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MRI-diagnosis of category LR-M observations in the Liver Imaging Reporting and Data System v2018: a systematic review and meta-analysis
Objectives We performed a meta-analysis to determine the probability of hepatocellular carcinoma (HCC) and non-HCC malignancies in Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) observations and the frequency of defined LR-M imaging features on MRI using LI-RADS v2018. Methods W...
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Published in: | European radiology 2022-05, Vol.32 (5), p.3319-3326 |
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description | Objectives
We performed a meta-analysis to determine the probability of hepatocellular carcinoma (HCC) and non-HCC malignancies in Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) observations and the frequency of defined LR-M imaging features on MRI using LI-RADS v2018.
Methods
We searched the MEDLINE and EMBASE databases to identify studies published from 1 January 2018 to 16 March 2021 reporting the probability of category LR-M in HCC and non-HCC malignancies on MRI. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were evaluated. Meta-regression analysis was performed to identify factors for study heterogeneity. The frequencies of defined LR-M imaging features were also calculated. Risk of bias and concerns regarding applicability were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.
Results
We identified 18 studies reporting the diagnostic performance of the LR-M category (3,812 observations in 3,615 patients), with nine studies reporting the frequencies of LR-M imaging features. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were 29% (95% confidence interval [CI], 21–38%) and 67% (95%CI, 57–77%), respectively. The study type and inclusion of benign lesions were significant factors for study heterogeneity. Of the 10 LR-M imaging features, rim arterial phase hyperenhancement (APHE) showed the highest frequency in non-HCC malignancies (68%; 95%CI, 61–75%).
Conclusions
The LR-M category was commonly used to characterize non-HCC malignancies, but also included 29% of HCC. The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies.
Key Points
• In the LR-M category using LI-RADS v2018 for MRI
,
the pooled percentage of malignancies in general was 96%, with 29% HCC and 67% non-HCC malignancies, while the remaining 4% was benign entity.
• The study type and inclusion of benign lesions were significant factors contributing to substantial heterogeneity among included studies.
• The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies. |
doi_str_mv | 10.1007/s00330-021-08382-y |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2620087969</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2654822461</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-cbe8c01b6755e39e155133d4e17918df6dac78dbcb263aa7067451855800bfc53</originalsourceid><addsrcrecordid>eNp9kU1v1DAQhi1ERT_gD3BAlrhwMYztOHa4oZbCSltVWuAcOc4kuNrEi-1dlF_A3ya7Wz7UQ08zIz_zjqWHkJcc3nIA_S4BSAkMBGdgpBFsekLOeCEF42CKp__1p-Q8pTsAqHihn5FTqUByI6sz8utmtWCtt_0Ykk80dNTZjH2IE12u2A0NTcK4s9mHMVE_0vwd6dLvMNLFYHs_9nSFmxDzvrNjS69stvTLlDIOdCeAm_fU0nSY5xBHI-48_jygA2bL7GjX03z5OTnp7Drhi_t6Qb5df_x6-Zktbz8tLj8smZNaZeYaNA54U2qlUFbIleJStgVyXXHTdmVrnTZt4xpRSms1lLpQ3ChlAJrOKXlB3hxzNzH82GLK9eCTw_Xajhi2qRalADC6KqsZff0AvQvbOP93T6nCCFGUfKbEkXIxpBSxqzfRDzZONYd6r6k-aqpnTfVBUz3NS6_uo7fNgO3flT9eZkAegTQ_jT3Gf7cfif0NDK2dOg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2654822461</pqid></control><display><type>article</type><title>MRI-diagnosis of category LR-M observations in the Liver Imaging Reporting and Data System v2018: a systematic review and meta-analysis</title><source>Springer Nature</source><creator>Shin, Jaeseung ; Lee, Sunyoung ; Hwang, Jeong Ah ; Lee, Ji Eun ; Chung, Yong Eun ; Choi, Jin-Young ; Park, Mi-Suk</creator><creatorcontrib>Shin, Jaeseung ; Lee, Sunyoung ; Hwang, Jeong Ah ; Lee, Ji Eun ; Chung, Yong Eun ; Choi, Jin-Young ; Park, Mi-Suk</creatorcontrib><description>Objectives
We performed a meta-analysis to determine the probability of hepatocellular carcinoma (HCC) and non-HCC malignancies in Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) observations and the frequency of defined LR-M imaging features on MRI using LI-RADS v2018.
