Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?
Purpose This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases. Materials and methods Twenty-two fp-AML pa...
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| Veröffentlicht in: | Abdominal imaging 2020-08, Vol.45 (8), p.2500-2507 |
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| creator | Ma, Yanqing Cao, Fang Xu, Xiren Ma, Weijun |
| description | Purpose
This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases.
Materials and methods
Twenty-two fp-AML patients and 62 ccRCC patients who were pathologically identified were enrolled in this study, and underwent three-phase (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP) CT examinations. Whole-tumor regions of interest (ROIs) were contoured in ITK software by two radiologists. Radiomic features were dimensionally reduced by means of ANOVA + MW, correlation analysis, and LASSO. Four radiomics logistic classifiers including the UP group, CMP group, NP group, and sum group were built, and the radiomic scores (rad-scores) were calculated. After collecting the qualitative and quantitative conventional CT characteristics, the conventional CT analysis logistic classifier and the radiomics-based logistic classifier were constructed. The receiver operating characteristic curve (ROC) was constructed to evaluate the validity of each classifier.
Results
The area under curve (AUC) of the conventional CT analysis logistic classifier including angular interface, cyst degeneration, and pseudocapsule was 0.935 (95% CI 0.860–0.977). Regarding logistic classifiers for radiomics analysis, the AUCs of the UP group were 0.950 (95% CI 0.895–1.000) and 0.917 (95% CI 0.801–1.000) in the training set and testing set, respectively, which were higher than those of the CMP and NP groups. The AUCs of the sum group were observed to be the highest. The top three selected features for the UP and sum groups belonged to GLCM parameters and histogram parameters. The radiomics-based logistic classifier encompassed cyst degeneration, pseudocapsule, and sum rad-score, and the AUC of the model was 0.988 (95% CI 0.935–1.000).
Conclusion
Whole-tumor radiomics-based CT analysis is superior to conventional CT analysis in the differentiation of fp-AML from ccRCC. Cyst degeneration, pseudocapsule, and sum rad-score are the most significant factors. The radiomics analysis of the UP group shows a higher AUC than that of the CMP and NP groups. |
| doi_str_mv | 10.1007/s00261-020-02414-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2345504985</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2420904583</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-39dbdf38c18875905325260825cf2c3b6285e011005da6822513c5db0947c4743</originalsourceid><addsrcrecordid>eNp9kctq3TAQhkVoSEKSF8giCLrpRu3oZsvZlHLoDQLZpNCdkGU5UbAtR5IbzhP1NatT51K6yEJoBn3_P4N-hM4ovKcA9YcEwCpKgEE5ggrS7KEjxquKAEj15rkWPw_RaUp3AEArSSmTB-iQ00aBquoj9HtjJvxwGwZH8jKGiKPpfBi9TaQ1yXV4c43NZIZt8gm3LmcXcef73kU3ZW-yw73JZA5FaaabotyGwc9hNLiPYcR2cCZi64YBF4EZ1tKa6K2fCnWBbRhnE8ugB59vSzf92hmHHfvP6I8naL83Q3Knj_cx-vHl8_XmG7m8-vp98-mSWF7LTHjTtV3PlaVK1bIByZlkFSgmbc8sbyumpANaflB2plKMScqt7FpoRG1FLfgxerf6zjHcLy5lPfq0W9pMLixJMy6kBNEoWdC3_6F3YYll4UIJBg0IqXih2ErZGFKKrtdz9KOJW01B75LUa5K6JKn_JqmbIjp_tF7a0XXPkqfcCsBXIJWn6cbFl9mv2P4BUF6qCQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2420904583</pqid></control><display><type>article</type><title>Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?</title><source>SpringerLink Journals - AutoHoldings</source><creator>Ma, Yanqing ; Cao, Fang ; Xu, Xiren ; Ma, Weijun</creator><creatorcontrib>Ma, Yanqing ; Cao, Fang ; Xu, Xiren ; Ma, Weijun</creatorcontrib><description>Purpose
This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases.
