Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes
Objectives Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using 18 F-fluorodeox...
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| Veröffentlicht in: | European radiology 2021-12, Vol.31 (12), p.9335-9345 |
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| creator | Li, Yuting Feng, Jianhua Zhang, Teng Shi, Kexin Ding, Yao Zhang, Xiaohui Jin, Chentao Pan, Jiayue Xue, Le Liao, Yi Wang, Xiawan Zhuo, Cheng Zhang, Hong Tian, Mei |
| description | Objectives
Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using
18
F-fluorodeoxyglucose positron emission tomography ([
18
F]FDG PET) imaging, and explore whether these differences can help distinguish.
Methods
Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [
18
F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features.
Results
The |AI| was found significantly higher in atypical BECTS than in typical BECTS (
p
= 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70,
p
= 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models.
Conclusions
Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS.
Key Points
• Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice.
• Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex.
• The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance. |
| doi_str_mv | 10.1007/s00330-021-08051-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_webof</sourceid><recordid>TN_cdi_proquest_journals_2596811965</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2534612840</sourcerecordid><originalsourceid>FETCH-LOGICAL-c352t-f0e033d801e4b02137269e70752890ba55a6c49653887ecf38f3db4796bdb82d3</originalsourceid><addsrcrecordid>eNqNkUtv1DAUhS0EotOBP8AqEhskFLh-JfYSRlCQKnXTriPHuZlxydjBdlTNv6-H8JBYIDa-tvSd63N0CHlF4R0FaN8nAM6hBkZrUCDL-YRsqOCspqDEU7IBzVXdai0uyGVK9wCgqWifkwsuQBYJ35DwMRrnqyNm04fJ2coeTDQ2Y3QpO5uq4Tz9fnHpUEaVT7OzZqqMHyrz69Gjd3tf4ewmnNOpenD5UFn0OYY643EOsUBpdt8wvSDPRjMlfPlzbsnd50-3uy_19c3V192H69pyyXI9ApZsgwKKoi8BecsajS20kikNvZHSNFboRnKlWrQjVyMfetHqph96xQa-JW_WvXMM3xdMuTu6ZHGajMewpI5JLhrKlICCvv4LvQ9L9MVdoXSjKD3_syVspWwMKUUcuzm6o4mnjkJ3rqNb6-iK2-5HHd15tVpFD9iHMVmH3uJvYemjkVLLVpUbsJ3LJrvgd2HxuUjf_r-00HylUyH8HuOfDP-w9wgVGq0a</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2596811965</pqid></control><display><type>article</type><title>Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes</title><source>SpringerNature Journals</source><source>Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><creator>Li, Yuting ; Feng, Jianhua ; Zhang, Teng ; Shi, Kexin ; Ding, Yao ; Zhang, Xiaohui ; Jin, Chentao ; Pan, Jiayue ; Xue, Le ; Liao, Yi ; Wang, Xiawan ; Zhuo, Cheng ; Zhang, Hong ; Tian, Mei</creator><creatorcontrib>Li, Yuting ; Feng, Jianhua ; Zhang, Teng ; Shi, Kexin ; Ding, Yao ; Zhang, Xiaohui ; Jin, Chentao ; Pan, Jiayue ; Xue, Le ; Liao, Yi ; Wang, Xiawan ; Zhuo, Cheng ; Zhang, Hong ; Tian, Mei</creatorcontrib><description>Objectives
Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using
18
F-fluorodeoxyglucose positron emission tomography ([
18
F]FDG PET) imaging, and explore whether these differences can help distinguish.
Methods
Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [
18
F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features.
Results
The |AI| was found significantly higher in atypical BECTS than in typical BECTS (
p
= 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70,
p
= 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models.
Conclusions
Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS.
Key Points
• Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice.
• Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex.
