Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas
Introduction In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas. Methods Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II ( n = 19), grade III ( n = 20) and g...
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| Veröffentlicht in: | Neuroradiology 2016-12, Vol.58 (12), p.1217-1231 |
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| creator | Raja, Rajikha Sinha, Neelam Saini, Jitender Mahadevan, Anita Rao, KVL Narasinga Swaminathan, Aarthi |
| description | Introduction
In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.
Methods
Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (
n
= 19), grade III (
n
= 20) and grade IV (
n
= 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy.
Results
Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of
P
= 0.029 (0.0421) for grade II vs. III and
P
= 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability,
P
= 0.018 (0.038) for grade II vs. III and
P
= 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed
P
values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK).
Conclusion
In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters. |
| doi_str_mv | 10.1007/s00234-016-1758-y |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_1855080012</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1835005372</sourcerecordid><originalsourceid>FETCH-LOGICAL-p245t-132e99a5cd3b22c7df46a6dfe74e2d021f6b9542518e8528f2836a257805f41d3</originalsourceid><addsrcrecordid>eNqN0U1v1DAQBmALgejS8gO4oEhcegmMv2LnWFVQkCpxoWfLux6HlMRePMlh_z2OtkgVJ04eeR6NNPMy9o7DRw5gPhGAkKoF3rXcaNueXrAdV1K0vBfwku1q27ayV3DB3hA9AoA00rxmF8KYvlPK7th0Q4REM6alybFZRqIVm5-4YMkDJhyXU7PSmIYmjDHWKqdmwUS5ND6FZ5-_1rJkGqkZZz9sPlYyFB-2epjGPHu6Yq-inwjfPr2X7OHL5x-3X9v773ffbm_u26NQemm5FNj3Xh-C3AtxMCGqzncholEoAggeu32vldDcotXCRmFl54U2FnRUPMhLdn2eeyz594q0uHmkA06TT5hXctxqDRaAi_-gUgNoaTb64R_6mNeS6iJVKQv1oEpW9f5JrfsZgzuWepBycn9PXoE4A6qtNGB5Ngbclqs75-pqrm7L1Z3kH4Ifk9c</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1848064443</pqid></control><display><type>article</type><title>Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas</title><source>MEDLINE</source><source>Springer Nature - Complete Springer Journals</source><creator>Raja, Rajikha ; Sinha, Neelam ; Saini, Jitender ; Mahadevan, Anita ; Rao, KVL Narasinga ; Swaminathan, Aarthi</creator><creatorcontrib>Raja, Rajikha ; Sinha, Neelam ; Saini, Jitender ; Mahadevan, Anita ; Rao, KVL Narasinga ; Swaminathan, Aarthi</creatorcontrib><description>Introduction
In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.
Methods
Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (
n
= 19), grade III (
n
= 20) and grade IV (
n
= 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy.
Results
Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of
P
= 0.029 (0.0421) for grade II vs. III and
P
= 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability,
P
= 0.018 (0.038) for grade II vs. III and
P
= 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed
P
values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK).
Conclusion
In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters.</description><identifier>ISSN: 0028-3940</identifier><identifier>EISSN: 1432-1920</identifier><identifier>DOI: 10.1007/s00234-016-1758-y</identifier><identifier>PMID: 27796448</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adolescent ; Adult ; Aged ; Brain Neoplasms - diagnostic imaging ; Brain Neoplasms - pathology ; Child ; Child, Preschool ; Diffusion Tensor Imaging - methods ; Female ; Functional Neuroradiology ; Glioma - diagnostic imaging ; Glioma - pathology ; Humans ; Image Interpretation, Computer-Assisted - methods ; Imaging ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Neoplasm Grading ; Neurology ; Neuroradiology ; Neurosciences ; Neurosurgery ; Pattern Recognition, Automated - methods ; Radiology ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult</subject><ispartof>Neuroradiology, 2016-12, Vol.58 (12), p.1217-1231</ispartof><rights>Springer-Verlag Berlin Heidelberg 2016</rights><rights>Neuroradiology is a copyright of Springer, 2016.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-p245t-132e99a5cd3b22c7df46a6dfe74e2d021f6b9542518e8528f2836a257805f41d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00234-016-1758-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00234-016-1758-y$$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/27796448$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Raja, Rajikha</creatorcontrib><creatorcontrib>Sinha, Neelam</creatorcontrib><creatorcontrib>Saini, Jitender</creatorcontrib><creatorcontrib>Mahadevan, Anita</creatorcontrib><creatorcontrib>Rao, KVL Narasinga</creatorcontrib><creatorcontrib>Swaminathan, Aarthi</creatorcontrib><title>Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas</title><title>Neuroradiology</title><addtitle>Neuroradiology</addtitle><addtitle>Neuroradiology</addtitle><description>Introduction
In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.
Methods
Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (
n
= 19), grade III (
n
= 20) and grade IV (
n
= 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy.
