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...
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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. |
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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 ; 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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> |
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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 |
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