• The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-021-08051-0</identifier><identifier>PMID: 34050803</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Classification ; Cortex (parietal) ; Cortex (temporal) ; Diagnostic Radiology ; Epilepsy ; Fluorine isotopes ; Glucose ; Imaging ; Internal Medicine ; Interventional Radiology ; Life Sciences & Biomedicine ; Medicine ; Medicine & Public Health ; Metabolism ; Neuroimaging ; Neuroradiology ; Nuclear Medicine ; Patients ; Positron emission ; Positron emission tomography ; Radiology ; Radiology, Nuclear Medicine & Medical Imaging ; Regression analysis ; Regression models ; Science & Technology ; Statistical analysis ; Tomography ; Ultrasound</subject><ispartof>European radiology, 2021-12, Vol.31 (12), p.9335-9345</ispartof><rights>European Society of Radiology 2021</rights><rights>European Society of Radiology 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>8</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000655957800002</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c352t-f0e033d801e4b02137269e70752890ba55a6c49653887ecf38f3db4796bdb82d3</citedby><cites>FETCH-LOGICAL-c352t-f0e033d801e4b02137269e70752890ba55a6c49653887ecf38f3db4796bdb82d3</cites><orcidid>0000-0003-4073-3500 ; 0000-0003-3372-9722 ; 0000-0002-0978-2876 ; 0000-0002-4238-1564</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-021-08051-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-021-08051-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,39263,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Li, Yuting</creatorcontrib><creatorcontrib>Feng, Jianhua</creatorcontrib><creatorcontrib>Zhang, Teng</creatorcontrib><creatorcontrib>Shi, Kexin</creatorcontrib><creatorcontrib>Ding, Yao</creatorcontrib><creatorcontrib>Zhang, Xiaohui</creatorcontrib><creatorcontrib>Jin, Chentao</creatorcontrib><creatorcontrib>Pan, Jiayue</creatorcontrib><creatorcontrib>Xue, Le</creatorcontrib><creatorcontrib>Liao, Yi</creatorcontrib><creatorcontrib>Wang, Xiawan</creatorcontrib><creatorcontrib>Zhuo, Cheng</creatorcontrib><creatorcontrib>Zhang, Hong</creatorcontrib><creatorcontrib>Tian, Mei</creatorcontrib><title>Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>EUR RADIOL</addtitle><description>Objectives
Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using
18
F-fluorodeoxyglucose positron emission tomography ([
18
F]FDG PET) imaging, and explore whether these differences can help distinguish.
Methods
Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [
18
F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features.
Results
The |AI| was found significantly higher in atypical BECTS than in typical BECTS (
p
= 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70,
p
= 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models.
Conclusions
Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS.
Key Points
• Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice.
• Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex.
• The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance.</description><subject>Classification</subject><subject>Cortex (parietal)</subject><subject>Cortex (temporal)</subject><subject>Diagnostic Radiology</subject><subject>Epilepsy</subject><subject>Fluorine isotopes</subject><subject>Glucose</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Life Sciences & Biomedicine</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metabolism</subject><subject>Neuroimaging</subject><subject>Neuroradiology</subject><subject>Nuclear Medicine</subject><subject>Patients</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>Radiology</subject><subject>Radiology, Nuclear Medicine & Medical Imaging</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Science & Technology</subject><subject>Statistical analysis</subject><subject>Tomography</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkUtv1DAUhS0EotOBP8AqEhskFLh-JfYSRlCQKnXTriPHuZlxydjBdlTNv6-H8JBYIDa-tvSd63N0CHlF4R0FaN8nAM6hBkZrUCDL-YRsqOCspqDEU7IBzVXdai0uyGVK9wCgqWifkwsuQBYJ35DwMRrnqyNm04fJ2coeTDQ2Y3QpO5uq4Tz9fnHpUEaVT7OzZqqMHyrz69Gjd3tf4ewmnNOpenD5UFn0OYY643EOsUBpdt8wvSDPRjMlfPlzbsnd50-3uy_19c3V192H69pyyXI9ApZsgwKKoi8BecsajS20kikNvZHSNFboRnKlWrQjVyMfetHqph96xQa-JW_WvXMM3xdMuTu6ZHGajMewpI5JLhrKlICCvv4LvQ9L9MVdoXSjKD3_syVspWwMKUUcuzm6o4mnjkJ3rqNb6-iK2-5HHd15tVpFD9iHMVmH3uJvYemjkVLLVpUbsJ3LJrvgd2HxuUjf_r-00HylUyH8HuOfDP-w9wgVGq0a</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Li, Yuting</creator><creator>Feng, Jianhua</creator><creator>Zhang, Teng</creator><creator>Shi, Kexin</creator><creator>Ding, Yao</creator><creator>Zhang, Xiaohui</creator><creator>Jin, Chentao</creator><creator>Pan, Jiayue</creator><creator>Xue, Le</creator><creator>Liao, Yi</creator><creator>Wang, Xiawan</creator><creator>Zhuo, Cheng</creator><creator>Zhang, Hong</creator><creator>Tian, Mei</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature</general><general>Springer