Materials and methods
Twenty-two fp-AML patients and 62 ccRCC patients who were pathologically identified were enrolled in this study, and underwent three-phase (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP) CT examinations. Whole-tumor regions of interest (ROIs) were contoured in ITK software by two radiologists. Radiomic features were dimensionally reduced by means of ANOVA + MW, correlation analysis, and LASSO. Four radiomics logistic classifiers including the UP group, CMP group, NP group, and sum group were built, and the radiomic scores (rad-scores) were calculated. After collecting the qualitative and quantitative conventional CT characteristics, the conventional CT analysis logistic classifier and the radiomics-based logistic classifier were constructed. The receiver operating characteristic curve (ROC) was constructed to evaluate the validity of each classifier.
Results
The area under curve (AUC) of the conventional CT analysis logistic classifier including angular interface, cyst degeneration, and pseudocapsule was 0.935 (95% CI 0.860–0.977). Regarding logistic classifiers for radiomics analysis, the AUCs of the UP group were 0.950 (95% CI 0.895–1.000) and 0.917 (95% CI 0.801–1.000) in the training set and testing set, respectively, which were higher than those of the CMP and NP groups. The AUCs of the sum group were observed to be the highest. The top three selected features for the UP and sum groups belonged to GLCM parameters and histogram parameters. The radiomics-based logistic classifier encompassed cyst degeneration, pseudocapsule, and sum rad-score, and the AUC of the model was 0.988 (95% CI 0.935–1.000).
Conclusion
Whole-tumor radiomics-based CT analysis is superior to conventional CT analysis in the differentiation of fp-AML from ccRCC. Cyst degeneration, pseudocapsule, and sum rad-score are the most significant factors. The radiomics analysis of the UP group shows a higher AUC than that of the CMP and NP groups.</description><identifier>ISSN: 2366-004X</identifier><identifier>EISSN: 2366-0058</identifier><identifier>DOI: 10.1007/s00261-020-02414-9</identifier><identifier>PMID: 31980867</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Angiomyolipoma ; Bladder ; Cell differentiation ; Classifiers ; Clear cell-type renal cell carcinoma ; Computed tomography ; Correlation analysis ; Cysts ; Degeneration ; Gastroenterology ; Hepatology ; Histograms ; Identification methods ; Imaging ; Itk protein ; Kidney cancer ; Kidneys ; Medicine ; Medicine & Public Health ; Parameters ; Qualitative analysis ; Radiology ; Radiomics ; Retroperitoneum ; Tumors ; Ureters ; Variance analysis</subject><ispartof>Abdominal imaging, 2020-08, Vol.45 (8), p.2500-2507</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-39dbdf38c18875905325260825cf2c3b6285e011005da6822513c5db0947c4743</citedby><cites>FETCH-LOGICAL-c375t-39dbdf38c18875905325260825cf2c3b6285e011005da6822513c5db0947c4743</cites><orcidid>0000-0002-6131-3284</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/s00261-020-02414-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00261-020-02414-9$$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/31980867$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ma, Yanqing</creatorcontrib><creatorcontrib>Cao, Fang</creatorcontrib><creatorcontrib>Xu, Xiren</creatorcontrib><creatorcontrib>Ma, Weijun</creatorcontrib><title>Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?</title><title>Abdominal imaging</title><addtitle>Abdom Radiol</addtitle><addtitle>Abdom Radiol (NY)</addtitle><description>Purpose
This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases.
Materials and methods
Twenty-two fp-AML patients and 62 ccRCC patients who were pathologically identified were enrolled in this study, and underwent three-phase (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP) CT examinations. Whole-tumor regions of interest (ROIs) were contoured in ITK software by two radiologists. Radiomic features were dimensionally reduced by means of ANOVA + MW, correlation analysis, and LASSO. Four radiomics logistic classifiers including the UP group, CMP group, NP group, and sum group were built, and the radiomic scores (rad-scores) were calculated. After collecting the qualitative and quantitative conventional CT characteristics, the conventional CT analysis logistic classifier and the radiomics-based logistic classifier were constructed. The receiver operating characteristic curve (ROC) was constructed to evaluate the validity of each classifier.
