang, R Prasanna, P Correa, R Singh, G Bera, K Beig, N Thawani, R Madabhushi, A Aahluwalia, M Tiwari, P |
description | Differentiating pseudoprogression, a radiation-induced treatment effect, from tumor progression on imaging is a substantial challenge in glioblastoma management. Unfortunately, guidelines set by the Response Assessment in Neuro-Oncology criteria are based solely on bidirectional diametric measurements of enhancement observed on T1WI and T2WI/FLAIR scans. We hypothesized that quantitative 3D shape features of the enhancing lesion on T1WI, and T2WI/FLAIR hyperintensities (together called the lesion habitat) can more comprehensively capture pathophysiologic differences across pseudoprogression and tumor recurrence, not appreciable on diametric measurements alone.
A total of 105 glioblastoma studies from 2 institutions were analyzed, consisting of a training (
= 59) and an independent test (
= 46) cohort. For every study, expert delineation of the lesion habitat (T1WI enhancing lesion and T2WI/FLAIR hyperintense perilesional region) was obtained, followed by extraction of 30 shape features capturing 14 "global" contour characteristics and 16 "local" curvature measures for every habitat region. Feature selection was used to identify most discriminative features on the training cohort, which were evaluated on the test cohort using a support vector machine classifier.
The top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion and curvedness of the T2WI/FLAIR hyperintense perilesional region. Using top features from the training cohort (training accuracy = 91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing pseudoprogression from tumor progression.
Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across pseudoprogression and tumor progression and could be used to distinguish these radiographically similar pathologies. |
doi_str_mv | 10.3174/ajnr.A5858 |
format | Article |
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A total of 105 glioblastoma studies from 2 institutions were analyzed, consisting of a training (
= 59) and an independent test (
= 46) cohort. For every study, expert delineation of the lesion habitat (T1WI enhancing lesion and T2WI/FLAIR hyperintense perilesional region) was obtained, followed by extraction of 30 shape features capturing 14 "global" contour characteristics and 16 "local" curvature measures for every habitat region. Feature selection was used to identify most discriminative features on the training cohort, which were evaluated on the test cohort using a support vector machine classifier.
The top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion and curvedness of the T2WI/FLAIR hyperintense perilesional region. Using top features from the training cohort (training accuracy = 91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing pseudoprogression from tumor progression.
Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across pseudoprogression and tumor progression and could be used to distinguish these radiographically similar pathologies.</description><identifier>ISSN: 0195-6108</identifier><identifier>EISSN: 1936-959X</identifier><identifier>DOI: 10.3174/ajnr.A5858</identifier><identifier>PMID: 30385468</identifier><language>eng</language><publisher>United States: American Society of Neuroradiology</publisher><subject>Adult ; Adult Brain ; Aged ; Brain Neoplasms - diagnostic imaging ; Brain Neoplasms - pathology ; Cohort Studies ; Diagnosis, Differential ; Disease Progression ; Female ; Glioblastoma - diagnostic imaging ; Glioblastoma - pathology ; Humans ; Imaging, Three-Dimensional - methods ; Magnetic Resonance Imaging - methods ; Male ; Middle Aged ; Neuroimaging - methods ; Radiation Injuries - diagnostic imaging ; Radiation Injuries - pathology ; Retrospective Studies ; Support Vector Machine</subject><ispartof>American journal of neuroradiology : AJNR, 2018-12, Vol.39 (12), p.2187-2193</ispartof><rights>2018 by American Journal of Neuroradiology.