MR classification of renal masses with pathologic correlation
To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis wa...
Gespeichert in:
| Veröffentlicht in: | European radiology 2008-02, Vol.18 (2), p.365-375 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 375 |
|---|---|
| container_issue | 2 |
| container_start_page | 365 |
| container_title | European radiology |
| container_volume | 18 |
| creator | Pedrosa, Ivan Chou, Mary T. Ngo, Long H. Baroni, Ronaldo Genega, Elizabeth M. Galaburda, Laura DeWolf, William C. Rofsky, Neil M. |
| description | To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis was performed. Each renal mass was assigned to one of eight categories from a proposed MRI classification system. The sensitivity and specificity of the MRI classification system to predict the histologic subtype and nuclear grade was calculated. Subvoxel fat on chemical shift imaging correlated to clear cell type (p |
| doi_str_mv | 10.1007/s00330-007-0757-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_70260110</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1898224221</sourcerecordid><originalsourceid>FETCH-LOGICAL-c369t-14449f9ee8a60b4842637411bf7fb6908be0864010b4a51dd19b1ff1c1dd667e3</originalsourceid><addsrcrecordid>eNp1kN1LwzAUxYMobk7_AF-k-OBb9d42y8eDDzL8gokg-hzSLtk62mUmLeJ_b2oHA8GX5HDPL-eSQ8g5wjUC8JsAkOeQRpkCn8bjgIyR5lmKIOghGYPMRcqlpCNyEsIaACRSfkxGyIWUCGxMbl_ekrLWIVS2KnVbuU3ibOLNRtdJE8cmJF9Vu0q2ul252i2rMimd96b-ZU_JkdV1MGe7e0I-Hu7fZ0_p_PXxeXY3T8ucyTZFSqm00hihGRRU0IzlnCIWltuCSRCFAcEoYDT1FBcLlAVai2WUjHGTT8jVkLv17rMzoVVNFUpT13pjXBcUh4wBIkTw8g-4dp2PnwkqQyGEhCyLEA5Q6V0I3li19VWj_bdCUH2xaihW9bIvVvXBF7vgrmjMYv9i12QEsgEI0dosjd9v_j_1BzdGgbc</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>218889022</pqid></control><display><type>article</type><title>MR classification of renal masses with pathologic correlation</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Pedrosa, Ivan ; Chou, Mary T. ; Ngo, Long ; H. Baroni, Ronaldo ; Genega, Elizabeth M. ; Galaburda, Laura ; DeWolf, William C. ; Rofsky, Neil M.</creator><creatorcontrib>Pedrosa, Ivan ; Chou, Mary T. ; Ngo, Long ; H. Baroni, Ronaldo ; Genega, Elizabeth M. ; Galaburda, Laura ; DeWolf, William C. ; Rofsky, Neil M.</creatorcontrib><description>To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis was performed. Each renal mass was assigned to one of eight categories from a proposed MRI classification system. The sensitivity and specificity of the MRI classification system to predict the histologic subtype and nuclear grade was calculated. Subvoxel fat on chemical shift imaging correlated to clear cell type (p < 0.05); sensitivity = 42%, specificity = 100%. Large size, intratumoral necrosis, retroperitoneal vascular collaterals, and renal vein thrombosis predicted high-grade clear cell type (p < 0.05). Small size, peripheral location, low intratumoral SI on T2-weighted images, and low-level enhancement were associated with low-grade papillary carcinomas (p < 0.05). The sensitivity and specificity of the MRI classification system for diagnosing low grade clear cell, high-grade clear cell, all clear cell, all papillary, and transitional carcinomas were 50% and 94%, 93% and 75%, 92% and 83%, 80% and 94%, and 100% and 99%, respectively. The MRI feature analysis and proposed classification system help predict the histological type and nuclear grade of renal masses.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-007-0757-0</identifier><identifier>PMID: 17899106</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Carcinoma, Renal Cell - diagnosis ; Carcinoma, Renal Cell - pathology ; Classification ; Contrast Media - administration & dosage ; Diagnostic Radiology ; Female ; Humans ; Image Enhancement - methods ; Imaging ; Internal Medicine ; Interventional Radiology ; Kidney - pathology ; Kidney Neoplasms - diagnosis ; Kidney Neoplasms - pathology ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Neoplasm Staging ; Neuroradiology ; Observer Variation ; Patients ; Predictive Value of Tests ; Radiology ; Retrospective Studies ; Risk ; Sensitivity and Specificity ; Thrombosis ; Tumors ; Ultrasound ; Urogenital</subject><ispartof>European radiology, 2008-02, Vol.18 (2), p.365-375</ispartof><rights>European Society of Radiology 2007</rights><rights>European Society of Radiology 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-14449f9ee8a60b4842637411bf7fb6908be0864010b4a51dd19b1ff1c1dd667e3</citedby><cites>FETCH-LOGICAL-c369t-14449f9ee8a60b4842637411bf7fb6908be0864010b4a51dd19b1ff1c1dd667e3</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/s00330-007-0757-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-007-0757-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17899106$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pedrosa, Ivan</creatorcontrib><creatorcontrib>Chou, Mary T.