T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma
Objective To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma. Methods This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping...
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| Veröffentlicht in: | European radiology 2023-01, Vol.33 (1), p.258-269 |
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| creator | Cao, Tiexin Jiang, Rifeng Zheng, Lingmin Zhang, Rufei Chen, Xiaodan Wang, Zongmeng Jiang, Peirong Chen, Yilin Zhong, Tianjin Chen, Hu Wu, PuYeh Xue, Yunjing Lin, Lin |
| description | Objective
To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.
Methods
This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman’s rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.
Results
High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (
p =
0.001–0.009), lower minimum, and C10 of ADC (
p =
0.013–0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).
Conclusion
T1 and ADC histogram parameters may represent an
in vivo
biomarker for predicting the grade, subtype, and proliferative activity of meningioma.
Key Points
• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma.
• The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas. |
| doi_str_mv | 10.1007/s00330-022-09026-5 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2702192734</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2702192734</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-8f39c56b556f20fe4efb23ef1555579f0287fa7e8ea087c59f45e602a3cd491c3</originalsourceid><addsrcrecordid>eNp9kcuO1DAQRSMEYoaBH2CBLLFhQaD8ipPlqIeXNBKbYW05SbnHQycOttNSfwG_TTU9PMQCL-ySfO6tUt2qes7hDQcwbzOAlFCDEDV0IJpaP6jOuZKi5tCqh3_VZ9WTnO8AoOPKPK7OpO60NFKdV99vOHPzyC6vNuw25BK3yU1scXRjwZTZ5A6sR2JYmNk-7CPrQ5xc-oqJ-ZjYknAMQwnzlpVbZCQf8TXLa18OCxVH7yXFXfCYXAl7ciJ4H8qBRc8mnEl49HtaPfJul_HZ_XtRfXn_7mbzsb7-_OHT5vK6HqTRpW697Abd9Fo3XoBHhb4XEj3XdEznQbTGO4MtOmjNoDuvNDYgnBxG1fFBXlSvTr401LcVc7FTyAPudm7GuGYrDAjeCdoNoS__Qe_immaajiitGtW1qiFKnKghxZwTerukQPs5WA72GJM9xWQpJvszJqtJ9OLeeu0nHH9LfuVCgDwBmb7mLaY_vf9j-wMB2p3G</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2754649846</pqid></control><display><type>article</type><title>T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Cao, Tiexin ; Jiang, Rifeng ; Zheng, Lingmin ; Zhang, Rufei ; Chen, Xiaodan ; Wang, Zongmeng ; Jiang, Peirong ; Chen, Yilin ; Zhong, Tianjin ; Chen, Hu ; Wu, PuYeh ; Xue, Yunjing ; Lin, Lin</creator><creatorcontrib>Cao, Tiexin ; Jiang, Rifeng ; Zheng, Lingmin ; Zhang, Rufei ; Chen, Xiaodan ; Wang, Zongmeng ; Jiang, Peirong ; Chen, Yilin ; Zhong, Tianjin ; Chen, Hu ; Wu, PuYeh ; Xue, Yunjing ; Lin, Lin</creatorcontrib><description>Objective
To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.
Methods
This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman’s rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.
Results
High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (
p =
0.001–0.009), lower minimum, and C10 of ADC (
p =
0.013–0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).
Conclusion
T1 and ADC histogram parameters may represent an
in vivo
biomarker for predicting the grade, subtype, and proliferative activity of meningioma.
Key Points
• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma.
