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Monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma
Purpose To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC). Materials and Me...
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| Published in: | Journal of magnetic resonance imaging 2017-07, Vol.46 (1), p.240-247 |
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| container_title | Journal of magnetic resonance imaging |
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| creator | Li, Haojie Liang, Lili Li, Anqin Hu, Yao Hu, Daoyu Li, Zhen Kamel, Ihab R. |
| description | Purpose
To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC).
Materials and Methods
One hundred thirty‐one patients with pathologically confirmed MFAML (n = 27) or ccRCC (n = 104) underwent multi‐b value DWI (0∼1700 s/mm2) imaging at 3.0 Tesla MRI. An isotropic apparent diffusion coefficient (ADC) was calculated from diffusion‐weighted images by using a monoexponential model. A pseudo‐ADC (Dp), true ADC (Dt), and perfusion fraction (fp) were calculated from diffusion‐weighted images by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from diffusion‐weighted images by using a stretched exponential model. All parameters were compared between MFAML and ccRCC by using the Student's t test. Receiver operating characteristic and intraclass correlation coefficient analysis were used for statistical evaluations.
Results
ADC, Dt, and α values were significantly lower in the MFAML group than in the ccRCC group (P |
| doi_str_mv | 10.1002/jmri.25524 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_1841801659</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1841801659</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4244-8e55d1ac78936662b5378d0a1c80df47de26c66b1893072e26a3d0f8f2b0d5cc3</originalsourceid><addsrcrecordid>eNpdkU1u1TAUhS0EoqUwYQHIEhMGpNhO7DjMUFWgVSsEgnHk2DevfiT2q520vBlLYFGshJX0vrQgYORzdT4f3R9CnnJ2yBkTr9Zj8odCSlHdI_tcClEIqdV91EyWBdes3iOPcl4zxpqmkg_Jnqi1bLQs98nP8xgifNvEAGHyZnhJO_9PaYKjeUow2Qtw9C-LOt_3c_Yx_Pr-4xr86mJCwI9m5cOKjtHBkF_Tj7NBejKTvwKMjqNJXyFl2se0BEBa0qbdH5QYawcwiVoYUJpkfcA_SxejD5g-0N5MWK8waxsHv0H7MXnQmyHDk7v3gHx5e_z56H1x9uHdydGbs8JWoqoKDVI6bmytm1IpJTpZ1toxw61mrq9qB0JZpTqOPqsFVqZ0rNe96JiT1pYH5MVt7ibFyxny1I4-7zo1AeKcW64r3DZXskH0-X_oOs4J50OqYQp3r5RE6tkdNXcjuHaTcMK0bX_fBwF-C1z7AbZ_fM7a3eXb3eXb5fLt6fmnk0WVNwJGp4E</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1906853665</pqid></control><display><type>article</type><title>Monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma</title><source>Wiley-Blackwell Read & Publish Collection</source><creator>Li, Haojie ; Liang, Lili ; Li, Anqin ; Hu, Yao ; Hu, Daoyu ; Li, Zhen ; Kamel, Ihab R.</creator><creatorcontrib>Li, Haojie ; Liang, Lili ; Li, Anqin ; Hu, Yao ; Hu, Daoyu ; Li, Zhen ; Kamel, Ihab R.</creatorcontrib><description>Purpose
To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC).
Materials and Methods
One hundred thirty‐one patients with pathologically confirmed MFAML (n = 27) or ccRCC (n = 104) underwent multi‐b value DWI (0∼1700 s/mm2) imaging at 3.0 Tesla MRI. An isotropic apparent diffusion coefficient (ADC) was calculated from diffusion‐weighted images by using a monoexponential model. A pseudo‐ADC (Dp), true ADC (Dt), and perfusion fraction (fp) were calculated from diffusion‐weighted images by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from diffusion‐weighted images by using a stretched exponential model. All parameters were compared between MFAML and ccRCC by using the Student's t test. Receiver operating characteristic and intraclass correlation coefficient analysis were used for statistical evaluations.