Methods
We searched the MEDLINE and EMBASE databases to identify studies published from 1 January 2018 to 16 March 2021 reporting the probability of category LR-M in HCC and non-HCC malignancies on MRI. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were evaluated. Meta-regression analysis was performed to identify factors for study heterogeneity. The frequencies of defined LR-M imaging features were also calculated. Risk of bias and concerns regarding applicability were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.
Results
We identified 18 studies reporting the diagnostic performance of the LR-M category (3,812 observations in 3,615 patients), with nine studies reporting the frequencies of LR-M imaging features. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were 29% (95% confidence interval [CI], 21–38%) and 67% (95%CI, 57–77%), respectively. The study type and inclusion of benign lesions were significant factors for study heterogeneity. Of the 10 LR-M imaging features, rim arterial phase hyperenhancement (APHE) showed the highest frequency in non-HCC malignancies (68%; 95%CI, 61–75%).
Conclusions
The LR-M category was commonly used to characterize non-HCC malignancies, but also included 29% of HCC. The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies.
Key Points
• In the LR-M category using LI-RADS v2018 for MRI
,
the pooled percentage of malignancies in general was 96%, with 29% HCC and 67% non-HCC malignancies, while the remaining 4% was benign entity.
• The study type and inclusion of benign lesions were significant factors contributing to substantial heterogeneity among included studies.
• The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-021-08382-y</identifier><identifier>PMID: 35031839</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Carcinoma, Hepatocellular - diagnostic imaging ; Confidence intervals ; Contrast Media ; Diagnostic Radiology ; Gastrointestinal ; Hepatocellular carcinoma ; Heterogeneity ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Lesions ; Liver ; Liver cancer ; Liver Neoplasms - diagnostic imaging ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Mathematical analysis ; Medical diagnosis ; Medical imaging ; Medicine ; Medicine & Public Health ; Meta-analysis ; Neuroradiology ; Quality assessment ; Quality control ; Radiology ; Regression analysis ; Retrospective Studies ; Sensitivity and Specificity ; Statistical analysis ; Ultrasound</subject><ispartof>European radiology, 2022-05, Vol.32 (5), p.3319-3326</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2021</rights><rights>2021. The Author(s), under exclusive licence to European Society of Radiology.</rights><rights>The Author(s), under exclusive licence to European Society of Radiology 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-cbe8c01b6755e39e155133d4e17918df6dac78dbcb263aa7067451855800bfc53</citedby><cites>FETCH-LOGICAL-c375t-cbe8c01b6755e39e155133d4e17918df6dac78dbcb263aa7067451855800bfc53</cites><orcidid>0000-0002-6893-3136</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35031839$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shin, Jaeseung</creatorcontrib><creatorcontrib>Lee, Sunyoung</creatorcontrib><creatorcontrib>Hwang, Jeong Ah</creatorcontrib><creatorcontrib>Lee, Ji Eun</creatorcontrib><creatorcontrib>Chung, Yong Eun</creatorcontrib><creatorcontrib>Choi, Jin-Young</creatorcontrib><creatorcontrib>Park, Mi-Suk</creatorcontrib><title>MRI-diagnosis of category LR-M observations in the Liver Imaging Reporting and Data System v2018: a systematic review and meta-analysis</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
We performed a meta-analysis to determine the probability of hepatocellular carcinoma (HCC) and non-HCC malignancies in Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) observations and the frequency of defined LR-M imaging features on MRI using LI-RADS v2018.
Methods
We searched the MEDLINE and EMBASE databases to identify studies published from 1 January 2018 to 16 March 2021 reporting the probability of category LR-M in HCC and non-HCC malignancies on MRI. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were evaluated. Meta-regression analysis was performed to identify factors for study heterogeneity. The frequencies of defined LR-M imaging features were also calculated. Risk of bias and concerns regarding applicability were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.