Results
Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of
P
= 0.029 (0.0421) for grade II vs. III and
P
= 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability,
P
= 0.018 (0.038) for grade II vs. III and
P
= 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed
P
values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK).
Conclusion
In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Brain Neoplasms - diagnostic imaging</subject><subject>Brain Neoplasms - pathology</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Diffusion Tensor Imaging - methods</subject><subject>Female</subject><subject>Functional Neuroradiology</subject><subject>Glioma - diagnostic imaging</subject><subject>Glioma - pathology</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neoplasm Grading</subject><subject>Neurology</subject><subject>Neuroradiology</subject><subject>Neurosciences</subject><subject>Neurosurgery</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Radiology</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Young Adult</subject><issn>0028-3940</issn><issn>1432-1920</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqN0U1v1DAQBmALgejS8gO4oEhcegmMv2LnWFVQkCpxoWfLux6HlMRePMlh_z2OtkgVJ04eeR6NNPMy9o7DRw5gPhGAkKoF3rXcaNueXrAdV1K0vBfwku1q27ayV3DB3hA9AoA00rxmF8KYvlPK7th0Q4REM6alybFZRqIVm5-4YMkDJhyXU7PSmIYmjDHWKqdmwUS5ND6FZ5-_1rJkGqkZZz9sPlYyFB-2epjGPHu6Yq-inwjfPr2X7OHL5x-3X9v773ffbm_u26NQemm5FNj3Xh-C3AtxMCGqzncholEoAggeu32vldDcotXCRmFl54U2FnRUPMhLdn2eeyz594q0uHmkA06TT5hXctxqDRaAi_-gUgNoaTb64R_6mNeS6iJVKQv1oEpW9f5JrfsZgzuWepBycn9PXoE4A6qtNGB5Ngbclqs75-pqrm7L1Z3kH4Ifk9c</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Raja, Rajikha</creator><creator>Sinha, Neelam</creator><creator>Saini, Jitender</creator><creator>Mahadevan, Anita</creator><creator>Rao, KVL Narasinga</creator><creator>Swaminathan, Aarthi</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>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7TK</scope><scope>7U7</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>8G5</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>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</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>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20161201</creationdate><title>Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas</title><author>Raja, Rajikha ; Sinha, Neelam ; Saini, Jitender ; Mahadevan, Anita ; Rao, KVL Narasinga ; Swaminathan, Aarthi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p245t-132e99a5cd3b22c7df46a6dfe74e2d021f6b9542518e8528f2836a257805f41d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Brain Neoplasms - diagnostic imaging</topic><topic>Brain Neoplasms - pathology</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Diffusion Tensor Imaging - methods</topic><topic>Female</topic><topic>Functional Neuroradiology</topic><topic>Glioma - diagnostic imaging</topic><topic>Glioma - pathology</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Imaging</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neoplasm Grading</topic><topic>Neurology</topic><topic>Neuroradiology</topic><topic>Neurosciences</topic><topic>Neurosurgery</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Raja, Rajikha</creatorcontrib><creatorcontrib>Sinha, Neelam</creatorcontrib><creatorcontrib>Saini, Jitender</creatorcontrib><creatorcontrib>Mahadevan, Anita</creatorcontrib><creatorcontrib>Rao, KVL Narasinga</creatorcontrib><creatorcontrib>Swaminathan, Aarthi</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</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>Research Library (Alumni Edition)</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>Environmental Sciences and Pollution Management</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>Research Library Prep</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>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</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>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Neuroradiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Raja, Rajikha</au><au>Sinha, Neelam</au><au>Saini, Jitender</au><au>Mahadevan, Anita</au><au>Rao, KVL Narasinga</au><au>Swaminathan, Aarthi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas</atitle><jtitle>Neuroradiology</jtitle><stitle>Neuroradiology</stitle><addtitle>Neuroradiology</addtitle><date>2016-12-01</date><risdate>2016</risdate><volume>58</volume><issue>12</issue><spage>1217</spage><epage>1231</epage><pages>1217-1231</pages><issn>0028-3940</issn><eissn>1432-1920</eissn><abstract>Introduction
In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.
Methods
Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (
n
= 19), grade III (
n
= 20) and grade IV (
n
= 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy.
Results
Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of
P
= 0.029 (0.0421) for grade II vs. III and
P
= 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability,
P
= 0.018 (0.038) for grade II vs. III and
P
= 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed
P
values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK).
Conclusion
In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>27796448</pmid><doi>10.1007/s00234-016-1758-y</doi><tpages>15</tpages></addata></record> |
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| subjects | Adolescent Adult Aged Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Child Child, Preschool Diffusion Tensor Imaging - methods Female Functional Neuroradiology Glioma - diagnostic imaging Glioma - pathology Humans Image Interpretation, Computer-Assisted - methods Imaging Male Medicine Medicine & Public Health Middle Aged Neoplasm Grading Neurology Neuroradiology Neurosciences Neurosurgery Pattern Recognition, Automated - methods Radiology Reproducibility of Results Sensitivity and Specificity Young Adult |
| title | Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas |
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