Results
ADC, Dt, and α values were significantly lower in the MFAML group than in the ccRCC group (P < 0.001). Dp, fp, and DDC values were slightly higher in the MFAML group than in the ccRCC group; however, the difference was not significant (P = 0.136, 0.090, and 0.424, respectively). The AUC values for both α (0.953) and Dt (0.964) were significantly higher than those for ADC (0860), Dp (0.605), fp (0.596), and DDC (0.477) in the differentiation of MFAML from ccRCC (P < 0.001).
Conclusion
Water molecular diffusion heterogeneity index (α) and Dt may provide additional information and could lead to improved differentiation with better sensitivity and specificity between MFAML and ccRCC compared with conventional diffusion parameters.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. MAGN. RESON. IMAGING 2017;46:240–247</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.25524</identifier><identifier>PMID: 27859853</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Aged ; Algorithms ; Angiomyolipoma ; Angiomyolipoma - diagnostic imaging ; Angiomyolipoma - pathology ; Biomarkers ; Carcinoma, Renal Cell - diagnostic imaging ; Carcinoma, Renal Cell - pathology ; clear cell renal cell carcinoma ; Clear cell-type renal cell carcinoma ; Computer Simulation ; Correlation analysis ; Correlation coefficient ; Correlation coefficients ; Diagnosis, Differential ; Differentiation ; Diffusion coefficient ; Diffusion Magnetic Resonance Imaging - methods ; diffusion‐weighted imaging ; exponential models ; Female ; Heterogeneity ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Kidney cancer ; Kidney Neoplasms - diagnostic imaging ; Kidney Neoplasms - pathology ; Magnetic resonance imaging ; Male ; Mathematical models ; Middle Aged ; Models, Biological ; Models, Statistical ; Molecular diffusion ; Motion ; Perfusion ; Reproducibility of Results ; Sensitivity and Specificity</subject><ispartof>Journal of magnetic resonance imaging, 2017-07, Vol.46 (1), p.240-247</ispartof><rights>2016 International Society for Magnetic Resonance in Medicine</rights><rights>2016 International Society for Magnetic Resonance in Medicine.</rights><rights>2017 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4244-8e55d1ac78936662b5378d0a1c80df47de26c66b1893072e26a3d0f8f2b0d5cc3</citedby><orcidid>0000-0001-8037-4245</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/27859853$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Haojie</creatorcontrib><creatorcontrib>Liang, Lili</creatorcontrib><creatorcontrib>Li, Anqin</creatorcontrib><creatorcontrib>Hu, Yao</creatorcontrib><creatorcontrib>Hu, Daoyu</creatorcontrib><creatorcontrib>Li, Zhen</creatorcontrib><creatorcontrib>Kamel, Ihab R.</creatorcontrib><title>Monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Purpose
To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC).
Materials and Methods
One hundred thirty‐one patients with pathologically confirmed MFAML (n = 27) or ccRCC (n = 104) underwent multi‐b value DWI (0∼1700 s/mm2) imaging at 3.0 Tesla MRI. An isotropic apparent diffusion coefficient (ADC) was calculated from diffusion‐weighted images by using a monoexponential model. A pseudo‐ADC (Dp), true ADC (Dt), and perfusion fraction (fp) were calculated from diffusion‐weighted images by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from diffusion‐weighted images by using a stretched exponential model. All parameters were compared between MFAML and ccRCC by using the Student's t test. Receiver operating characteristic and intraclass correlation coefficient analysis were used for statistical evaluations.
Results
ADC, Dt, and α values were significantly lower in the MFAML group than in the ccRCC group (P < 0.001). Dp, fp, and DDC values were slightly higher in the MFAML group than in the ccRCC group; however, the difference was not significant (P = 0.136, 0.090, and 0.424, respectively). The AUC values for both α (0.953) and Dt (0.964) were significantly higher than those for ADC (0860), Dp (0.605), fp (0.596), and DDC (0.477) in the differentiation of MFAML from ccRCC (P < 0.001).