Nature B.V</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4073-3500</orcidid><orcidid>https://orcid.org/0000-0003-3372-9722</orcidid><orcidid>https://orcid.org/0000-0002-0978-2876</orcidid><orcidid>https://orcid.org/0000-0002-4238-1564</orcidid></search><sort><creationdate>20211201</creationdate><title>Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes</title><author>Li, Yuting ; Feng, Jianhua ; Zhang, Teng ; Shi, Kexin ; Ding, Yao ; Zhang, Xiaohui ; Jin, Chentao ; Pan, Jiayue ; Xue, Le ; Liao, Yi ; Wang, Xiawan ; Zhuo, Cheng ; Zhang, Hong ; Tian, Mei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c352t-f0e033d801e4b02137269e70752890ba55a6c49653887ecf38f3db4796bdb82d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Classification</topic><topic>Cortex (parietal)</topic><topic>Cortex (temporal)</topic><topic>Diagnostic Radiology</topic><topic>Epilepsy</topic><topic>Fluorine isotopes</topic><topic>Glucose</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Life Sciences & Biomedicine</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metabolism</topic><topic>Neuroimaging</topic><topic>Neuroradiology</topic><topic>Nuclear Medicine</topic><topic>Patients</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Radiology</topic><topic>Radiology, Nuclear Medicine & Medical Imaging</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Science & Technology</topic><topic>Statistical analysis</topic><topic>Tomography</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yuting</creatorcontrib><creatorcontrib>Feng, Jianhua</creatorcontrib><creatorcontrib>Zhang, Teng</creatorcontrib><creatorcontrib>Shi, Kexin</creatorcontrib><creatorcontrib>Ding, Yao</creatorcontrib><creatorcontrib>Zhang, Xiaohui</creatorcontrib><creatorcontrib>Jin, Chentao</creatorcontrib><creatorcontrib>Pan, Jiayue</creatorcontrib><creatorcontrib>Xue, Le</creatorcontrib><creatorcontrib>Liao, Yi</creatorcontrib><creatorcontrib>Wang, Xiawan</creatorcontrib><creatorcontrib>Zhuo, Cheng</creatorcontrib><creatorcontrib>Zhang, Hong</creatorcontrib><creatorcontrib>Tian, Mei</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest 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Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yuting</au><au>Feng, Jianhua</au><au>Zhang, Teng</au><au>Shi, Kexin</au><au>Ding, Yao</au><au>Zhang, Xiaohui</au><au>Jin, Chentao</au><au>Pan, Jiayue</au><au>Xue, Le</au><au>Liao, Yi</au><au>Wang, Xiawan</au><au>Zhuo, Cheng</au><au>Zhang, Hong</au><au>Tian, Mei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><stitle>EUR RADIOL</stitle><date>2021-12-01</date><risdate>2021</risdate><volume>31</volume><issue>12</issue><spage>9335</spage><epage>9345</epage><pages>9335-9345</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using
18
F-fluorodeoxyglucose positron emission tomography ([
18
F]FDG PET) imaging, and explore whether these differences can help distinguish.
Methods
Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [
18
F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features.
Results
The |AI| was found significantly higher in atypical BECTS than in typical BECTS (
p
= 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70,
p
= 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models.
Conclusions
Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS.
Key Points
• Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice.
• Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex.
• The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34050803</pmid><doi>10.1007/s00330-021-08051-0</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-4073-3500</orcidid><orcidid>https://orcid.org/0000-0003-3372-9722</orcidid><orcidid>https://orcid.org/0000-0002-0978-2876</orcidid><orcidid>https://orcid.org/0000-0002-4238-1564</orcidid></addata></record> |
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| source | SpringerNature Journals; Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /> |
| subjects | Classification Cortex (parietal) Cortex (temporal) Diagnostic Radiology Epilepsy Fluorine isotopes Glucose Imaging Internal Medicine Interventional Radiology Life Sciences & Biomedicine Medicine Medicine & Public Health Metabolism Neuroimaging Neuroradiology Nuclear Medicine Patients Positron emission Positron emission tomography Radiology Radiology, Nuclear Medicine & Medical Imaging Regression analysis Regression models Science & Technology Statistical analysis Tomography Ultrasound |
| title | Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes |
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