Preliminary Study of MR Diffusion Tensor Imaging of the Liver for the Diagnosis of Hepatocellular Carcinoma
To evaluate the feasibility of differentiating between hepatocellular carcinomas (HCC) and healthy liver using diffusion tensor imaging (DTI). All subjects underwent an abdominal examination on a 3.0T MRI scanner. Two radiologists independently scored the image quality (IQ). An optimal set of DTI pa...
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| creator | Li, Xinghui Liang, Qi Zhuang, Ling Zhang, Xiaoming Chen, Tianwu Li, Liangjun Liu, Jun Calimente, Horea Wei, Yinan Hu, Jiani |
| description | To evaluate the feasibility of differentiating between hepatocellular carcinomas (HCC) and healthy liver using diffusion tensor imaging (DTI).
All subjects underwent an abdominal examination on a 3.0T MRI scanner. Two radiologists independently scored the image quality (IQ). An optimal set of DTI parameters was obtained from a group of fifteen volunteers with multiple b-values (100, 300, 500, and 800 s/mm2) and various diffusion-encoding directions (NED = 6, 9, and 12)using two way ANOVA analysis. Eighteen Patients with HCC underwent DTI scans with the optimized parameters. Fractional anisotropy(FA) and average apparent diffusion coefficient (ADC) values were measured. The differences of FA and ADC values between liver healthy region and HCC lesion were compared through paired t tests.
There were no significant changes in liver IQ and FA/ADC values with increased NED(P >0.05), whereas the liver IQ and FA/ADC values decreased significantly with increased b-values(P |
| doi_str_mv | 10.1371/journal.pone.0135568 |
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All subjects underwent an abdominal examination on a 3.0T MRI scanner. Two radiologists independently scored the image quality (IQ). An optimal set of DTI parameters was obtained from a group of fifteen volunteers with multiple b-values (100, 300, 500, and 800 s/mm2) and various diffusion-encoding directions (NED = 6, 9, and 12)using two way ANOVA analysis. Eighteen Patients with HCC underwent DTI scans with the optimized parameters. Fractional anisotropy(FA) and average apparent diffusion coefficient (ADC) values were measured. The differences of FA and ADC values between liver healthy region and HCC lesion were compared through paired t tests.
There were no significant changes in liver IQ and FA/ADC values with increased NED(P >0.05), whereas the liver IQ and FA/ADC values decreased significantly with increased b-values(P <0.05). Good IQ, acceptable scan time and reasonable FA/ADC values were acquired using NED = 9 with b-value of (0,300) s/mm2. Using the optimized DTI sequence, ADC value of the tumor lesion was significantly lower than that of the healthy liver region (1.30 ± 0.34×10-3 vs 1.52 ± 0.27×10-3 mm2/s, P = 0.013), whereas the mean FA value of the tumor lesion (0.42 ± 0.11) was significantly higher than the normal liver region (0.32 ± 0.10) (P = 0.004).
Either FA or ADC value from DTI can be used to differentiate HCC from healthy liver. HCC lead to higher FA value and lower ADC value on DTI than healthy liver.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0135568</identifier><identifier>PMID: 26317346</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Anisotropy ; Biopsy ; Brain cancer ; Carcinoma, Hepatocellular - diagnosis ; Case-Control Studies ; Diffusion ; Diffusion coefficient ; Diffusion Tensor Imaging - methods ; Feasibility studies ; Female ; Hepatocellular carcinoma ; Hospitals ; Humans ; Image Processing, Computer-Assisted ; Image quality ; Kidneys ; Laboratories ; Liver ; Liver cancer ; Liver Neoplasms - diagnosis ; Magnetic resonance imaging ; Male ; Medical diagnosis ; Medical imaging ; Middle Aged ; NMR ; Nuclear magnetic resonance ; Parameters ; Studies ; Tumors ; Variance analysis ; Young Adult</subject><ispartof>PloS one, 2015-08, Vol.10 (8), p.e0135568-e0135568</ispartof><rights>2015 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Li et al 2015 Li et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c592t-7e70c320f2b9c28318320f138f2b1ab4fa29878b25d7a69e6496f5bc0d220c2c3</citedby><cites>FETCH-LOGICAL-c592t-7e70c320f2b9c28318320f138f2b1ab4fa29878b25d7a69e6496f5bc0d220c2c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4552840/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4552840/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26317346$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Jiang, Quan</contributor><creatorcontrib>Li, Xinghui</creatorcontrib><creatorcontrib>Liang, Qi</creatorcontrib><creatorcontrib>Zhuang, Ling</creatorcontrib><creatorcontrib>Zhang, Xiaoming</creatorcontrib><creatorcontrib>Chen, Tianwu</creatorcontrib><creatorcontrib>Li, Liangjun</creatorcontrib><creatorcontrib>Liu, Jun</creatorcontrib><creatorcontrib>Calimente, Horea</creatorcontrib><creatorcontrib>Wei, Yinan</creatorcontrib><creatorcontrib>Hu, Jiani</creatorcontrib><title>Preliminary Study of MR Diffusion Tensor Imaging of the Liver for the Diagnosis of Hepatocellular Carcinoma</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>To evaluate the feasibility of differentiating between hepatocellular carcinomas (HCC) and healthy liver using diffusion tensor imaging (DTI).