Conclusion
Water molecular diffusion heterogeneity index (α) and Dt may provide additional information and could lead to improved differentiation with better sensitivity and specificity between MFAML and ccRCC compared with conventional diffusion parameters.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. MAGN. RESON. IMAGING 2017;46:240–247</description><subject>Aged</subject><subject>Algorithms</subject><subject>Angiomyolipoma</subject><subject>Angiomyolipoma - diagnostic imaging</subject><subject>Angiomyolipoma - pathology</subject><subject>Biomarkers</subject><subject>Carcinoma, Renal Cell - diagnostic imaging</subject><subject>Carcinoma, Renal Cell - pathology</subject><subject>clear cell renal cell carcinoma</subject><subject>Clear cell-type renal cell carcinoma</subject><subject>Computer Simulation</subject><subject>Correlation analysis</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Diagnosis, Differential</subject><subject>Differentiation</subject><subject>Diffusion coefficient</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>diffusion‐weighted imaging</subject><subject>exponential models</subject><subject>Female</subject><subject>Heterogeneity</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Kidney cancer</subject><subject>Kidney Neoplasms - diagnostic imaging</subject><subject>Kidney Neoplasms - pathology</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Middle Aged</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Molecular diffusion</subject><subject>Motion</subject><subject>Perfusion</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNpdkU1u1TAUhS0EoqUwYQHIEhMGpNhO7DjMUFWgVSsEgnHk2DevfiT2q520vBlLYFGshJX0vrQgYORzdT4f3R9CnnJ2yBkTr9Zj8odCSlHdI_tcClEIqdV91EyWBdes3iOPcl4zxpqmkg_Jnqi1bLQs98nP8xgifNvEAGHyZnhJO_9PaYKjeUow2Qtw9C-LOt_3c_Yx_Pr-4xr86mJCwI9m5cOKjtHBkF_Tj7NBejKTvwKMjqNJXyFl2se0BEBa0qbdH5QYawcwiVoYUJpkfcA_SxejD5g-0N5MWK8waxsHv0H7MXnQmyHDk7v3gHx5e_z56H1x9uHdydGbs8JWoqoKDVI6bmytm1IpJTpZ1toxw61mrq9qB0JZpTqOPqsFVqZ0rNe96JiT1pYH5MVt7ibFyxny1I4-7zo1AeKcW64r3DZXskH0-X_oOs4J50OqYQp3r5RE6tkdNXcjuHaTcMK0bX_fBwF-C1z7AbZ_fM7a3eXb3eXb5fLt6fmnk0WVNwJGp4E</recordid><startdate>201707</startdate><enddate>201707</enddate><creator>Li, Haojie</creator><creator>Liang, Lili</creator><creator>Li, Anqin</creator><creator>Hu, Yao</creator><creator>Hu, Daoyu</creator><creator>Li, Zhen</creator><creator>Kamel, Ihab R.</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8037-4245</orcidid></search><sort><creationdate>201707</creationdate><title>Monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma</title><author>Li, Haojie ; Liang, Lili ; Li, Anqin ; Hu, Yao ; Hu, Daoyu ; Li, Zhen ; Kamel, Ihab R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4244-8e55d1ac78936662b5378d0a1c80df47de26c66b1893072e26a3d0f8f2b0d5cc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aged</topic><topic>Algorithms</topic><topic>Angiomyolipoma</topic><topic>Angiomyolipoma - diagnostic imaging</topic><topic>Angiomyolipoma - pathology</topic><topic>Biomarkers</topic><topic>Carcinoma, Renal Cell - diagnostic imaging</topic><topic>Carcinoma, Renal Cell - pathology</topic><topic>clear cell renal cell carcinoma</topic><topic>Clear cell-type renal cell carcinoma</topic><topic>Computer Simulation</topic><topic>Correlation analysis</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Diagnosis, Differential</topic><topic>Differentiation</topic><topic>Diffusion coefficient</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>diffusion‐weighted imaging</topic><topic>exponential models</topic><topic>Female</topic><topic>Heterogeneity</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Kidney cancer</topic><topic>Kidney Neoplasms - diagnostic imaging</topic><topic>Kidney Neoplasms - pathology</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Middle Aged</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Molecular diffusion</topic><topic>Motion</topic><topic>Perfusion</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Haojie</creatorcontrib><creatorcontrib>Liang, Lili</creatorcontrib><creatorcontrib>Li, Anqin</creatorcontrib><creatorcontrib>Hu, Yao</creatorcontrib><creatorcontrib>Hu, Daoyu</creatorcontrib><creatorcontrib>Li, Zhen</creatorcontrib><creatorcontrib>Kamel, Ihab R.