MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis
Background Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially. Purpose To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma. Study T...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2019-08, Vol.50 (2), p.560-572 |
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| creator | Suh, Chong Hyun Kim, Ho Sung Jung, Seung Chai Park, Ji Eun Choi, Choong Gon Kim, Sang Joon |
| description | Background
Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.
Purpose
To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.
Study Type
Systematic review and meta‐analysis.
Subjects
Ovid‐MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."
Field Strength/Sequence
Patients underwent at least one MRI sequence including diffusion‐weighted imaging (DWI), dynamic susceptibility‐weighted contrast‐enhanced imaging (DSC), dynamic contrast‐enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility‐weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.
Assessment
Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies‐2 tool.
Statistical Tests
Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta‐regression was performed.
Results
Twenty‐two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87–93%) and specificity of 89% (95% CI, 85–93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90–0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89–97%] and specificity of 91% [95% CI, 86–96%]). Heterogeneity was only detected in specificity (I2 = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.
Data Conclusion
MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2019;50:560–572. |
| doi_str_mv | 10.1002/jmri.26602 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2179344624</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2179344624</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4232-870f783f9e1477bf36fe7da1a0e81cb1da9ef3ee4b916db2a48a6f3cd1e3cd473</originalsourceid><addsrcrecordid>eNp9kc1O3DAQx60KVD7aSx-gstQLQgr1V-KEG0ItHwJVqtpzNEnGW2-deGtnQbnxCBx4Qp4EL7tw6IGLx5r5zX_s-RPyibMjzpj4Ou-DPRJFwcQ7sstzITKRl8VWurNcZrxkeofsxThnjFWVyt-THckKqUsld8nD9c8LCpEC7SzMBh9H29LG-h7CXwzU-JAKxmDAYbQw2mFGF8Gm6kTblArg6IDhxi8jjVMcsadu6hd_Uj81wfd05qxvHMQxZY7pyQaC1ZSANxZvKQwd7XGEx7t7GMBN0cYPZNuAi_hxE_fJ7-_ffp2eZ1c_zi5OT66yVgkpslIzo0tpKuRK68bIwqDugAPDkrcN76BCIxFVU_GiawSoEgoj245jOpSW--RgrbsI_t8S41j3NrboHAyYflQLriupVCFUQr_8h879MqT3JkrkafVlJVmiDtdUG3yMAU29WVbNWb3yql55VT97leDPG8ll02P3ir6YkwC-Bm6tw-kNqfoyubgWfQKHY6OT</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2256028930</pqid></control><display><type>article</type><title>MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis</title><source>Wiley Free Content</source><source>Wiley Online Library All Journals</source><creator>Suh, Chong Hyun ; Kim, Ho Sung ; Jung, Seung Chai ; Park, Ji Eun ; Choi, Choong Gon ; Kim, Sang Joon</creator><creatorcontrib>Suh, Chong Hyun ; Kim, Ho Sung ; Jung, Seung Chai ; Park, Ji Eun ; Choi, Choong Gon ; Kim, Sang Joon</creatorcontrib><description>Background
Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.
Purpose
To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.
Study Type
Systematic review and meta‐analysis.
Subjects
Ovid‐MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."
Field Strength/Sequence
Patients underwent at least one MRI sequence including diffusion‐weighted imaging (DWI), dynamic susceptibility‐weighted contrast‐enhanced imaging (DSC), dynamic contrast‐enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility‐weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.
Assessment
Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies‐2 tool.
Statistical Tests
Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta‐regression was performed.
Results
Twenty‐two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87–93%) and specificity of 89% (95% CI, 85–93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90–0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89–97%] and specificity of 91% [95% CI, 86–96%]). Heterogeneity was only detected in specificity (I2 = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.
