Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Earl...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Cancers 2021-06, Vol.13 (12), p.2960
Hauptverfasser: Fordham, Austin-John, Hacherl, Caitlin-Craft, Patel, Neal, Jones, Keri, Myers, Brandon, Abraham, Mickey, Gendreau, Julian
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 12
container_start_page 2960
container_title Cancers
container_volume 13
creator Fordham, Austin-John
Hacherl, Caitlin-Craft
Patel, Neal
Jones, Keri
Myers, Brandon
Abraham, Mickey
Gendreau, Julian
description Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient’s clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.
doi_str_mv 10.3390/cancers13122960
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8231515</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2544961277</sourcerecordid><originalsourceid>FETCH-LOGICAL-c398t-c9512fc5fb1f12340de52fded2544cd762d30a4cc09a69257896d229195bc2873</originalsourceid><addsrcrecordid>eNpdkb1vFDEQxS1ERKIkNa0lGppL_LH2rimQjgNCpIsoILXls8cXR7v2YXsj0eVPx6tECOJmLL2nN7-ZQegtJRecK3JpTbSQC-WUMSXJK3TCSM9WUqru9T__Y3Reyj1pj3Pay_4NOuYdVYoKeoIePwfvIUOswdQQ9_hqDGk3mlLTZAr2OU34RxpDNfk3_pRNiPgGapNNgfIBryO-PThTAaeI6x3gzZyXMLwNFbKpc26Kx2v3sLA6fD2Z_dLlJjnTQgOUM3TkzVjg_LmeotuvX35uvq2236-uN-vtynI11JVVgjJvhd9RTxnviAPBvAPHRNdZ10vmODGdtUQZqZjoByVdWwtVYmfZ0PNT9PEp9zDvJnC2QWYz6kMOUxtNJxP0_0oMd3qfHvTAeNuUaAHvnwNy-jVDqXoKxcI4mghpLrqBDB2VYlDN-u6F9T7NObbxFlenJGX9QnT55LI5lZLB_4WhRC8H1i8OzP8AL4GamA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2544961277</pqid></control><display><type>article</type><title>Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities</title><source>PubMed Central Open Access</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Fordham, Austin-John ; Hacherl, Caitlin-Craft ; Patel, Neal ; Jones, Keri ; Myers, Brandon ; Abraham, Mickey ; Gendreau, Julian</creator><creatorcontrib>Fordham, Austin-John ; Hacherl, Caitlin-Craft ; Patel, Neal ; Jones, Keri ; Myers, Brandon ; Abraham, Mickey ; Gendreau, Julian</creatorcontrib><description>Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient’s clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers13122960</identifier><identifier>PMID: 34199151</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Blood ; Blood flow ; Brain cancer ; Cerebral blood flow ; Choline ; Clinical outcomes ; Creatine ; Diagnosis ; Disease ; Edema ; Learning algorithms ; Machine learning ; Magnetic fields ; Magnetic resonance imaging ; Magnetic resonance spectroscopy ; Metastases ; Metastasis ; N-Acetylaspartate ; Neuroimaging ; Older people ; Radiation therapy ; Radiomics ; Review ; Tumors</subject><ispartof>Cancers, 2021-06, Vol.13 (12), p.2960</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c398t-c9512fc5fb1f12340de52fded2544cd762d30a4cc09a69257896d229195bc2873</citedby><cites>FETCH-LOGICAL-c398t-c9512fc5fb1f12340de52fded2544cd762d30a4cc09a69257896d229195bc2873</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231515/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231515/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids></links><search><creatorcontrib>Fordham, Austin-John</creatorcontrib><creatorcontrib>Hacherl, Caitlin-Craft</creatorcontrib><creatorcontrib>Patel, Neal</creatorcontrib><creatorcontrib>Jones, Keri</creatorcontrib><creatorcontrib>Myers, Brandon</creatorcontrib><creatorcontrib>Abraham, Mickey</creatorcontrib><creatorcontrib>Gendreau, Julian</creatorcontrib><title>Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities</title><title>Cancers</title><description>Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient’s clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.</description><subject>Accuracy</subject><subject>Blood</subject><subject>Blood flow</subject><subject>Brain cancer</subject><subject>Cerebral blood flow</subject><subject>Choline</subject><subject>Clinical outcomes</subject><subject>Creatine</subject><subject>Diagnosis</subject><subject>Disease</subject><subject>Edema</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Magnetic fields</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic resonance spectroscopy</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>N-Acetylaspartate</subject><subject>Neuroimaging</subject><subject>Older people</subject><subject>Radiation