Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation
Purpose The aim of the present study is to identify predictive imaging findings and construct a diagnostic model for differentiating renal cell carcinoma (RCC) with and without sarcomatoid dedifferentiation (sRCC and non-sRCC). Methods This study is a single-center retrospective study. All patients...
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| Veröffentlicht in: | Abdominal imaging 2022-06, Vol.47 (6), p.2168-2177 |
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| creator | Takeuchi, Mitsuru Froemming, Adam T. Kawashima, Akira Thapa, Prabin Carter, Rickey E. Cheville, John C. Thompson, R. Houston Takahashi, Naoki |
| description | Purpose
The aim of the present study is to identify predictive imaging findings and construct a diagnostic model for differentiating renal cell carcinoma (RCC) with and without sarcomatoid dedifferentiation (sRCC and non-sRCC).
Methods
This study is a single-center retrospective study. All patients had magnetic resonance imaging (MRI) with gradient-echo T1-weighted images, single-shot T2-weighted images (T2WI), and enhanced nephrographic phase images. Forty pathologically confirmed sRCCs and 80 non-sRCCs were included in this study. Control cases were selected by matching the tumor diameter and the year of MRI. Two radiologists independently evaluated the following findings: growth pattern, presence of low-intensity area on T2WI in the tumor (T2LIA), presence of non-enhancing area, local tumor stage, and presence of regional lymphadenopathy. Two radiologists measured the diameter of the tumor, T2LIA, and the non-enhancing area. Multivariable logistic regression analysis was used to identify independent predictive factors for differentiating sRCC from non-sRCC. Selected variables were entered in the logistic regression model, and the area under the curve (AUC) was calculated for each reader with 95% confidence intervals (CIs).
Results
Larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 were associated with sRCC, and selected for the subsequent construction of a logistic regression model. With this model, the AUCs were 0.76 (95% CI, 0.66–0.85) and 0.70 (95% CI, 0.59–0.81) for prediction of sRCC.
Conclusion
In conclusion, larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 are predictive findings of sRCC. As a result, the model constructed using these findings demonstrated a moderate degree of diagnostic accuracy. |
| doi_str_mv | 10.1007/s00261-022-03501-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2647654725</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2664197210</sourcerecordid><originalsourceid>FETCH-LOGICAL-c305t-19e940664c21a70113c1d6acf7c9135996329f7085f0d2c0c85fca7ea461ffb23</originalsourceid><addsrcrecordid>eNp9kd1OHSEUhUlTU431BXrRkPRGL8buDQMzXDam_iQaE2OvCTJwxJyBU5iJ6Rv1Mcvx-FOb1BvYO3xrbWAR8gnhEAG6rwWASWyAsQa4AGzUO7LDuJQNgOjf_1Vvk71S7gAApUBk4gPZ5oL32Mt-h_y-MIvopmBpdiVFE62jYTSLEBd0_-Lq7IDeuuWq0CF477KLUzCTq2w0S2rdsi4m2xDTaOh9mG5pqW1tphSGV5qQIvU5jf-Xpnl6S_2RbHmzLG7vcd8lP46_Xx-dNueXJ2dH384by0FMDSqnWpCytQxNB4jc4iCN9Z1VyIVSkjPlO-iFh4FZsLWwpnOmlej9DeO7ZH_ju8rp5-zKpMdQ1tc10aW5aCbbToq2Y6KiX_5B79Kc6_PWlGxRdQyhUmxD2ZxKyc7rVa4_nH9pBL2OUm-i1DVK_RClVlX0-dF6vhnd8Cx5Cq4CfAOUehQXLr_MfsP2D7OIrDE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2664197210</pqid></control><display><type>article</type><title>Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Takeuchi, Mitsuru ; Froemming, Adam T. ; Kawashima, Akira ; Thapa, Prabin ; Carter, Rickey E. ; Cheville, John C. ; Thompson, R. Houston ; Takahashi, Naoki</creator><creatorcontrib>Takeuchi, Mitsuru ; Froemming, Adam T. ; Kawashima, Akira ; Thapa, Prabin ; Carter, Rickey E. ; Cheville, John C. ; Thompson, R. Houston ; Takahashi, Naoki</creatorcontrib><description>Purpose
The aim of the present study is to identify predictive imaging findings and construct a diagnostic model for differentiating renal cell carcinoma (RCC) with and without sarcomatoid dedifferentiation (sRCC and non-sRCC).
