Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease
Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates. To impr...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2023-10, Vol.58 (4), p.1153-1160 |
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| creator | Dev, Hreedi Zhu, Chenglin Sharbatdaran, Arman Raza, Syed I Wang, Sophie J Romano, Dominick J Goel, Akshay Teichman, Kurt Moghadam, Mina C Shih, George Blumenfeld, Jon D Shimonov, Daniil Chevalier, James M Prince, Martin R |
| description | Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates.
To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements.
Retrospective training, prospective testing.
Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility.
T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T.
2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded.
Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value |
| doi_str_mv | 10.1002/jmri.28593 |
| format | Article |
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To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements.
Retrospective training, prospective testing.
Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility.
T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T.
2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded.
Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value < 0.05 was considered statistically significant.
In 17 ADPKD subjects, model-assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model-assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model-assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier.
Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model-assisted segmentations and can improve TKV measurement reproducibility more than 2-fold in ADPKD.
2 TECHNICAL EFFICACY: Stage 1.</description><identifier>ISSN: 1053-1807</identifier><identifier>ISSN: 1522-2586</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.28593</identifier><identifier>PMID: 36645114</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Algorithms ; Artificial Intelligence ; Biomarkers ; Correlation coefficient ; Correlation coefficients ; Deep learning ; Field strength ; Humans ; Image processing ; Image segmentation ; Kidney - diagnostic imaging ; Kidney diseases ; Kidneys ; Machine learning ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Medical imaging ; Normality ; Outliers (statistics) ; Planes ; Polycystic kidney ; Polycystic Kidney, Autosomal Dominant - diagnostic imaging ; Prospective Studies ; Reproducibility ; Reproducibility of Results ; Retrospective Studies ; Statistical analysis ; Statistical tests ; Training</subject><ispartof>Journal of magnetic resonance imaging, 2023-10, Vol.58 (4), p.1153-1160</ispartof><rights>2023 International Society for Magnetic Resonance in Medicine.</rights><rights>2023 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c407t-46c73e0283849dd0e0282f00dd16adf6d4d18154110cba25cbe2ffb15a579de43</citedby><cites>FETCH-LOGICAL-c407t-46c73e0283849dd0e0282f00dd16adf6d4d18154110cba25cbe2ffb15a579de43</cites><orcidid>0000-0001-7722-9207 ; 0000-0002-1376-4099 ; 0000-0002-5341-8350 ; 0000-0002-0954-8836 ; 0000-0002-8691-406X ; 0000-0002-7097-0032 ; 0000-0002-5122-7067 ; 0000-0003-4833-9356 ; 0000-0002-9883-0584 ; 0000-0002-8356-2011</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36645114$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dev, Hreedi</creatorcontrib><creatorcontrib>Zhu, Chenglin</creatorcontrib><creatorcontrib>Sharbatdaran, Arman</creatorcontrib><creatorcontrib>Raza, Syed I</creatorcontrib><creatorcontrib>Wang, Sophie J</creatorcontrib><creatorcontrib>Romano, Dominick J</creatorcontrib><creatorcontrib>Goel, Akshay</creatorcontrib><creatorcontrib>Teichman, Kurt</creatorcontrib><creatorcontrib>Moghadam, Mina C</creatorcontrib><creatorcontrib>Shih, George</creatorcontrib><creatorcontrib>Blumenfeld, Jon D</creatorcontrib><creatorcontrib>Shimonov, Daniil</creatorcontrib><creatorcontrib>Chevalier, James M</creatorcontrib><creatorcontrib>Prince, Martin R</creatorcontrib><title>Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates.
To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements.
Retrospective training, prospective testing.
Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility.
T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T.
2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded.
Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value < 0.05 was considered statistically significant.
In 17 ADPKD subjects, model-assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model-assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model-assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier.
Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model-assisted segmentations and can improve TKV measurement reproducibility more than 2-fold in ADPKD.