Results
The area under curve (AUC) of the conventional CT analysis logistic classifier including angular interface, cyst degeneration, and pseudocapsule was 0.935 (95% CI 0.860–0.977). Regarding logistic classifiers for radiomics analysis, the AUCs of the UP group were 0.950 (95% CI 0.895–1.000) and 0.917 (95% CI 0.801–1.000) in the training set and testing set, respectively, which were higher than those of the CMP and NP groups. The AUCs of the sum group were observed to be the highest. The top three selected features for the UP and sum groups belonged to GLCM parameters and histogram parameters. The radiomics-based logistic classifier encompassed cyst degeneration, pseudocapsule, and sum rad-score, and the AUC of the model was 0.988 (95% CI 0.935–1.000).
Conclusion
Whole-tumor radiomics-based CT analysis is superior to conventional CT analysis in the differentiation of fp-AML from ccRCC. Cyst degeneration, pseudocapsule, and sum rad-score are the most significant factors. The radiomics analysis of the UP group shows a higher AUC than that of the CMP and NP groups.</description><subject>Angiomyolipoma</subject><subject>Bladder</subject><subject>Cell differentiation</subject><subject>Classifiers</subject><subject>Clear cell-type renal cell carcinoma</subject><subject>Computed tomography</subject><subject>Correlation analysis</subject><subject>Cysts</subject><subject>Degeneration</subject><subject>Gastroenterology</subject><subject>Hepatology</subject><subject>Histograms</subject><subject>Identification methods</subject><subject>Imaging</subject><subject>Itk protein</subject><subject>Kidney cancer</subject><subject>Kidneys</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Parameters</subject><subject>Qualitative analysis</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Retroperitoneum</subject><subject>Tumors</subject><subject>Ureters</subject><subject>Variance analysis</subject><issn>2366-004X</issn><issn>2366-0058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kctq3TAQhkVoSEKSF8giCLrpRu3oZsvZlHLoDQLZpNCdkGU5UbAtR5IbzhP1NatT51K6yEJoBn3_P4N-hM4ovKcA9YcEwCpKgEE5ggrS7KEjxquKAEj15rkWPw_RaUp3AEArSSmTB-iQ00aBquoj9HtjJvxwGwZH8jKGiKPpfBi9TaQ1yXV4c43NZIZt8gm3LmcXcef73kU3ZW-yw73JZA5FaaabotyGwc9hNLiPYcR2cCZi64YBF4EZ1tKa6K2fCnWBbRhnE8ugB59vSzf92hmHHfvP6I8naL83Q3Knj_cx-vHl8_XmG7m8-vp98-mSWF7LTHjTtV3PlaVK1bIByZlkFSgmbc8sbyumpANaflB2plKMScqt7FpoRG1FLfgxerf6zjHcLy5lPfq0W9pMLixJMy6kBNEoWdC3_6F3YYll4UIJBg0IqXih2ErZGFKKrtdz9KOJW01B75LUa5K6JKn_JqmbIjp_tF7a0XXPkqfcCsBXIJWn6cbFl9mv2P4BUF6qCQ</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Ma, Yanqing</creator><creator>Cao, Fang</creator><creator>Xu, Xiren</creator><creator>Ma, Weijun</creator><general>Springer US</general><general>Springer Nature B.V</general><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/0000-0002-6131-3284</orcidid></search><sort><creationdate>20200801</creationdate><title>Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?</title><author>Ma, Yanqing ; Cao, Fang ; Xu, Xiren ; Ma, Weijun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-39dbdf38c18875905325260825cf2c3b6285e011005da6822513c5db0947c4743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Angiomyolipoma</topic><topic>Bladder</topic><topic>Cell differentiation</topic><topic>Classifiers</topic><topic>Clear cell-type renal cell carcinoma</topic><topic>Computed tomography</topic><topic>Correlation analysis</topic><topic>Cysts</topic><topic>Degeneration</topic><topic>Gastroenterology</topic><topic>Hepatology</topic><topic>Histograms</topic><topic>Identification methods</topic><topic>Imaging</topic><topic>Itk protein</topic><topic>Kidney cancer</topic><topic>Kidneys</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Parameters</topic><topic>Qualitative