</creatorcontrib><creatorcontrib>Ngo, Long</creatorcontrib><creatorcontrib>H. Baroni, Ronaldo</creatorcontrib><creatorcontrib>Genega, Elizabeth M.</creatorcontrib><creatorcontrib>Galaburda, Laura</creatorcontrib><creatorcontrib>DeWolf, William C.</creatorcontrib><creatorcontrib>Rofsky, Neil M.</creatorcontrib><title>MR classification of renal masses with pathologic correlation</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis was performed. Each renal mass was assigned to one of eight categories from a proposed MRI classification system. The sensitivity and specificity of the MRI classification system to predict the histologic subtype and nuclear grade was calculated. Subvoxel fat on chemical shift imaging correlated to clear cell type (p < 0.05); sensitivity = 42%, specificity = 100%. Large size, intratumoral necrosis, retroperitoneal vascular collaterals, and renal vein thrombosis predicted high-grade clear cell type (p < 0.05). Small size, peripheral location, low intratumoral SI on T2-weighted images, and low-level enhancement were associated with low-grade papillary carcinomas (p < 0.05). The sensitivity and specificity of the MRI classification system for diagnosing low grade clear cell, high-grade clear cell, all clear cell, all papillary, and transitional carcinomas were 50% and 94%, 93% and 75%, 92% and 83%, 80% and 94%, and 100% and 99%, respectively. The MRI feature analysis and proposed classification system help predict the histological type and nuclear grade of renal masses.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Carcinoma, Renal Cell - diagnosis</subject><subject>Carcinoma, Renal Cell - pathology</subject><subject>Classification</subject><subject>Contrast Media - administration & dosage</subject><subject>Diagnostic Radiology</subject><subject>Female</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Kidney - pathology</subject><subject>Kidney Neoplasms - diagnosis</subject><subject>Kidney Neoplasms - pathology</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neoplasm Staging</subject><subject>Neuroradiology</subject><subject>Observer Variation</subject><subject>Patients</subject><subject>Predictive Value of Tests</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Risk</subject><subject>Sensitivity and Specificity</subject><subject>Thrombosis</subject><subject>Tumors</subject><subject>Ultrasound</subject><subject>Urogenital</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</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>eNp1kN1LwzAUxYMobk7_AF-k-OBb9d42y8eDDzL8gokg-hzSLtk62mUmLeJ_b2oHA8GX5HDPL-eSQ8g5wjUC8JsAkOeQRpkCn8bjgIyR5lmKIOghGYPMRcqlpCNyEsIaACRSfkxGyIWUCGxMbl_ekrLWIVS2KnVbuU3ibOLNRtdJE8cmJF9Vu0q2ul252i2rMimd96b-ZU_JkdV1MGe7e0I-Hu7fZ0_p_PXxeXY3T8ucyTZFSqm00hihGRRU0IzlnCIWltuCSRCFAcEoYDT1FBcLlAVai2WUjHGTT8jVkLv17rMzoVVNFUpT13pjXBcUh4wBIkTw8g-4dp2PnwkqQyGEhCyLEA5Q6V0I3li19VWj_bdCUH2xaihW9bIvVvXBF7vgrmjMYv9i12QEsgEI0dosjd9v_j_1BzdGgbc</recordid><startdate>20080201</startdate><enddate>20080201</enddate><creator>Pedrosa, Ivan</creator><creator>Chou, Mary T.</creator><creator>Ngo, Long</creator><creator>H. Baroni, Ronaldo</creator><creator>Genega, Elizabeth M.</creator><creator>Galaburda, Laura</creator><creator>DeWolf, William C.</creator><creator>Rofsky, Neil M.