</rights><rights>2018 by American Journal of Neuroradiology 2018 American Journal of Neuroradiology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-f8068b2d4d7136ad46dae84c214d210bd4267259125df3ca4c6e2105f0a91f713</citedby><cites>FETCH-LOGICAL-c444t-f8068b2d4d7136ad46dae84c214d210bd4267259125df3ca4c6e2105f0a91f713</cites><orcidid>0000-0002-3068-3573 ; 0000-0001-9146-9370 ; 0000-0002-5741-0399 ; 0000-0003-2991-4730 ; 0000-0002-1150-5886 ; 0000-0003-2247-7777 ; 0000-0003-3794-9511 ; 0000-0002-5378-9434 ; 0000-0003-4291-9026 ; 0000-0001-7661-797X ; 0000-0001-9831-6000 ; 0000-0001-9477-4856 ; 0000-0002-1396-0372</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6529206/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6529206/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30385468$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ismail, M</creatorcontrib><creatorcontrib>Hill, V</creatorcontrib><creatorcontrib>Statsevych, V</creatorcontrib><creatorcontrib>Huang, R</creatorcontrib><creatorcontrib>Prasanna, P</creatorcontrib><creatorcontrib>Correa, R</creatorcontrib><creatorcontrib>Singh, G</creatorcontrib><creatorcontrib>Bera, K</creatorcontrib><creatorcontrib>Beig, N</creatorcontrib><creatorcontrib>Thawani, R</creatorcontrib><creatorcontrib>Madabhushi, A</creatorcontrib><creatorcontrib>Aahluwalia, M</creatorcontrib><creatorcontrib>Tiwari, P</creatorcontrib><title>Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study</title><title>American journal of neuroradiology : AJNR</title><addtitle>AJNR Am J Neuroradiol</addtitle><description>Differentiating pseudoprogression, a radiation-induced treatment effect, from tumor progression on imaging is a substantial challenge in glioblastoma management. Unfortunately, guidelines set by the Response Assessment in Neuro-Oncology criteria are based solely on bidirectional diametric measurements of enhancement observed on T1WI and T2WI/FLAIR scans. We hypothesized that quantitative 3D shape features of the enhancing lesion on T1WI, and T2WI/FLAIR hyperintensities (together called the lesion habitat) can more comprehensively capture pathophysiologic differences across pseudoprogression and tumor recurrence, not appreciable on diametric measurements alone.
A total of 105 glioblastoma studies from 2 institutions were analyzed, consisting of a training (
= 59) and an independent test (
= 46) cohort. For every study, expert delineation of the lesion habitat (T1WI enhancing lesion and T2WI/FLAIR hyperintense perilesional region) was obtained, followed by extraction of 30 shape features capturing 14 "global" contour characteristics and 16 "local" curvature measures for every habitat region. Feature selection was used to identify most discriminative features on the training cohort, which were evaluated on the test cohort using a support vector machine classifier.
The top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion and curvedness of the T2WI/FLAIR hyperintense perilesional region. Using top features from the training cohort (training accuracy = 91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing pseudoprogression from tumor progression.
Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across pseudoprogression and tumor progression and could be used to distinguish these radiographically similar pathologies.