analysis</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Retroperitoneum</topic><topic>Tumors</topic><topic>Ureters</topic><topic>Variance analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Yanqing</creatorcontrib><creatorcontrib>Cao, Fang</creatorcontrib><creatorcontrib>Xu, Xiren</creatorcontrib><creatorcontrib>Ma, Weijun</creatorcontrib><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>Ma, Yanqing</au><au>Cao, Fang</au><au>Xu, Xiren</au><au>Ma, Weijun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?</atitle><jtitle>Abdominal imaging</jtitle><stitle>Abdom Radiol</stitle><addtitle>Abdom Radiol (NY)</addtitle><date>2020-08-01</date><risdate>2020</risdate><volume>45</volume><issue>8</issue><spage>2500</spage><epage>2507</epage><pages>2500-2507</pages><issn>2366-004X</issn><eissn>2366-0058</eissn><abstract>Purpose
This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases.
Materials and methods
Twenty-two fp-AML patients and 62 ccRCC patients who were pathologically identified were enrolled in this study, and underwent three-phase (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP) CT examinations. Whole-tumor regions of interest (ROIs) were contoured in ITK software by two radiologists. Radiomic features were dimensionally reduced by means of ANOVA + MW, correlation analysis, and LASSO. Four radiomics logistic classifiers including the UP group, CMP group, NP group, and sum group were built, and the radiomic scores (rad-scores) were calculated. After collecting the qualitative and quantitative conventional CT characteristics, the conventional CT analysis logistic classifier and the radiomics-based logistic classifier were constructed. The receiver operating characteristic curve (ROC) was constructed to evaluate the validity of each classifier.
Results
The area under curve (AUC) of the conventional CT analysis logistic classifier including angular interface, cyst degeneration, and pseudocapsule was 0.935 (95% CI 0.860–0.977). Regarding logistic classifiers for radiomics analysis, the AUCs of the UP group were 0.950 (95% CI 0.895–1.000) and 0.917 (95% CI 0.801–1.000) in the training set and testing set, respectively, which were higher than those of the CMP and NP groups. The AUCs of the sum group were observed to be the highest. The top three selected features for the UP and sum groups belonged to GLCM parameters and histogram parameters. The radiomics-based logistic classifier encompassed cyst degeneration, pseudocapsule, and sum rad-score, and the AUC of the model was 0.988 (95% CI 0.935–1.000).
Conclusion
Whole-tumor radiomics-based CT analysis is superior to conventional CT analysis in the differentiation of fp-AML from ccRCC. Cyst degeneration, pseudocapsule, and sum rad-score are the most significant factors. The radiomics analysis of the UP group shows a higher AUC than that of the CMP and NP groups.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>31980867</pmid><doi>10.1007/s00261-020-02414-9</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-6131-3284</orcidid></addata></record> |
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| subjects | Angiomyolipoma Bladder Cell differentiation Classifiers Clear cell-type renal cell carcinoma Computed tomography Correlation analysis Cysts Degeneration Gastroenterology Hepatology Histograms Identification methods Imaging Itk protein Kidney cancer Kidneys Medicine Medicine & Public Health Parameters Qualitative analysis Radiology Radiomics Retroperitoneum Tumors Ureters Variance analysis |
| title | Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis? |
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