All subjects underwent an abdominal examination on a 3.0T MRI scanner. Two radiologists independently scored the image quality (IQ). An optimal set of DTI parameters was obtained from a group of fifteen volunteers with multiple b-values (100, 300, 500, and 800 s/mm2) and various diffusion-encoding directions (NED = 6, 9, and 12)using two way ANOVA analysis. Eighteen Patients with HCC underwent DTI scans with the optimized parameters. Fractional anisotropy(FA) and average apparent diffusion coefficient (ADC) values were measured. The differences of FA and ADC values between liver healthy region and HCC lesion were compared through paired t tests.
There were no significant changes in liver IQ and FA/ADC values with increased NED(P >0.05), whereas the liver IQ and FA/ADC values decreased significantly with increased b-values(P <0.05). Good IQ, acceptable scan time and reasonable FA/ADC values were acquired using NED = 9 with b-value of (0,300) s/mm2. Using the optimized DTI sequence, ADC value of the tumor lesion was significantly lower than that of the healthy liver region (1.30 ± 0.34×10-3 vs 1.52 ± 0.27×10-3 mm2/s, P = 0.013), whereas the mean FA value of the tumor lesion (0.42 ± 0.11) was significantly higher than the normal liver region (0.32 ± 0.10) (P = 0.004).
Either FA or ADC value from DTI can be used to differentiate HCC from healthy liver. HCC lead to higher FA value and lower ADC value on DTI than healthy liver.</description><subject>Adult</subject><subject>Aged</subject><subject>Anisotropy</subject><subject>Biopsy</subject><subject>Brain cancer</subject><subject>Carcinoma, Hepatocellular - diagnosis</subject><subject>Case-Control Studies</subject><subject>Diffusion</subject><subject>Diffusion coefficient</subject><subject>Diffusion Tensor Imaging - methods</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Hepatocellular carcinoma</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image quality</subject><subject>Kidneys</subject><subject>Laboratories</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Liver Neoplasms - diagnosis</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Middle Aged</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Parameters</subject><subject>Studies</subject><subject>Tumors</subject><subject>Variance analysis</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</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><sourceid>DOA</sourceid><recordid>eNptUstu1DAUjRCIlsIfIIjEhs0MfsSPbJDQFOhIg0BQ1pbjXKceEnuwk0r9exwmrVrEyr73nHvuQ6coXmK0xlTgd_swRa_79SF4WCNMGePyUXGKa0pWnCD6-N7_pHiW0h4hRiXnT4sTwikWtOKnxa9vEXo3OK_jTfljnNqbMtjyy_fy3Fk7JRd8eQk-hVhuB905383weAXlzl1DLG0G5ujc6c6H5NIMX8BBj8FA30-9juVGR-N8GPTz4onVfYIXy3tW_Pz08XJzsdp9_bzdfNitDKvJuBIgkKEEWdLUhkiK5RxgKnMC66aymtRSyIawVmheA69qblljUEsIMsTQs-L1UffQh6SWOyWFBZKVJKzimbE9Mtqg9-oQ3ZDXV0E79TcRYqd0HJ3pQWHGWNPmdla0FQirc28ONTAmBDDJstb7pdvUDNAa8GPU_QPRh4h3V6oL16pijMgKZYG3i0AMvydIoxpcmq-nPYTpOHeNkBDz3G_-of5_u-rIMjGkFMHeDYORmr1zW6Vm76jFO7ns1f1F7opuzUL_AHPKwlk</recordid><startdate>20150828</startdate><enddate>20150828</enddate><creator>Li, Xinghui</creator><creator>Liang, Qi</creator><creator>Zhuang, Ling</creator><creator>Zhang, Xiaoming</creator><creator>Chen, Tianwu</creator><creator>Li, Liangjun</creator><creator>Liu, Jun</creator><creator>Calimente, Horea</creator><creator>Wei, Yinan</creator><creator>Hu, Jiani</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20150828</creationdate><title>Preliminary Study of MR Diffusion Tensor Imaging of the Liver for the Diagnosis of Hepatocellular Carcinoma</title><author>Li, Xinghui ; Liang, Qi ; Zhuang, Ling ; Zhang, Xiaoming ; Chen, Tianwu ; Li, Liangjun ; Liu, Jun ; Calimente, Horea ; Wei, Yinan ; Hu, Jiani</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c592t-7e70c320f2b9c28318320f138f2b1ab4fa29878b25d7a69e6496f5bc0d220c2c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Anisotropy</topic><topic>Biopsy</topic><topic>Brain cancer</topic><topic>Carcinoma, Hepatocellular - diagnosis</topic><topic>Case-Control Studies</topic><topic>Diffusion</topic><topic>Diffusion coefficient</topic><topic>Diffusion Tensor Imaging - methods</topic><topic>Feasibility studies</topic><topic>Female</topic><topic>Hepatocellular carcinoma</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Image