Data Conclusion
MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2019;50:560–572.</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.26602</identifier><identifier>PMID: 30637843</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Biomarkers ; Brain cancer ; Central nervous system ; Confidence intervals ; Diagnostic software ; Diagnostic systems ; diffusion ; Field strength ; Glioblastoma ; Heterogeneity ; Lymphoma ; Magnetic fields ; Magnetic permeability ; Magnetic resonance imaging ; Magnetic resonance spectroscopy ; Medical imaging ; Meta-analysis ; Nervous system ; perfusion ; Quality assessment ; Quality control ; Regression analysis ; Sensitivity ; Spin dynamics ; Spin labeling ; Statistical analysis ; Statistical tests ; Systematic review</subject><ispartof>Journal of magnetic resonance imaging, 2019-08, Vol.50 (2), p.560-572</ispartof><rights>2019 International Society for Magnetic Resonance in Medicine</rights><rights>2019 International Society for Magnetic Resonance in Medicine.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4232-870f783f9e1477bf36fe7da1a0e81cb1da9ef3ee4b916db2a48a6f3cd1e3cd473</citedby><cites>FETCH-LOGICAL-c4232-870f783f9e1477bf36fe7da1a0e81cb1da9ef3ee4b916db2a48a6f3cd1e3cd473</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.26602$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.26602$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30637843$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Suh, Chong Hyun</creatorcontrib><creatorcontrib>Kim, Ho Sung</creatorcontrib><creatorcontrib>Jung, Seung Chai</creatorcontrib><creatorcontrib>Park, Ji Eun</creatorcontrib><creatorcontrib>Choi, Choong Gon</creatorcontrib><creatorcontrib>Kim, Sang Joon</creatorcontrib><title>MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.
Purpose
To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.
Study Type
Systematic review and meta‐analysis.
Subjects
Ovid‐MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."
Field Strength/Sequence
Patients underwent at least one MRI sequence including diffusion‐weighted imaging (DWI), dynamic susceptibility‐weighted contrast‐enhanced imaging (DSC), dynamic contrast‐enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility‐weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.
Assessment
Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies‐2 tool.
Statistical Tests
Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta‐regression was performed.
Results
Twenty‐two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87–93%) and specificity of 89% (95% CI, 85–93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90–0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89–97%] and specificity of 91% [95% CI, 86–96%]). Heterogeneity was only detected in specificity (I2 = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.
Data Conclusion
MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2019;50:560–572.</description><subject>Biomarkers</subject><subject>Brain cancer</subject><subject>Central nervous system</subject><subject>Confidence intervals</subject><subject>Diagnostic software</subject><subject>Diagnostic systems</subject><subject>diffusion</subject><subject>Field strength</subject><subject>Glioblastoma</subject><subject>Heterogeneity</subject><subject>Lymphoma</subject><subject>Magnetic fields</subject><subject>Magnetic permeability</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic resonance spectroscopy</subject><subject>Medical imaging</subject><subject>Meta-analysis</subject><subject>Nervous system</subject><subject>perfusion</subject><subject>Quality assessment</subject><subject>Quality control</subject><subject>Regression analysis</subject><subject>Sensitivity</subject><subject>Spin dynamics</subject><subject>Spin labeling</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Systematic review</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kc1O3DAQx60KVD7aSx-gstQLQgr1V-KEG0ItHwJVqtpzNEnGW2-deGtnQbnxCBx4Qp4EL7tw6IGLx5r5zX_s-RPyibMjzpj4Ou-DPRJFwcQ7sstzITKRl8VWurNcZrxkeofsxThnjFWVyt-THckKqUsld8nD9c8LCpEC7SzMBh9H29LG-h7CXwzU-JAKxmDAYbQw2mFGF8Gm6kTblArg6IDhxi8jjVMcsadu6hd_Uj81wfd05qxvHMQxZY7pyQaC1ZSANxZvKQwd7XGEx7t7GMBN0cYPZNuAi_hxE_fJ7-_ffp2eZ1c_zi5OT66yVgkpslIzo0tpKuRK68bIwqDugAPDkrcN76BCIxFVU_GiawSoEgoj245jOpSW--RgrbsI_t8S41j3NrboHAyYflQLriupVCFUQr_8h879MqT3JkrkafVlJVmiDtdUG3yMAU29WVbNWb3yql55VT97leDPG8ll02P3ir6YkwC-Bm6tw-kNqfoyubgWfQKHY6OT</recordid><startdate>201908</startdate><enddate>201908</enddate><creator>Suh, Chong