Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability
Objectives Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. Methods Six...
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| Veröffentlicht in: | European radiology 2021-02, Vol.31 (2), p.729-739 |
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| creator | Chu, Jian-ping Song, Yu-kun Tian, Yi-su Qiu, Hai-shan Huang, Xia-hua Wang, Yu-liang Huang, Ying-qian Zhao, Jing |
| description | Objectives
Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting
IDH-1
mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data.
Methods
Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed.
Results
The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (
p
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| doi_str_mv | 10.1007/s00330-020-07204-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2438683886</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2478672415</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-6c2469dc7f04cac24e47af664a1c3c1f83891450c0f76e326a8e07a8c753af33</originalsourceid><addsrcrecordid>eNp9kc1u1TAQhS0EoreFF2CBLLFh0YATO7HDrioFKlVi073lOuPgktgX26naF-I5OyEXkFiwsGzPfOf45xDyqmbvasbk-8wY56xiDQ7ZMFHdPyG7WvCmqpkST8mO9VxVsu_FETnO-ZYx1tdCPidHvFHtqtiRnx-9c0v2MdDvSyox-0z9bEYfRuoDhTszLaasu3HycTb5Ax1QAQlCoQnGVRgdogVLudAME9iyVmco3-KQKS6Ln4GCc956CPbhFHsmLwnmzWQPppgbP_mCLRMGPMGMIebiLT3UX5BnzkwZXh7mE3L96eL6_Et19fXz5fnZVWW5bEvV2UZ0_WClY8Ia3ICQxnWdMLXltnaKK_yBllnmZAe86YwCJo2ysuXGcX5C3m62-xR_LPgePftsYZpMgLhk3QiuOjRRHaJv_kFv45ICXg4pqTrZiLpFqtkom2LOCZzeJ_ze9KBrptcQ9RaixhD1rxD1PYpeH6yXmxmGP5LfqSHANyBjK4yQ_p79H9tHHJyr6w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2478672415</pqid></control><display><type>article</type><title>Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Chu, Jian-ping ; Song, Yu-kun ; Tian, Yi-su ; Qiu, Hai-shan ; Huang, Xia-hua ; Wang, Yu-liang ; Huang, Ying-qian ; Zhao, Jing</creator><creatorcontrib>Chu, Jian-ping ; Song, Yu-kun ; Tian, Yi-su ; Qiu, Hai-shan ; Huang, Xia-hua ; Wang, Yu-liang ; Huang, Ying-qian ; Zhao, Jing</creatorcontrib><description>Objectives
Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting
IDH-1
mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data.
Methods
Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed.
Results
The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (
p
< 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the
IDH-1
mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81).
Conclusions
Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.
Key Points
• The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods.
• DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma.
• The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-020-07204-x</identifier><identifier>PMID: 32857204</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Brain Neoplasms - diagnostic imaging ; Brain Neoplasms - genetics ; Diagnostic Radiology ; Diagnostic systems ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Evaluation ; Female ; Gene deletion ; Glioma ; Glioma - diagnostic imaging ; Glioma - genetics ; Humans ; Image processing ; Imaging ; Internal Medicine ; Interventional Radiology ; Kurtosis ; Magnetic Resonance ; Medical diagnosis ; Medical imaging ; Medicine ; Medicine & Public Health ; Middle Aged ; Mutation ; Neoplasm Grading ; Neuroradiology ; Parameters ; Parenchyma ; Radiology ; Reliability analysis ; Reproducibility ; Reproducibility of Results ; Spots ; Survival ; Survival analysis ; Time measurement ; Tumors ; Ultrasound</subject><ispartof>European radiology, 2021-02, Vol.31 (2), p.729-739</ispartof><rights>European Society of Radiology 2020</rights><rights>European Society of Radiology 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-6c2469dc7f04cac24e47af664a1c3c1f83891450c0f76e326a8e07a8c753af33</citedby><cites>FETCH-LOGICAL-c375t-6c2469dc7f04cac24e47af664a1c3c1f83891450c0f76e326a8e07a8c753af33</cites><orcidid>0000-0002-9270-3250</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/s00330-020-07204-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-020-07204-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27907,27908,41471,42540,51302</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32857204$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chu, Jian-ping</creatorcontrib><creatorcontrib>Song, Yu-kun</creatorcontrib><creatorcontrib>Tian, Yi-su</creatorcontrib><creatorcontrib>Qiu, Hai-shan</creatorcontrib><creatorcontrib>Huang, Xia-hua</creatorcontrib><creatorcontrib>Wang, Yu-liang</creatorcontrib><creatorcontrib>Huang, Ying-qian</creatorcontrib><creatorcontrib>Zhao, Jing</creatorcontrib><title>Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting
IDH-1
mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data.
Methods
Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed.
Results
The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (
p
< 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the
IDH-1
mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81).
Conclusions
Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.
Key Points
• The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods.
• DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma.