therapy</subject><subject>Radiomics</subject><subject>Review</subject><subject>Tumors</subject><issn>2072-6694</issn><issn>2072-6694</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkb1vFDEQxS1ERKIkNa0lGppL_LH2rimQjgNCpIsoILXls8cXR7v2YXsj0eVPx6tECOJmLL2nN7-ZQegtJRecK3JpTbSQC-WUMSXJK3TCSM9WUqru9T__Y3Reyj1pj3Pay_4NOuYdVYoKeoIePwfvIUOswdQQ9_hqDGk3mlLTZAr2OU34RxpDNfk3_pRNiPgGapNNgfIBryO-PThTAaeI6x3gzZyXMLwNFbKpc26Kx2v3sLA6fD2Z_dLlJjnTQgOUM3TkzVjg_LmeotuvX35uvq2236-uN-vtynI11JVVgjJvhd9RTxnviAPBvAPHRNdZ10vmODGdtUQZqZjoByVdWwtVYmfZ0PNT9PEp9zDvJnC2QWYz6kMOUxtNJxP0_0oMd3qfHvTAeNuUaAHvnwNy-jVDqXoKxcI4mghpLrqBDB2VYlDN-u6F9T7NObbxFlenJGX9QnT55LI5lZLB_4WhRC8H1i8OzP8AL4GamA</recordid><startdate>20210613</startdate><enddate>20210613</enddate><creator>Fordham, Austin-John</creator><creator>Hacherl, Caitlin-Craft</creator><creator>Patel, Neal</creator><creator>Jones, Keri</creator><creator>Myers, Brandon</creator><creator>Abraham, Mickey</creator><creator>Gendreau, Julian</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T5</scope><scope>7TO</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20210613</creationdate><title>Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities</title><author>Fordham, Austin-John ; Hacherl, Caitlin-Craft ; Patel, Neal ; Jones, Keri ; Myers, Brandon ; Abraham, Mickey ; Gendreau, Julian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c398t-c9512fc5fb1f12340de52fded2544cd762d30a4cc09a69257896d229195bc2873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Blood</topic><topic>Blood flow</topic><topic>Brain cancer</topic><topic>Cerebral blood flow</topic><topic>Choline</topic><topic>Clinical outcomes</topic><topic>Creatine</topic><topic>Diagnosis</topic><topic>Disease</topic><topic>Edema</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Magnetic fields</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic resonance spectroscopy</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>N-Acetylaspartate</topic><topic>Neuroimaging</topic><topic>Older people</topic><topic>Radiation therapy</topic><topic>Radiomics</topic><topic>Review</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fordham, Austin-John</creatorcontrib><creatorcontrib>Hacherl, Caitlin-Craft</creatorcontrib><creatorcontrib>Patel, Neal</creatorcontrib><creatorcontrib>Jones, Keri</creatorcontrib><creatorcontrib>Myers, Brandon</creatorcontrib><creatorcontrib>Abraham, Mickey</creatorcontrib><creatorcontrib>Gendreau, Julian</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Immunology Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cancers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fordham, Austin-John</au><au>Hacherl, Caitlin-Craft</au><au>Patel, Neal</au><au>Jones, Keri</au><au>Myers, Brandon</au><au>Abraham, Mickey</au><au>Gendreau, Julian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities</atitle><jtitle>Cancers</jtitle><date>2021-06-13</date><risdate>2021</risdate><volume>13</volume><issue>12</issue><spage>2960</spage><pages>2960-</pages><issn>2072-6694</issn><eissn>2072-6694</eissn><abstract>Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient’s clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>34199151</pmid><doi>10.3390/cancers13122960</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2072-6694
ispartof Cancers, 2021-06, Vol.13 (12), p.2960
issn 2072-6694
2072-6694
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8231515
source PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals; PubMed Central
subjects Accuracy
Blood
Blood flow
Brain cancer
Cerebral blood flow
Choline
Clinical outcomes
Creatine
Diagnosis
Disease
Edema
Learning algorithms
Machine learning
Magnetic fields
Magnetic resonance imaging
Magnetic resonance spectroscopy
Metastases
Metastasis
N-Acetylaspartate
Neuroimaging
Older people
Radiation therapy
Radiomics
Review
Tumors
title Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T15%3A15%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Differentiating%20Glioblastomas%20from%20Solitary%20Brain%20Metastases:%20An%20Update%20on%20the%20Current%20Literature%20of%20Advanced%20Imaging%20Modalities&rft.jtitle=Cancers&rft.au=Fordham,%20Austin-John&rft.date=2021-06-13&rft.volume=13&rft.issue=12&rft.spage=2960&rft.pages=2960-&rft.issn=2072-6694&rft.eissn=2072-6694&rft_id=info:doi/10.3390/cancers13122960&rft_dat=%3Cproquest_pubme%3E2544961277%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2544961277&rft_id=info:pmid/34199151&rfr_iscdi=true