</description><subject>Adult</subject><subject>Adult Brain</subject><subject>Aged</subject><subject>Brain Neoplasms - diagnostic imaging</subject><subject>Brain Neoplasms - pathology</subject><subject>Cohort Studies</subject><subject>Diagnosis, Differential</subject><subject>Disease Progression</subject><subject>Female</subject><subject>Glioblastoma - diagnostic imaging</subject><subject>Glioblastoma - pathology</subject><subject>Humans</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Neuroimaging - methods</subject><subject>Radiation Injuries - diagnostic imaging</subject><subject>Radiation Injuries - pathology</subject><subject>Retrospective Studies</subject><subject>Support Vector Machine</subject><issn>0195-6108</issn><issn>1936-959X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkVFrFDEUhYModrv64g-QPIowNckkmYkPwlqtLWyxtBV8C9nJTTdlZjJNMkL_SH-vabeWCoHAueec5PIh9I6Sg5o2_JO5HuPBSrSifYEWVNWyUkL9fokWhCpRSUraPbSf0jUhRKiGvUZ7NalbwWW7QHcXWzMBPgKT5wgJB4fzFvAakg8jPjYbn03GOeBv3jmIMGZvMuCv0fgRX85DiPgshqsSfQi4GAZ8lmC2YXoml3Me5uxHwKdzn_1kohkgR9_h0_OTz3i1k5Mv1Rd5trdv0Ctn-gRvH-8l-nX0_fLwuFr__HFyuFpXHec8V64lst0wy21Da2ksl9ZAyztGuWWUbCxnsmFCUSasqzvDOwlFF44YRV3JLNGXXe80bwawXdkvml5P0Q8m3upgvP5_Mvqtvgp_tBRMMSJLwYfHghhuZkhZDz510PdmhDAnzShTom5kwbJEH3fWLoaUIrinZyjR9yD1PUj9ALKY3z__2JP1H7n6LwmMnYQ</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Ismail, M</creator><creator>Hill, V</creator><creator>Statsevych, V</creator><creator>Huang, R</creator><creator>Prasanna, P</creator><creator>Correa, R</creator><creator>Singh, G</creator><creator>Bera, K</creator><creator>Beig, N</creator><creator>Thawani, R</creator><creator>Madabhushi, A</creator><creator>Aahluwalia, M</creator><creator>Tiwari, P</creator><general>American Society of Neuroradiology</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><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3068-3573</orcidid><orcidid>https://orcid.org/0000-0001-9146-9370</orcidid><orcidid>https://orcid.org/0000-0002-5741-0399</orcidid><orcidid>https://orcid.org/0000-0003-2991-4730</orcidid><orcidid>https://orcid.org/0000-0002-1150-5886</orcidid><orcidid>https://orcid.org/0000-0003-2247-7777</orcidid><orcidid>https://orcid.org/0000-0003-3794-9511</orcidid><orcidid>https://orcid.org/0000-0002-5378-9434</orcidid><orcidid>https://orcid.org/0000-0003-4291-9026</orcidid><orcidid>https://orcid.org/0000-0001-7661-797X</orcidid><orcidid>https://orcid.org/0000-0001-9831-6000</orcidid><orcidid>https://orcid.org/0000-0001-9477-4856</orcidid><orcidid>https://orcid.org/0000-0002-1396-0372</orcidid></search><sort><creationdate>20181201</creationdate><title>Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study</title><author>Ismail, M ; Hill, V ; Statsevych, V ; Huang, R ; Prasanna, P ; Correa, R ; Singh, G ; Bera, K ; Beig, N ; Thawani, R ; Madabhushi, A ; Aahluwalia, M ; Tiwari, P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-f8068b2d4d7136ad46dae84c214d210bd4267259125df3ca4c6e2105f0a91f713</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Adult Brain</topic><topic>Aged</topic><topic>Brain Neoplasms - diagnostic imaging</topic><topic>Brain Neoplasms - pathology</topic><topic>Cohort Studies</topic><topic>Diagnosis, Differential</topic><topic>Disease Progression</topic><topic>Female</topic><topic>Glioblastoma - diagnostic imaging</topic><topic>Glioblastoma - pathology</topic><topic>Humans</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Neuroimaging - methods</topic><topic>Radiation Injuries - diagnostic imaging</topic><topic>Radiation Injuries - pathology</topic><topic>Retrospective Studies</topic><topic>Support Vector Machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ismail, M</creatorcontrib><creatorcontrib>Hill, V</creatorcontrib><creatorcontrib>Statsevych, V</creatorcontrib><creatorcontrib>Huang, R</creatorcontrib><creatorcontrib>Prasanna, P</creatorcontrib><creatorcontrib>Correa, R</creatorcontrib><creatorcontrib>Singh, G</creatorcontrib><creatorcontrib>Bera, K</creatorcontrib><creatorcontrib>Beig, N</creatorcontrib><creatorcontrib>Thawani, R</creatorcontrib><creatorcontrib>Madabhushi, A</creatorcontrib><creatorcontrib>Aahluwalia, M</creatorcontrib><creatorcontrib>Tiwari, P</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><collection>PubMed Central (Full Participant titles)</collection><jtitle>American journal of