Methods
This study is a single-center retrospective study. All patients had magnetic resonance imaging (MRI) with gradient-echo T1-weighted images, single-shot T2-weighted images (T2WI), and enhanced nephrographic phase images. Forty pathologically confirmed sRCCs and 80 non-sRCCs were included in this study. Control cases were selected by matching the tumor diameter and the year of MRI. Two radiologists independently evaluated the following findings: growth pattern, presence of low-intensity area on T2WI in the tumor (T2LIA), presence of non-enhancing area, local tumor stage, and presence of regional lymphadenopathy. Two radiologists measured the diameter of the tumor, T2LIA, and the non-enhancing area. Multivariable logistic regression analysis was used to identify independent predictive factors for differentiating sRCC from non-sRCC. Selected variables were entered in the logistic regression model, and the area under the curve (AUC) was calculated for each reader with 95% confidence intervals (CIs).
Results
Larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 were associated with sRCC, and selected for the subsequent construction of a logistic regression model. With this model, the AUCs were 0.76 (95% CI, 0.66–0.85) and 0.70 (95% CI, 0.59–0.81) for prediction of sRCC.
Conclusion
In conclusion, larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 are predictive findings of sRCC. As a result, the model constructed using these findings demonstrated a moderate degree of diagnostic accuracy.</description><identifier>ISSN: 2366-0058</identifier><identifier>ISSN: 2366-004X</identifier><identifier>EISSN: 2366-0058</identifier><identifier>DOI: 10.1007/s00261-022-03501-9</identifier><identifier>PMID: 35381868</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Bladder ; Carcinoma, Renal Cell - diagnostic imaging ; Carcinoma, Renal Cell - pathology ; Cell differentiation ; Confidence intervals ; Diagnostic systems ; Diameters ; Differentiation ; Female ; Gastroenterology ; Growth patterns ; Hepatology ; Humans ; Image enhancement ; Imaging ; Kidney cancer ; Kidney Neoplasms - diagnostic imaging ; Kidney Neoplasms - pathology ; Kidneys ; Lymphadenopathy ; Magnetic Resonance Imaging ; Male ; Medical diagnosis ; Medical imaging ; Medicine ; Medicine & Public Health ; Radiology ; Regression analysis ; Regression models ; Renal cell carcinoma ; Resonance ; Retroperitoneum ; Retrospective Studies ; Sarcoma ; Statistical analysis ; Tumors ; Ureters</subject><ispartof>Abdominal imaging, 2022-06, Vol.47 (6), p.2168-2177</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</rights><rights>2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c305t-19e940664c21a70113c1d6acf7c9135996329f7085f0d2c0c85fca7ea461ffb23</citedby><cites>FETCH-LOGICAL-c305t-19e940664c21a70113c1d6acf7c9135996329f7085f0d2c0c85fca7ea461ffb23</cites><orcidid>0000-0002-7946-6078</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/s00261-022-03501-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00261-022-03501-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35381868$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Takeuchi, Mitsuru</creatorcontrib><creatorcontrib>Froemming, Adam T.</creatorcontrib><creatorcontrib>Kawashima, Akira</creatorcontrib><creatorcontrib>Thapa, Prabin</creatorcontrib><creatorcontrib>Carter, Rickey E.</creatorcontrib><creatorcontrib>Cheville, John C.</creatorcontrib><creatorcontrib>Thompson, R. Houston</creatorcontrib><creatorcontrib>Takahashi, Naoki</creatorcontrib><title>Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation</title><title>Abdominal imaging</title><addtitle>Abdom Radiol</addtitle><addtitle>Abdom Radiol (NY)</addtitle><description>Purpose
The aim of the present study is to identify predictive imaging findings and construct a diagnostic model for differentiating renal cell carcinoma (RCC) with and without sarcomatoid dedifferentiation (sRCC and non-sRCC).
Methods
This study is a single-center retrospective study. All patients had magnetic resonance imaging (MRI) with gradient-echo T1-weighted images, single-shot T2-weighted images (T2WI), and enhanced nephrographic phase images. Forty pathologically confirmed sRCCs and 80 non-sRCCs were included in this study. Control cases were selected by matching the tumor diameter and the year of MRI. Two radiologists independently evaluated the following findings: growth pattern, presence of low-intensity area on T2WI in the tumor (T2LIA), presence of non-enhancing area, local tumor stage, and presence of regional lymphadenopathy. Two radiologists measured the diameter of the tumor, T2LIA, and the non-enhancing area. Multivariable logistic regression analysis was used to identify independent predictive factors for differentiating sRCC from non-sRCC. Selected variables were entered in the logistic regression model, and the area under the curve (AUC) was calculated for each reader with 95% confidence intervals (CIs).