• The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-022-09026-5</identifier><identifier>PMID: 35953734</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biomarkers ; Brain cancer ; Correlation coefficient ; Correlation coefficients ; Diagnostic Radiology ; Diagnostic systems ; Diffusion coefficient ; Diffusion Magnetic Resonance Imaging - methods ; Edema ; Feature extraction ; Histograms ; Humans ; Imaging ; In vivo methods and tests ; Internal Medicine ; Interventional Radiology ; Ki-67 Antigen - metabolism ; Kurtosis ; Mapping ; Mathematical analysis ; Mean ; Medicine ; Medicine & Public Health ; Meningeal Neoplasms - diagnostic imaging ; Meningeal Neoplasms - pathology ; Meningioma ; Meningioma - diagnostic imaging ; Meningioma - pathology ; Neuroradiology ; Oncology ; Parameter identification ; Prospective Studies ; Quality ; Radiology ; Regression analysis ; Retrospective Studies ; ROC Curve ; Tumors ; Ultrasound</subject><ispartof>European radiology, 2023-01, Vol.33 (1), p.258-269</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2022. The Author(s), under exclusive licence to European Society of Radiology.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-8f39c56b556f20fe4efb23ef1555579f0287fa7e8ea087c59f45e602a3cd491c3</citedby><cites>FETCH-LOGICAL-c375t-8f39c56b556f20fe4efb23ef1555579f0287fa7e8ea087c59f45e602a3cd491c3</cites><orcidid>0000-0003-2782-1848</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/s00330-022-09026-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-022-09026-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35953734$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cao, Tiexin</creatorcontrib><creatorcontrib>Jiang, Rifeng</creatorcontrib><creatorcontrib>Zheng, Lingmin</creatorcontrib><creatorcontrib>Zhang, Rufei</creatorcontrib><creatorcontrib>Chen, Xiaodan</creatorcontrib><creatorcontrib>Wang, Zongmeng</creatorcontrib><creatorcontrib>Jiang, Peirong</creatorcontrib><creatorcontrib>Chen, Yilin</creatorcontrib><creatorcontrib>Zhong, Tianjin</creatorcontrib><creatorcontrib>Chen, Hu</creatorcontrib><creatorcontrib>Wu, PuYeh</creatorcontrib><creatorcontrib>Xue, Yunjing</creatorcontrib><creatorcontrib>Lin, Lin</creatorcontrib><title>T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objective
To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.
Methods
This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman’s rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.
Results
High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (
p =
0.001–0.009), lower minimum, and C10 of ADC (
p =
0.013–0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).
Conclusion
T1 and ADC histogram parameters may represent an
in vivo
biomarker for predicting the grade, subtype, and proliferative activity of meningioma.
Key Points
• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma.
• The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.</description><subject>Biomarkers</subject><subject>Brain cancer</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Diffusion coefficient</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Edema</subject><subject>Feature extraction</subject><subject>Histograms</subject><subject>Humans</subject><subject>Imaging</subject><subject>In vivo methods and tests</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Ki-67 Antigen - metabolism</subject><subject>Kurtosis</subject><subject>Mapping</subject><subject>Mathematical analysis</subject><subject>Mean</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Meningeal Neoplasms - diagnostic imaging</subject><subject>Meningeal Neoplasms - pathology</subject><subject>Meningioma</subject><subject>Meningioma - diagnostic imaging</subject><subject>Meningioma - pathology</subject><subject>Neuroradiology</subject><subject>Oncology</subject><subject>Parameter identification</subject><subject>Prospective Studies</subject><subject>Quality</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Tumors</subject><subject>Ultrasound</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kcuO1DAQRSMEYoaBH2CBLLFhQaD8ipPlqIeXNBKbYW05SbnHQycOttNSfwG_TTU9PMQCL-ySfO6tUt2qes7hDQcwbzOAlFCDEDV0IJpaP6jOuZKi5tCqh3_VZ9WTnO8AoOPKPK7OpO60NFKdV99vOHPzyC6vNuw25BK3yU1scXRjwZTZ5A6sR2JYmNk-7CPrQ5xc-oqJ-ZjYknAMQwnzlpVbZCQf8TXLa18OCxVH7yXFXfCYXAl7ciJ4H8qBRc8mnEl49HtaPfJul_HZ_XtRfXn_7mbzsb7-_OHT5vK6HqTRpW697Abd9Fo3XoBHhb4XEj3XdEznQbTGO4MtOmjNoDuvNDYgnBxG1fFBXlSvTr401LcVc7FTyAPudm7GuGYrDAjeCdoNoS__Qe_immaajiitGtW1qiFKnKghxZwTerukQPs5WA72GJM9xWQpJvszJqtJ9OLeeu0nHH9LfuVCgDwBmb7mLaY_vf9j-wMB2p3G</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Cao, Tiexin</creator><creator>Jiang, Rifeng</creator><creator>Zheng, Lingmin</creator><creator>Zhang, Rufei</creator><creator>Chen, Xiaodan</creator><creator>Wang, Zongmeng</creator><creator>Jiang, Peirong</creator><creator>Chen, Yilin</creator><creator>Zhong, Tianjin</creator><creator>Chen, Hu</creator><creator>Wu, PuYeh</creator><creator>Xue, Yunjing</creator><creator>Lin, Lin</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</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>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/0000-0003-2782-1848</orcidid></search><sort><creationdate>20230101</creationdate><title>T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma</title><author>Cao, Tiexin ; Jiang, Rifeng ; Zheng, Lingmin ; Zhang, Rufei ; Chen, Xiaodan ; Wang, Zongmeng ; Jiang, Peirong ; Chen, Yilin ; Zhong, Tianjin ; Chen, Hu ; Wu, PuYeh ; Xue, Yunjing ; Lin, Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-8f39c56b556f20fe4efb23ef1555579f0287fa7e8ea087c59f45e602a3cd491c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biomarkers</topic><topic>Brain cancer</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Diffusion coefficient</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Edema</topic><topic>Feature extraction</topic><topic>Histograms</topic><topic>Humans</topic><topic>Imaging</topic><topic>In vivo methods and tests</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Ki-67 Antigen - metabolism</topic><topic>Kurtosis</topic><topic>Mapping</topic><topic>Mathematical analysis</topic><topic>Mean</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Meningeal Neoplasms - diagnostic imaging</topic><topic>Meningeal Neoplasms - pathology</topic><topic>Meningioma</topic><topic>Meningioma - diagnostic imaging</topic><topic>Meningioma - pathology</topic><topic>Neuroradiology</topic><topic>Oncology</topic><topic>Parameter identification</topic><topic>Prospective Studies</topic><topic>Quality</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Tumors</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cao, Tiexin</creatorcontrib><creatorcontrib>Jiang, Rifeng</creatorcontrib><creatorcontrib>Zheng, Lingmin</creatorcontrib><creatorcontrib>Zhang, Rufei</creatorcontrib><creatorcontrib>Chen, Xiaodan</creatorcontrib><creatorcontrib>Wang, Zongmeng</creatorcontrib><creatorcontrib>Jiang, Peirong</creatorcontrib><creatorcontrib>Chen, Yilin</creatorcontrib><creatorcontrib>Zhong, Tianjin</creatorcontrib><creatorcontrib>Chen, Hu</creatorcontrib><creatorcontrib>Wu, PuYeh</creatorcontrib><creatorcontrib>Xue, Yunjing</creatorcontrib><creatorcontrib>Lin, Lin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</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 Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central 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>Cao, Tiexin</au><au>Jiang, Rifeng</au><au>Zheng, Lingmin</au><au>Zhang, Rufei</au><au>Chen, Xiaodan</au><au>Wang, Zongmeng</au><au>Jiang, Peirong</au><au>Chen, Yilin</au><au>Zhong, Tianjin</au><au>Chen, Hu</au><au>Wu, PuYeh</au><au>Xue, Yunjing</au><au>Lin, Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2023-01-01</date><risdate>2023</risdate><volume>33</volume><issue>1</issue><spage>258</spage><epage>269</epage><pages>258-269</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objective
To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.
Methods
This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman’s rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.
Results
High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (
p =
0.001–0.009), lower minimum, and C10 of ADC (
p =
0.013–0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).
Conclusion
T1 and ADC histogram parameters may represent an
in vivo
biomarker for predicting the grade, subtype, and proliferative activity of meningioma.
Key Points
• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma.
• The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35953734</pmid><doi>10.1007/s00330-022-09026-5</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-2782-1848</orcidid></addata></record> |
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| subjects | Biomarkers Brain cancer Correlation coefficient Correlation coefficients Diagnostic Radiology Diagnostic systems Diffusion coefficient Diffusion Magnetic Resonance Imaging - methods Edema Feature extraction Histograms Humans Imaging In vivo methods and tests Internal Medicine Interventional Radiology Ki-67 Antigen - metabolism Kurtosis Mapping Mathematical analysis Mean Medicine Medicine & Public Health Meningeal Neoplasms - diagnostic imaging Meningeal Neoplasms - pathology Meningioma Meningioma - diagnostic imaging Meningioma - pathology Neuroradiology Oncology Parameter identification Prospective Studies Quality Radiology Regression analysis Retrospective Studies ROC Curve Tumors Ultrasound |
| title | T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma |
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