2 TECHNICAL EFFICACY: Stage 1.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biomarkers</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Deep learning</subject><subject>Field strength</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Kidney - diagnostic imaging</subject><subject>Kidney diseases</subject><subject>Kidneys</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Medical imaging</subject><subject>Normality</subject><subject>Outliers (statistics)</subject><subject>Planes</subject><subject>Polycystic kidney</subject><subject>Polycystic Kidney, Autosomal Dominant - diagnostic imaging</subject><subject>Prospective Studies</subject><subject>Reproducibility</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Training</subject><issn>1053-1807</issn><issn>1522-2586</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkdtq3DAQhk1paNKkN32AIuhNKTiVZEm2r8qSM82SkB5uhSyNt1pkaSNZAT9HXzjenGh7NQPzzT-HvyjeE3xIMKZf1kO0h7ThbfWq2COc0pLyRryec8yrkjS43i3eprTGGLct42-K3UoIxglhe8Wfk74HPaLQo8UdRLWyfoWWoFKOMIAfEzqNYUDL7Ea7cYCWNxfoOrsE6DvcZvAaEgoefbPGw4R-BZcHQDewicFkbTvr7Dgh69EijyGFQTl0HAbrlR_RdXCTntJo9XP7sU3zZDgodno1j3j3FPeLn6cnP47Oy8urs4ujxWWpGa7HkgldV4BpUzWsNQZvU9pjbAwRyvTCMEMawhkhWHeKct0B7fuOcMXr1gCr9ouvj7qb3A1g9HxuVE5uoh1UnGRQVv5b8fa3XIU7SXDLatZWs8KnJ4UY5m-kUQ42aXBOeQg5SVoLgQUjD-jH_9B1yNHP90naCE7rlvMt9fmR0jGkFKF_2YZguTVbbs2WD2bP8Ie_939Bn92t7gFoWKh3</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Dev, Hreedi</creator><creator>Zhu, Chenglin</creator><creator>Sharbatdaran, Arman</creator><creator>Raza, Syed I</creator><creator>Wang, Sophie J</creator><creator>Romano, Dominick J</creator><creator>Goel, Akshay</creator><creator>Teichman, Kurt</creator><creator>Moghadam, Mina C</creator><creator>Shih, George</creator><creator>Blumenfeld, Jon D</creator><creator>Shimonov, Daniil</creator><creator>Chevalier, James M</creator><creator>Prince, Martin 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>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-7722-9207</orcidid><orcidid>https://orcid.org/0000-0002-1376-4099</orcidid><orcidid>https://orcid.org/0000-0002-5341-8350</orcidid><orcidid>https://orcid.org/0000-0002-0954-8836</orcidid><orcidid>https://orcid.org/0000-0002-8691-406X</orcidid><orcidid>https://orcid.org/0000-0002-7097-0032</orcidid><orcidid>https://orcid.org/0000-0002-5122-7067</orcidid><orcidid>https://orcid.org/0000-0003-4833-9356</orcidid><orcidid>https://orcid.org/0000-0002-9883-0584</orcidid><orcidid>https://orcid.org/0000-0002-8356-2011</orcidid></search><sort><creationdate>20231001</creationdate><title>Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease</title><author>Dev, Hreedi ; Zhu, Chenglin ; Sharbatdaran, Arman ; Raza, Syed I ; Wang, Sophie J ; Romano, Dominick J ; Goel, Akshay ; Teichman, Kurt ; Moghadam, Mina C ; Shih, George ; Blumenfeld, Jon D ; Shimonov, Daniil ; Chevalier, James M ; Prince, Martin R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-46c73e0283849dd0e0282f00dd16adf6d4d18154110cba25cbe2ffb15a579de43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biomarkers</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Deep learning</topic><topic>Field strength</topic><topic>Humans</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Kidney - diagnostic imaging</topic><topic>Kidney diseases</topic><topic>Kidneys</topic><topic>Machine learning</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Medical imaging</topic><topic>Normality</topic><topic>Outliers (statistics)</topic><topic>Planes</topic><topic>Polycystic kidney</topic><topic>Polycystic Kidney, Autosomal Dominant - diagnostic imaging</topic><topic>Prospective Studies</topic><topic>Reproducibility</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dev, Hreedi</creatorcontrib><creatorcontrib>Zhu, Chenglin</creatorcontrib><creatorcontrib>Sharbatdaran, Arman</creatorcontrib><creatorcontrib>Raza, Syed I</creatorcontrib><creatorcontrib>Wang, Sophie J</creatorcontrib><creatorcontrib>Romano, Dominick J</creatorcontrib><creatorcontrib>Goel, Akshay</creatorcontrib><creatorcontrib>Teichman, Kurt</creatorcontrib><creatorcontrib>Moghadam, Mina C</creatorcontrib><creatorcontrib>Shih, George</creatorcontrib><creatorcontrib>Blumenfeld, Jon D</creatorcontrib><creatorcontrib>Shimonov, Daniil</creatorcontrib><creatorcontrib>Chevalier, James M</creatorcontrib><creatorcontrib>Prince, Martin R</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</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><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dev, Hreedi</au><au>Zhu, Chenglin</au><au>Sharbatdaran, Arman</au><au>Raza, Syed I</au><au>Wang, Sophie J</au><au>Romano, Dominick J</au><au>Goel, Akshay</au><au>Teichman, Kurt</au><au>Moghadam, Mina C</au><au>Shih, George</au><au>Blumenfeld, Jon D</au><au>Shimonov, Daniil</au><au>Chevalier, James M</au><au>Prince, Martin R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2023-10-01</date><risdate>2023</risdate><volume>58</volume><issue>4</issue><spage>1153</spage><epage>1160</epage><pages>1153-1160</pages><issn>1053-1807</issn><issn>1522-2586</issn><eissn>1522-2586</eissn><abstract>Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates.
To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements.
Retrospective training, prospective testing.
Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility.
T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T.
2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded.
Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value < 0.05 was considered statistically significant.
In 17 ADPKD subjects, model-assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model-assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model-assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier.
Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model-assisted segmentations and can improve TKV measurement reproducibility more than 2-fold in ADPKD.
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| ispartof | Journal of magnetic resonance imaging, 2023-10, Vol.58 (4), p.1153-1160 |
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| subjects | Algorithms Artificial Intelligence Biomarkers Correlation coefficient Correlation coefficients Deep learning Field strength Humans Image processing Image segmentation Kidney - diagnostic imaging Kidney diseases Kidneys Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Normality Outliers (statistics) Planes Polycystic kidney Polycystic Kidney, Autosomal Dominant - diagnostic imaging Prospective Studies Reproducibility Reproducibility of Results Retrospective Studies Statistical analysis Statistical tests Training |
| title | Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease |
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