Results
Larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 were associated with sRCC, and selected for the subsequent construction of a logistic regression model. With this model, the AUCs were 0.76 (95% CI, 0.66–0.85) and 0.70 (95% CI, 0.59–0.81) for prediction of sRCC.
Conclusion
In conclusion, larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 are predictive findings of sRCC. As a result, the model constructed using these findings demonstrated a moderate degree of diagnostic accuracy.</description><subject>Bladder</subject><subject>Carcinoma, Renal Cell - diagnostic imaging</subject><subject>Carcinoma, Renal Cell - pathology</subject><subject>Cell differentiation</subject><subject>Confidence intervals</subject><subject>Diagnostic systems</subject><subject>Diameters</subject><subject>Differentiation</subject><subject>Female</subject><subject>Gastroenterology</subject><subject>Growth patterns</subject><subject>Hepatology</subject><subject>Humans</subject><subject>Image enhancement</subject><subject>Imaging</subject><subject>Kidney cancer</subject><subject>Kidney Neoplasms - diagnostic imaging</subject><subject>Kidney Neoplasms - pathology</subject><subject>Kidneys</subject><subject>Lymphadenopathy</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Renal cell carcinoma</subject><subject>Resonance</subject><subject>Retroperitoneum</subject><subject>Retrospective Studies</subject><subject>Sarcoma</subject><subject>Statistical analysis</subject><subject>Tumors</subject><subject>Ureters</subject><issn>2366-0058</issn><issn>2366-004X</issn><issn>2366-0058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</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>eNp9kd1OHSEUhUlTU431BXrRkPRGL8buDQMzXDam_iQaE2OvCTJwxJyBU5iJ6Rv1Mcvx-FOb1BvYO3xrbWAR8gnhEAG6rwWASWyAsQa4AGzUO7LDuJQNgOjf_1Vvk71S7gAApUBk4gPZ5oL32Mt-h_y-MIvopmBpdiVFE62jYTSLEBd0_-Lq7IDeuuWq0CF477KLUzCTq2w0S2rdsi4m2xDTaOh9mG5pqW1tphSGV5qQIvU5jf-Xpnl6S_2RbHmzLG7vcd8lP46_Xx-dNueXJ2dH384by0FMDSqnWpCytQxNB4jc4iCN9Z1VyIVSkjPlO-iFh4FZsLWwpnOmlej9DeO7ZH_ju8rp5-zKpMdQ1tc10aW5aCbbToq2Y6KiX_5B79Kc6_PWlGxRdQyhUmxD2ZxKyc7rVa4_nH9pBL2OUm-i1DVK_RClVlX0-dF6vhnd8Cx5Cq4CfAOUehQXLr_MfsP2D7OIrDE</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Takeuchi, Mitsuru</creator><creator>Froemming, Adam T.</creator><creator>Kawashima, Akira</creator><creator>Thapa, Prabin</creator><creator>Carter, Rickey E.</creator><creator>Cheville, John C.</creator><creator>Thompson, R. Houston</creator><creator>Takahashi, Naoki</creator><general>Springer US</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>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>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7Z</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/0000-0002-7946-6078</orcidid></search><sort><creationdate>20220601</creationdate><title>Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation</title><author>Takeuchi, Mitsuru ; Froemming, Adam T. ; Kawashima, Akira ; Thapa, Prabin ; Carter, Rickey E. ; Cheville, John C. ; Thompson, R. Houston ; Takahashi, Naoki</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c305t-19e940664c21a70113c1d6acf7c9135996329f7085f0d2c0c85fca7ea461ffb23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Bladder</topic><topic>Carcinoma, Renal Cell - diagnostic imaging</topic><topic>Carcinoma, Renal Cell - pathology</topic><topic>Cell differentiation</topic><topic>Confidence intervals</topic><topic>Diagnostic systems</topic><topic>Diameters</topic><topic>Differentiation</topic><topic>Female</topic><topic>Gastroenterology</topic><topic>Growth patterns</topic><topic>Hepatology</topic><topic>Humans</topic><topic>Image enhancement</topic><topic>Imaging</topic><topic>Kidney cancer</topic><topic>Kidney Neoplasms - diagnostic imaging</topic><topic>Kidney Neoplasms - pathology</topic><topic>Kidneys</topic><topic>Lymphadenopathy</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Renal cell carcinoma</topic><topic>Resonance</topic><topic>Retroperitoneum</topic><topic>Retrospective Studies</topic><topic>Sarcoma</topic><topic>Statistical analysis</topic><topic>Tumors</topic><topic>Ureters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Takeuchi, Mitsuru</creatorcontrib><creatorcontrib>Froemming, Adam T.