Results
We identified 18 studies reporting the diagnostic performance of the LR-M category (3,812 observations in 3,615 patients), with nine studies reporting the frequencies of LR-M imaging features. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were 29% (95% confidence interval [CI], 21–38%) and 67% (95%CI, 57–77%), respectively. The study type and inclusion of benign lesions were significant factors for study heterogeneity. Of the 10 LR-M imaging features, rim arterial phase hyperenhancement (APHE) showed the highest frequency in non-HCC malignancies (68%; 95%CI, 61–75%).
Conclusions
The LR-M category was commonly used to characterize non-HCC malignancies, but also included 29% of HCC. The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies.
Key Points
• In the LR-M category using LI-RADS v2018 for MRI
,
the pooled percentage of malignancies in general was 96%, with 29% HCC and 67% non-HCC malignancies, while the remaining 4% was benign entity.
• The study type and inclusion of benign lesions were significant factors contributing to substantial heterogeneity among included studies.
• The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies.</description><subject>Carcinoma, Hepatocellular - diagnostic imaging</subject><subject>Confidence intervals</subject><subject>Contrast Media</subject><subject>Diagnostic Radiology</subject><subject>Gastrointestinal</subject><subject>Hepatocellular carcinoma</subject><subject>Heterogeneity</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Lesions</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Liver Neoplasms - diagnostic imaging</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Mathematical analysis</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Meta-analysis</subject><subject>Neuroradiology</subject><subject>Quality assessment</subject><subject>Quality control</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Sensitivity and Specificity</subject><subject>Statistical analysis</subject><subject>Ultrasound</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kU1v1DAQhi1ERT_gD3BAlrhwMYztOHa4oZbCSltVWuAcOc4kuNrEi-1dlF_A3ya7Wz7UQ08zIz_zjqWHkJcc3nIA_S4BSAkMBGdgpBFsekLOeCEF42CKp__1p-Q8pTsAqHihn5FTqUByI6sz8utmtWCtt_0Ykk80dNTZjH2IE12u2A0NTcK4s9mHMVE_0vwd6dLvMNLFYHs_9nSFmxDzvrNjS69stvTLlDIOdCeAm_fU0nSY5xBHI-48_jygA2bL7GjX03z5OTnp7Drhi_t6Qb5df_x6-Zktbz8tLj8smZNaZeYaNA54U2qlUFbIleJStgVyXXHTdmVrnTZt4xpRSms1lLpQ3ChlAJrOKXlB3hxzNzH82GLK9eCTw_Xajhi2qRalADC6KqsZff0AvQvbOP93T6nCCFGUfKbEkXIxpBSxqzfRDzZONYd6r6k-aqpnTfVBUz3NS6_uo7fNgO3flT9eZkAegTQ_jT3Gf7cfif0NDK2dOg</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Shin, Jaeseung</creator><creator>Lee, Sunyoung</creator><creator>Hwang, Jeong Ah</creator><creator>Lee, Ji Eun</creator><creator>Chung, Yong Eun</creator><creator>Choi, Jin-Young</creator><creator>Park, Mi-Suk</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/0000-0002-6893-3136</orcidid></search><sort><creationdate>20220501</creationdate><title>MRI-diagnosis of category LR-M observations in the Liver Imaging Reporting and Data System v2018: a systematic review and meta-analysis</title><author>Shin, Jaeseung ; Lee, Sunyoung ; Hwang, Jeong Ah ; Lee, Ji Eun ; Chung, Yong Eun ; Choi, Jin-Young ; Park, Mi-Suk</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-cbe8c01b6755e39e155133d4e17918df6dac78dbcb263aa7067451855800bfc53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Carcinoma, Hepatocellular - diagnostic imaging</topic><topic>Confidence intervals</topic><topic>Contrast Media</topic><topic>Diagnostic Radiology</topic><topic>Gastrointestinal</topic><topic>Hepatocellular carcinoma</topic><topic>Heterogeneity</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Lesions</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Liver Neoplasms - diagnostic imaging</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Mathematical analysis</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Meta-analysis</topic><topic>Neuroradiology</topic><topic>Quality assessment</topic><topic>Quality control</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Sensitivity and Specificity</topic><topic>Statistical analysis</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shin, Jaeseung</creatorcontrib><creatorcontrib>Lee, Sunyoung</creatorcontrib><creatorcontrib>Hwang, Jeong Ah</creatorcontrib><creatorcontrib>Lee, Ji