neuroradiology : AJNR</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ismail, M</au><au>Hill, V</au><au>Statsevych, V</au><au>Huang, R</au><au>Prasanna, P</au><au>Correa, R</au><au>Singh, G</au><au>Bera, K</au><au>Beig, N</au><au>Thawani, R</au><au>Madabhushi, A</au><au>Aahluwalia, M</au><au>Tiwari, P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study</atitle><jtitle>American journal of neuroradiology : AJNR</jtitle><addtitle>AJNR Am J Neuroradiol</addtitle><date>2018-12-01</date><risdate>2018</risdate><volume>39</volume><issue>12</issue><spage>2187</spage><epage>2193</epage><pages>2187-2193</pages><issn>0195-6108</issn><eissn>1936-959X</eissn><abstract>Differentiating pseudoprogression, a radiation-induced treatment effect, from tumor progression on imaging is a substantial challenge in glioblastoma management. Unfortunately, guidelines set by the Response Assessment in Neuro-Oncology criteria are based solely on bidirectional diametric measurements of enhancement observed on T1WI and T2WI/FLAIR scans. We hypothesized that quantitative 3D shape features of the enhancing lesion on T1WI, and T2WI/FLAIR hyperintensities (together called the lesion habitat) can more comprehensively capture pathophysiologic differences across pseudoprogression and tumor recurrence, not appreciable on diametric measurements alone.
A total of 105 glioblastoma studies from 2 institutions were analyzed, consisting of a training (
= 59) and an independent test (
= 46) cohort. For every study, expert delineation of the lesion habitat (T1WI enhancing lesion and T2WI/FLAIR hyperintense perilesional region) was obtained, followed by extraction of 30 shape features capturing 14 "global" contour characteristics and 16 "local" curvature measures for every habitat region. Feature selection was used to identify most discriminative features on the training cohort, which were evaluated on the test cohort using a support vector machine classifier.
The top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion and curvedness of the T2WI/FLAIR hyperintense perilesional region. Using top features from the training cohort (training accuracy = 91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing pseudoprogression from tumor progression.
Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across pseudoprogression and tumor progression and could be used to distinguish these radiographically similar pathologies.</abstract><cop>United States</cop><pub>American Society of Neuroradiology</pub><pmid>30385468</pmid><doi>10.3174/ajnr.A5858</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0002-3068-3573</orcidid><orcidid>https://orcid.org/0000-0001-9146-9370</orcidid><orcidid>https://orcid.org/0000-0002-5741-0399</orcidid><orcidid>https://orcid.org/0000-0003-2991-4730</orcidid><orcidid>https://orcid.org/0000-0002-1150-5886</orcidid><orcidid>https://orcid.org/0000-0003-2247-7777</orcidid><orcidid>https://orcid.org/0000-0003-3794-9511</orcidid><orcidid>https://orcid.org/0000-0002-5378-9434</orcidid><orcidid>https://orcid.org/0000-0003-4291-9026</orcidid><orcidid>https://orcid.org/0000-0001-7661-797X</orcidid><orcidid>https://orcid.org/0000-0001-9831-6000</orcidid><orcidid>https://orcid.org/0000-0001-9477-4856</orcidid><orcidid>https://orcid.org/0000-0002-1396-0372</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Adult Brain Aged Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Cohort Studies Diagnosis, Differential Disease Progression Female Glioblastoma - diagnostic imaging Glioblastoma - pathology Humans Imaging, Three-Dimensional - methods Magnetic Resonance Imaging - methods Male Middle Aged Neuroimaging - methods Radiation Injuries - diagnostic imaging Radiation Injuries - pathology Retrospective Studies Support Vector Machine |
title | Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study |
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