</creator><general>Springer-Verlag</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></search><sort><creationdate>20080201</creationdate><title>MR classification of renal masses with pathologic correlation</title><author>Pedrosa, Ivan ; Chou, Mary T. ; Ngo, Long ; H. Baroni, Ronaldo ; Genega, Elizabeth M. ; Galaburda, Laura ; DeWolf, William C. ; Rofsky, Neil M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c369t-14449f9ee8a60b4842637411bf7fb6908be0864010b4a51dd19b1ff1c1dd667e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Carcinoma, Renal Cell - diagnosis</topic><topic>Carcinoma, Renal Cell - pathology</topic><topic>Classification</topic><topic>Contrast Media - administration & dosage</topic><topic>Diagnostic Radiology</topic><topic>Female</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Kidney - pathology</topic><topic>Kidney Neoplasms - diagnosis</topic><topic>Kidney Neoplasms - pathology</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neoplasm Staging</topic><topic>Neuroradiology</topic><topic>Observer Variation</topic><topic>Patients</topic><topic>Predictive Value of Tests</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Risk</topic><topic>Sensitivity and Specificity</topic><topic>Thrombosis</topic><topic>Tumors</topic><topic>Ultrasound</topic><topic>Urogenital</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pedrosa, Ivan</creatorcontrib><creatorcontrib>Chou, Mary T.</creatorcontrib><creatorcontrib>Ngo, Long</creatorcontrib><creatorcontrib>H. Baroni, Ronaldo</creatorcontrib><creatorcontrib>Genega, Elizabeth M.</creatorcontrib><creatorcontrib>Galaburda, Laura</creatorcontrib><creatorcontrib>DeWolf, William C.</creatorcontrib><creatorcontrib>Rofsky, Neil M.</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>Pedrosa, Ivan</au><au>Chou, Mary T.</au><au>Ngo, Long</au><au>H. Baroni, Ronaldo</au><au>Genega, Elizabeth M.</au><au>Galaburda, Laura</au><au>DeWolf, William C.</au><au>Rofsky, Neil M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MR classification of renal masses with pathologic correlation</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2008-02-01</date><risdate>2008</risdate><volume>18</volume><issue>2</issue><spage>365</spage><epage>375</epage><pages>365-375</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis was performed. Each renal mass was assigned to one of eight categories from a proposed MRI classification system. The sensitivity and specificity of the MRI classification system to predict the histologic subtype and nuclear grade was calculated. Subvoxel fat on chemical shift imaging correlated to clear cell type (p < 0.05); sensitivity = 42%, specificity = 100%. Large size, intratumoral necrosis, retroperitoneal vascular collaterals, and renal vein thrombosis predicted high-grade clear cell type (p < 0.05). Small size, peripheral location, low intratumoral SI on T2-weighted images, and low-level enhancement were associated with low-grade papillary carcinomas (p < 0.05). The sensitivity and specificity of the MRI classification system for diagnosing low grade clear cell, high-grade clear cell, all clear cell, all papillary, and transitional carcinomas were 50% and 94%, 93% and 75%, 92% and 83%, 80% and 94%, and 100% and 99%, respectively. The MRI feature analysis and proposed classification system help predict the histological type and nuclear grade of renal masses.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><pmid>17899106</pmid><doi>10.1007/s00330-007-0757-0</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0938-7994 |
| ispartof | European radiology, 2008-02, Vol.18 (2), p.365-375 |
| issn | 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_70260110 |
| source | MEDLINE; SpringerLink Journals |
| subjects | Adult Aged Aged, 80 and over Carcinoma, Renal Cell - diagnosis Carcinoma, Renal Cell - pathology Classification Contrast Media - administration & dosage Diagnostic Radiology Female Humans Image Enhancement - methods Imaging Internal Medicine Interventional Radiology Kidney - pathology Kidney Neoplasms - diagnosis Kidney Neoplasms - pathology Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medicine Medicine & Public Health Middle Aged Neoplasm Staging Neuroradiology Observer Variation Patients Predictive Value of Tests Radiology Retrospective Studies Risk Sensitivity and Specificity Thrombosis Tumors Ultrasound Urogenital |
| title | MR classification of renal masses with pathologic correlation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T04%3A56%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=MR%20classification%20of%20renal%20masses%20with%20pathologic%20correlation&rft.jtitle=European%20radiology&rft.au=Pedrosa,%20Ivan&rft.date=2008-02-01&rft.volume=18&rft.issue=2&rft.spage=365&rft.epage=375&rft.pages=365-375&rft.issn=0938-7994&rft.eissn=1432-1084&rft_id=info:doi/10.1007/s00330-007-0757-0&rft_dat=%3Cproquest_cross%3E1898224221%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=218889022&rft_id=info:pmid/17899106&rfr_iscdi=true |