Eun</creatorcontrib><creatorcontrib>Chung, Yong Eun</creatorcontrib><creatorcontrib>Choi, Jin-Young</creatorcontrib><creatorcontrib>Park, Mi-Suk</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>ProQuest Nursing and Allied Health Journals</collection><collection>ProQuest_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)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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 (Proquest) (PQ_SDU_P3)</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>PML(ProQuest Medical Library)</collection><collection>ProQuest Biological Science Journals</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>Shin, Jaeseung</au><au>Lee, Sunyoung</au><au>Hwang, Jeong Ah</au><au>Lee, Ji Eun</au><au>Chung, Yong Eun</au><au>Choi, Jin-Young</au><au>Park, Mi-Suk</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MRI-diagnosis of category LR-M observations in the Liver Imaging Reporting and Data System v2018: a systematic review and meta-analysis</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2022-05-01</date><risdate>2022</risdate><volume>32</volume><issue>5</issue><spage>3319</spage><epage>3326</epage><pages>3319-3326</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
We performed a meta-analysis to determine the probability of hepatocellular carcinoma (HCC) and non-HCC malignancies in Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) observations and the frequency of defined LR-M imaging features on MRI using LI-RADS v2018.
Methods
We searched the MEDLINE and EMBASE databases to identify studies published from 1 January 2018 to 16 March 2021 reporting the probability of category LR-M in HCC and non-HCC malignancies on MRI. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were evaluated. Meta-regression analysis was performed to identify factors for study heterogeneity. The frequencies of defined LR-M imaging features were also calculated. Risk of bias and concerns regarding applicability were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.
Results
We identified 18 studies reporting the diagnostic performance of the LR-M category (3,812 observations in 3,615 patients), with nine studies reporting the frequencies of LR-M imaging features. The pooled percentages of HCC and non-HCC malignancies in the LR-M observations were 29% (95% confidence interval [CI], 21–38%) and 67% (95%CI, 57–77%), respectively. The study type and inclusion of benign lesions were significant factors for study heterogeneity. Of the 10 LR-M imaging features, rim arterial phase hyperenhancement (APHE) showed the highest frequency in non-HCC malignancies (68%; 95%CI, 61–75%).
Conclusions
The LR-M category was commonly used to characterize non-HCC malignancies, but also included 29% of HCC. The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies.
Key Points
• In the LR-M category using LI-RADS v2018 for MRI
,
the pooled percentage of malignancies in general was 96%, with 29% HCC and 67% non-HCC malignancies, while the remaining 4% was benign entity.
• The study type and inclusion of benign lesions were significant factors contributing to substantial heterogeneity among included studies.
• The frequencies of the different LR-M imaging features were variable, with rim APHE showing the highest frequency in non-HCC malignancies.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35031839</pmid><doi>10.1007/s00330-021-08382-y</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-6893-3136</orcidid></addata></record> |
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subjects | Carcinoma, Hepatocellular - diagnostic imaging Confidence intervals Contrast Media Diagnostic Radiology Gastrointestinal Hepatocellular carcinoma Heterogeneity Humans Imaging Internal Medicine Interventional Radiology Lesions Liver Liver cancer Liver Neoplasms - diagnostic imaging Magnetic resonance imaging Magnetic Resonance Imaging - methods Mathematical analysis Medical diagnosis Medical imaging Medicine Medicine & Public Health Meta-analysis Neuroradiology Quality assessment Quality control Radiology Regression analysis Retrospective Studies Sensitivity and Specificity Statistical analysis Ultrasound |
title | MRI-diagnosis of category LR-M observations in the Liver Imaging Reporting and Data System v2018: a systematic review and meta-analysis |
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