quality</topic><topic>Kidneys</topic><topic>Laboratories</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Liver Neoplasms - diagnosis</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Middle Aged</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Parameters</topic><topic>Studies</topic><topic>Tumors</topic><topic>Variance analysis</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xinghui</creatorcontrib><creatorcontrib>Liang, Qi</creatorcontrib><creatorcontrib>Zhuang, Ling</creatorcontrib><creatorcontrib>Zhang, Xiaoming</creatorcontrib><creatorcontrib>Chen, Tianwu</creatorcontrib><creatorcontrib>Li, Liangjun</creatorcontrib><creatorcontrib>Liu, Jun</creatorcontrib><creatorcontrib>Calimente, Horea</creatorcontrib><creatorcontrib>Wei, Yinan</creatorcontrib><creatorcontrib>Hu, Jiani</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>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</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>Public Health Database</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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science 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 Materials Science Collection</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>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xinghui</au><au>Liang, Qi</au><au>Zhuang, Ling</au><au>Zhang, Xiaoming</au><au>Chen, Tianwu</au><au>Li, Liangjun</au><au>Liu, Jun</au><au>Calimente, Horea</au><au>Wei, Yinan</au><au>Hu, Jiani</au><au>Jiang, Quan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Preliminary Study of MR Diffusion Tensor Imaging of the Liver for the Diagnosis of Hepatocellular Carcinoma</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-08-28</date><risdate>2015</risdate><volume>10</volume><issue>8</issue><spage>e0135568</spage><epage>e0135568</epage><pages>e0135568-e0135568</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To evaluate the feasibility of differentiating between hepatocellular carcinomas (HCC) and healthy liver using diffusion tensor imaging (DTI).
All subjects underwent an abdominal examination on a 3.0T MRI scanner. Two radiologists independently scored the image quality (IQ). An optimal set of DTI parameters was obtained from a group of fifteen volunteers with multiple b-values (100, 300, 500, and 800 s/mm2) and various diffusion-encoding directions (NED = 6, 9, and 12)using two way ANOVA analysis. Eighteen Patients with HCC underwent DTI scans with the optimized parameters. Fractional anisotropy(FA) and average apparent diffusion coefficient (ADC) values were measured. The differences of FA and ADC values between liver healthy region and HCC lesion were compared through paired t tests.
There were no significant changes in liver IQ and FA/ADC values with increased NED(P >0.05), whereas the liver IQ and FA/ADC values decreased significantly with increased b-values(P <0.05). Good IQ, acceptable scan time and reasonable FA/ADC values were acquired using NED = 9 with b-value of (0,300) s/mm2. Using the optimized DTI sequence, ADC value of the tumor lesion was significantly lower than that of the healthy liver region (1.30 ± 0.34×10-3 vs 1.52 ± 0.27×10-3 mm2/s, P = 0.013), whereas the mean FA value of the tumor lesion (0.42 ± 0.11) was significantly higher than the normal liver region (0.32 ± 0.10) (P = 0.004).
Either FA or ADC value from DTI can be used to differentiate HCC from healthy liver. HCC lead to higher FA value and lower ADC value on DTI than healthy liver.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26317346</pmid><doi>10.1371/journal.pone.0135568</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Aged Anisotropy Biopsy Brain cancer Carcinoma, Hepatocellular - diagnosis Case-Control Studies Diffusion Diffusion coefficient Diffusion Tensor Imaging - methods Feasibility studies Female Hepatocellular carcinoma Hospitals Humans Image Processing, Computer-Assisted Image quality Kidneys Laboratories Liver Liver cancer Liver Neoplasms - diagnosis Magnetic resonance imaging Male Medical diagnosis Medical imaging Middle Aged NMR Nuclear magnetic resonance Parameters Studies Tumors Variance analysis Young Adult |
| title | Preliminary Study of MR Diffusion Tensor Imaging of the Liver for the Diagnosis of Hepatocellular Carcinoma |
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