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Haojie</au><au>Liang, Lili</au><au>Li, Anqin</au><au>Hu, Yao</au><au>Hu, Daoyu</au><au>Li, Zhen</au><au>Kamel, Ihab R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2017-07</date><risdate>2017</risdate><volume>46</volume><issue>1</issue><spage>240</spage><epage>247</epage><pages>240-247</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Purpose
To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC).
Materials and Methods
One hundred thirty‐one patients with pathologically confirmed MFAML (n = 27) or ccRCC (n = 104) underwent multi‐b value DWI (0∼1700 s/mm2) imaging at 3.0 Tesla MRI. An isotropic apparent diffusion coefficient (ADC) was calculated from diffusion‐weighted images by using a monoexponential model. A pseudo‐ADC (Dp), true ADC (Dt), and perfusion fraction (fp) were calculated from diffusion‐weighted images by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from diffusion‐weighted images by using a stretched exponential model. All parameters were compared between MFAML and ccRCC by using the Student's t test. Receiver operating characteristic and intraclass correlation coefficient analysis were used for statistical evaluations.
Results
ADC, Dt, and α values were significantly lower in the MFAML group than in the ccRCC group (P < 0.001). Dp, fp, and DDC values were slightly higher in the MFAML group than in the ccRCC group; however, the difference was not significant (P = 0.136, 0.090, and 0.424, respectively). The AUC values for both α (0.953) and Dt (0.964) were significantly higher than those for ADC (0860), Dp (0.605), fp (0.596), and DDC (0.477) in the differentiation of MFAML from ccRCC (P < 0.001).
Conclusion
Water molecular diffusion heterogeneity index (α) and Dt may provide additional information and could lead to improved differentiation with better sensitivity and specificity between MFAML and ccRCC compared with conventional diffusion parameters.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. MAGN. RESON. IMAGING 2017;46:240–247</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>27859853</pmid><doi>10.1002/jmri.25524</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-8037-4245</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Algorithms Angiomyolipoma Angiomyolipoma - diagnostic imaging Angiomyolipoma - pathology Biomarkers Carcinoma, Renal Cell - diagnostic imaging Carcinoma, Renal Cell - pathology clear cell renal cell carcinoma Clear cell-type renal cell carcinoma Computer Simulation Correlation analysis Correlation coefficient Correlation coefficients Diagnosis, Differential Differentiation Diffusion coefficient Diffusion Magnetic Resonance Imaging - methods diffusion‐weighted imaging exponential models Female Heterogeneity Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Kidney cancer Kidney Neoplasms - diagnostic imaging Kidney Neoplasms - pathology Magnetic resonance imaging Male Mathematical models Middle Aged Models, Biological Models, Statistical Molecular diffusion Motion Perfusion Reproducibility of Results Sensitivity and Specificity |
| title | Monoexponential, biexponential, and stretched exponential diffusion‐weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma |
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