Hyun</creator><creator>Kim, Ho Sung</creator><creator>Jung, Seung Chai</creator><creator>Park, Ji Eun</creator><creator>Choi, Choong Gon</creator><creator>Kim, Sang Joon</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>201908</creationdate><title>MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis</title><author>Suh, Chong Hyun ; Kim, Ho Sung ; Jung, Seung Chai ; Park, Ji Eun ; Choi, Choong Gon ; Kim, Sang Joon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4232-870f783f9e1477bf36fe7da1a0e81cb1da9ef3ee4b916db2a48a6f3cd1e3cd473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Biomarkers</topic><topic>Brain cancer</topic><topic>Central nervous system</topic><topic>Confidence intervals</topic><topic>Diagnostic software</topic><topic>Diagnostic systems</topic><topic>diffusion</topic><topic>Field strength</topic><topic>Glioblastoma</topic><topic>Heterogeneity</topic><topic>Lymphoma</topic><topic>Magnetic fields</topic><topic>Magnetic permeability</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic resonance spectroscopy</topic><topic>Medical imaging</topic><topic>Meta-analysis</topic><topic>Nervous system</topic><topic>perfusion</topic><topic>Quality assessment</topic><topic>Quality control</topic><topic>Regression analysis</topic><topic>Sensitivity</topic><topic>Spin dynamics</topic><topic>Spin labeling</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Systematic review</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Suh, Chong Hyun</creatorcontrib><creatorcontrib>Kim, Ho Sung</creatorcontrib><creatorcontrib>Jung, Seung Chai</creatorcontrib><creatorcontrib>Park, Ji Eun</creatorcontrib><creatorcontrib>Choi, Choong Gon</creatorcontrib><creatorcontrib>Kim, Sang Joon</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Suh, Chong Hyun</au><au>Kim, Ho Sung</au><au>Jung, Seung Chai</au><au>Park, Ji Eun</au><au>Choi, Choong Gon</au><au>Kim, Sang Joon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2019-08</date><risdate>2019</risdate><volume>50</volume><issue>2</issue><spage>560</spage><epage>572</epage><pages>560-572</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Background
Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.
Purpose
To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.
Study Type
Systematic review and meta‐analysis.
Subjects
Ovid‐MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."
Field Strength/Sequence
Patients underwent at least one MRI sequence including diffusion‐weighted imaging (DWI), dynamic susceptibility‐weighted contrast‐enhanced imaging (DSC), dynamic contrast‐enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility‐weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.
Assessment
Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies‐2 tool.
Statistical Tests
Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta‐regression was performed.
Results
Twenty‐two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87–93%) and specificity of 89% (95% CI, 85–93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90–0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89–97%] and specificity of 91% [95% CI, 86–96%]). Heterogeneity was only detected in specificity (I2 = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.
Data Conclusion
MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.
Level of Evidence: 3
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2019;50:560–572.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>30637843</pmid><doi>10.1002/jmri.26602</doi><tpages>13</tpages></addata></record> |
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| source | Wiley Free Content; Wiley Online Library All Journals |
| subjects | Biomarkers Brain cancer Central nervous system Confidence intervals Diagnostic software Diagnostic systems diffusion Field strength Glioblastoma Heterogeneity Lymphoma Magnetic fields Magnetic permeability Magnetic resonance imaging Magnetic resonance spectroscopy Medical imaging Meta-analysis Nervous system perfusion Quality assessment Quality control Regression analysis Sensitivity Spin dynamics Spin labeling Statistical analysis Statistical tests Systematic review |
| title | MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta‐analysis |
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