• The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.</description><subject>Brain Neoplasms - diagnostic imaging</subject><subject>Brain Neoplasms - genetics</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Diffusion Magnetic Resonance Imaging</subject><subject>Diffusion Tensor Imaging</subject><subject>Evaluation</subject><subject>Female</subject><subject>Gene deletion</subject><subject>Glioma</subject><subject>Glioma - diagnostic imaging</subject><subject>Glioma - genetics</subject><subject>Humans</subject><subject>Image processing</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Kurtosis</subject><subject>Magnetic Resonance</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Mutation</subject><subject>Neoplasm Grading</subject><subject>Neuroradiology</subject><subject>Parameters</subject><subject>Parenchyma</subject><subject>Radiology</subject><subject>Reliability analysis</subject><subject>Reproducibility</subject><subject>Reproducibility of Results</subject><subject>Spots</subject><subject>Survival</subject><subject>Survival analysis</subject><subject>Time measurement</subject><subject>Tumors</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>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kc1u1TAQhS0EoreFF2CBLLFh0YATO7HDrioFKlVi073lOuPgktgX26naF-I5OyEXkFiwsGzPfOf45xDyqmbvasbk-8wY56xiDQ7ZMFHdPyG7WvCmqpkST8mO9VxVsu_FETnO-ZYx1tdCPidHvFHtqtiRnx-9c0v2MdDvSyox-0z9bEYfRuoDhTszLaasu3HycTb5Ax1QAQlCoQnGVRgdogVLudAME9iyVmco3-KQKS6Ln4GCc956CPbhFHsmLwnmzWQPppgbP_mCLRMGPMGMIebiLT3UX5BnzkwZXh7mE3L96eL6_Et19fXz5fnZVWW5bEvV2UZ0_WClY8Ia3ICQxnWdMLXltnaKK_yBllnmZAe86YwCJo2ysuXGcX5C3m62-xR_LPgePftsYZpMgLhk3QiuOjRRHaJv_kFv45ICXg4pqTrZiLpFqtkom2LOCZzeJ_ze9KBrptcQ9RaixhD1rxD1PYpeH6yXmxmGP5LfqSHANyBjK4yQ_p79H9tHHJyr6w</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Chu, Jian-ping</creator><creator>Song, Yu-kun</creator><creator>Tian, Yi-su</creator><creator>Qiu, Hai-shan</creator><creator>Huang, Xia-hua</creator><creator>Wang, Yu-liang</creator><creator>Huang, Ying-qian</creator><creator>Zhao, Jing</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/0000-0002-9270-3250</orcidid></search><sort><creationdate>20210201</creationdate><title>Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability</title><author>Chu, Jian-ping ; Song, Yu-kun ; Tian, Yi-su ; Qiu, Hai-shan ; Huang, Xia-hua ; Wang, Yu-liang ; Huang, Ying-qian ; Zhao, Jing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-6c2469dc7f04cac24e47af664a1c3c1f83891450c0f76e326a8e07a8c753af33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Brain Neoplasms - diagnostic imaging</topic><topic>Brain Neoplasms - genetics</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Diffusion Magnetic Resonance Imaging</topic><topic>Diffusion Tensor Imaging</topic><topic>Evaluation</topic><topic>Female</topic><topic>Gene deletion</topic><topic>Glioma</topic><topic>Glioma - diagnostic imaging</topic><topic>Glioma - genetics</topic><topic>Humans</topic><topic>Image processing</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Kurtosis</topic><topic>Magnetic Resonance</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Mutation</topic><topic>Neoplasm Grading</topic><topic>Neuroradiology</topic><topic>Parameters</topic><topic>Parenchyma</topic><topic>Radiology</topic><topic>Reliability analysis</topic><topic>Reproducibility</topic><topic>Reproducibility of Results</topic><topic>Spots</topic><topic>Survival</topic><topic>Survival analysis</topic><topic>Time measurement</topic><topic>Tumors</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chu, Jian-ping</creatorcontrib><creatorcontrib>Song, Yu-kun</creatorcontrib><creatorcontrib>Tian, Yi-su</creatorcontrib><creatorcontrib>Qiu, Hai-shan</creatorcontrib><creatorcontrib>Huang, Xia-hua</creatorcontrib><creatorcontrib>Wang, Yu-liang</creatorcontrib><creatorcontrib>Huang, Ying-qian</creatorcontrib><creatorcontrib>Zhao, Jing</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>Chu, Jian-ping</au><au>Song, Yu-kun</au><au>Tian, Yi-su</au><au>Qiu, Hai-shan</au><au>Huang, Xia-hua</au><au>Wang, Yu-liang</au><au>Huang, Ying-qian</au><au>Zhao, Jing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2021-02-01</date><risdate>2021</risdate><volume>31</volume><issue>2</issue><spage>729</spage><epage>739</epage><pages>729-739</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting
IDH-1
mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data.
Methods
Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed.
Results
The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (
p
< 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the
IDH-1
mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81).
Conclusions
Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.
Key Points
• The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods.
• DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma.
• The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32857204</pmid><doi>10.1007/s00330-020-07204-x</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-9270-3250</orcidid></addata></record> |
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| ispartof | European radiology, 2021-02, Vol.31 (2), p.729-739 |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Brain Neoplasms - diagnostic imaging Brain Neoplasms - genetics Diagnostic Radiology Diagnostic systems Diffusion Magnetic Resonance Imaging Diffusion Tensor Imaging Evaluation Female Gene deletion Glioma Glioma - diagnostic imaging Glioma - genetics Humans Image processing Imaging Internal Medicine Interventional Radiology Kurtosis Magnetic Resonance Medical diagnosis Medical imaging Medicine Medicine & Public Health Middle Aged Mutation Neoplasm Grading Neuroradiology Parameters Parenchyma Radiology Reliability analysis Reproducibility Reproducibility of Results Spots Survival Survival analysis Time measurement Tumors Ultrasound |
| title | Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability |
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