</creatorcontrib><creatorcontrib>Kawashima, Akira</creatorcontrib><creatorcontrib>Thapa, Prabin</creatorcontrib><creatorcontrib>Carter, Rickey E.</creatorcontrib><creatorcontrib>Cheville, John C.</creatorcontrib><creatorcontrib>Thompson, R. Houston</creatorcontrib><creatorcontrib>Takahashi, Naoki</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>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 Computer Science Collection</collection><collection>Computer Science Database</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>Biochemistry Abstracts 1</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>Abdominal imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Takeuchi, Mitsuru</au><au>Froemming, Adam T.</au><au>Kawashima, Akira</au><au>Thapa, Prabin</au><au>Carter, Rickey E.</au><au>Cheville, John C.</au><au>Thompson, R. Houston</au><au>Takahashi, Naoki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation</atitle><jtitle>Abdominal imaging</jtitle><stitle>Abdom Radiol</stitle><addtitle>Abdom Radiol (NY)</addtitle><date>2022-06-01</date><risdate>2022</risdate><volume>47</volume><issue>6</issue><spage>2168</spage><epage>2177</epage><pages>2168-2177</pages><issn>2366-0058</issn><issn>2366-004X</issn><eissn>2366-0058</eissn><abstract>Purpose
The aim of the present study is to identify predictive imaging findings and construct a diagnostic model for differentiating renal cell carcinoma (RCC) with and without sarcomatoid dedifferentiation (sRCC and non-sRCC).
Methods
This study is a single-center retrospective study. All patients had magnetic resonance imaging (MRI) with gradient-echo T1-weighted images, single-shot T2-weighted images (T2WI), and enhanced nephrographic phase images. Forty pathologically confirmed sRCCs and 80 non-sRCCs were included in this study. Control cases were selected by matching the tumor diameter and the year of MRI. Two radiologists independently evaluated the following findings: growth pattern, presence of low-intensity area on T2WI in the tumor (T2LIA), presence of non-enhancing area, local tumor stage, and presence of regional lymphadenopathy. Two radiologists measured the diameter of the tumor, T2LIA, and the non-enhancing area. Multivariable logistic regression analysis was used to identify independent predictive factors for differentiating sRCC from non-sRCC. Selected variables were entered in the logistic regression model, and the area under the curve (AUC) was calculated for each reader with 95% confidence intervals (CIs).
Results
Larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 were associated with sRCC, and selected for the subsequent construction of a logistic regression model. With this model, the AUCs were 0.76 (95% CI, 0.66–0.85) and 0.70 (95% CI, 0.59–0.81) for prediction of sRCC.
Conclusion
In conclusion, larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 are predictive findings of sRCC. As a result, the model constructed using these findings demonstrated a moderate degree of diagnostic accuracy.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>35381868</pmid><doi>10.1007/s00261-022-03501-9</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-7946-6078</orcidid></addata></record> |
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| subjects | Bladder Carcinoma, Renal Cell - diagnostic imaging Carcinoma, Renal Cell - pathology Cell differentiation Confidence intervals Diagnostic systems Diameters Differentiation Female Gastroenterology Growth patterns Hepatology Humans Image enhancement Imaging Kidney cancer Kidney Neoplasms - diagnostic imaging Kidney Neoplasms - pathology Kidneys Lymphadenopathy Magnetic Resonance Imaging Male Medical diagnosis Medical imaging Medicine Medicine & Public Health Radiology Regression analysis Regression models Renal cell carcinoma Resonance Retroperitoneum Retrospective Studies Sarcoma Statistical analysis Tumors Ureters |
| title | Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation |
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