Evaluation of skeletal muscle mass using prediction formulas at the level of the 12th thoracic vertebra
•Two different formulas have been reported to predict L3 cross-sectional area (CSA) from Th12 CSA.•We evaluated the prediction accuracy of the two formulas.•With both formulas, the predicted L3 CSA correlated with the measured L3 CSA. People with cancer have a high risk of cachexia and sarcopenia, w...
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Veröffentlicht in: | Nutrition (Burbank, Los Angeles County, Calif.) Los Angeles County, Calif.), 2022-01, Vol.93, p.111475-111475, Article 111475 |
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creator | Matsuyama, Remi Maeda, Keisuke Yamanaka, Yosuke Ishida, Yuria Nonogaki, Tomoyuki Kato, Ryoko Shimizu, Akio Ueshima, Junko Kazaoka, Yoshiaki Hayashi, Tomio Ito, Kunihiro Furuhashi, Akifumi Ono, Takayuki Mori, Naoharu |
description | •Two different formulas have been reported to predict L3 cross-sectional area (CSA) from Th12 CSA.•We evaluated the prediction accuracy of the two formulas.•With both formulas, the predicted L3 CSA correlated with the measured L3 CSA.
People with cancer have a high risk of cachexia and sarcopenia, which are associated with worse clinical outcomes. We evaluated the prediction accuracy of the Matsuyama et al. and Ishida et al. formulas using computed tomography (CT) slices from the twelfth thoracic vertebra (Th12) level in people with cancer.
This retrospective study included patients with advanced cancer who underwent thoracic and abdominal CT scans (n = 173). The cross-sectional area (CSA) on CT images was measured at the levels of Th12 and the third lumbar vertebra (L3). The Matsuyama et al. formula used the Th12 CSA, whereas the Ishida et al. formula used only the Th12 CSA of the spinal erectors; thus, the measurements were performed separately. The correlation between predicted and actual L3 CSA was assessed using r and the intraclass correlation coefficient. A prediction-accuracy analysis of the predicted values was also performed.
The mean participant age was 66.2 ± 12.8 y; 50.3% of participants were women and 49.7% were men. Strong correlations were observed between the predicted and measured L3 values calculated from the two prediction formulas. The prediction-accuracy analysis using previously reported cutoff values showed that the Ishida et al. method had high sensitivity and the Matsuyama et al. method had high specificity for low skeletal muscle index determined by the predicted and measured L3 skeletal muscle index.
Both the Matsuyama et al. and Ishida et al. formulas had good reliability on CT slices at the Th12 level in people with advanced cancer, indicating that these formulas can be applied in clinical practice. |
doi_str_mv | 10.1016/j.nut.2021.111475 |
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People with cancer have a high risk of cachexia and sarcopenia, which are associated with worse clinical outcomes. We evaluated the prediction accuracy of the Matsuyama et al. and Ishida et al. formulas using computed tomography (CT) slices from the twelfth thoracic vertebra (Th12) level in people with cancer.
This retrospective study included patients with advanced cancer who underwent thoracic and abdominal CT scans (n = 173). The cross-sectional area (CSA) on CT images was measured at the levels of Th12 and the third lumbar vertebra (L3). The Matsuyama et al. formula used the Th12 CSA, whereas the Ishida et al. formula used only the Th12 CSA of the spinal erectors; thus, the measurements were performed separately. The correlation between predicted and actual L3 CSA was assessed using r and the intraclass correlation coefficient. A prediction-accuracy analysis of the predicted values was also performed.
The mean participant age was 66.2 ± 12.8 y; 50.3% of participants were women and 49.7% were men. Strong correlations were observed between the predicted and measured L3 values calculated from the two prediction formulas. The prediction-accuracy analysis using previously reported cutoff values showed that the Ishida et al. method had high sensitivity and the Matsuyama et al. method had high specificity for low skeletal muscle index determined by the predicted and measured L3 skeletal muscle index.
Both the Matsuyama et al. and Ishida et al. formulas had good reliability on CT slices at the Th12 level in people with advanced cancer, indicating that these formulas can be applied in clinical practice.</description><identifier>ISSN: 0899-9007</identifier><identifier>EISSN: 1873-1244</identifier><identifier>DOI: 10.1016/j.nut.2021.111475</identifier><identifier>PMID: 34638102</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Accuracy ; Age ; Cachexia ; Cancer ; Computed tomography ; Correlation coefficient ; Correlation coefficients ; Evaluation ; Female ; Females ; Humans ; Lumbar Vertebrae - diagnostic imaging ; Lung cancer ; Magnetic resonance imaging ; Male ; Males ; Medical imaging ; Muscle, Skeletal - diagnostic imaging ; Muscle, Skeletal - pathology ; Muscles ; Musculoskeletal system ; Palliative care ; Predictions ; Reproducibility of Results ; Retrospective Studies ; Sarcopenia ; Sarcopenia - diagnostic imaging ; Sarcopenia - pathology ; Skeletal muscle ; Th12 ; Thorax ; Vertebrae</subject><ispartof>Nutrition (Burbank, Los Angeles County, Calif.), 2022-01, Vol.93, p.111475-111475, Article 111475</ispartof><rights>2021 Elsevier Inc.</rights><rights>Copyright © 2021 Elsevier Inc. All rights reserved.</rights><rights>2021. Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-2aa672044da59361ecdb02b73dea13b2697fcab7ef9785d9667f6072858c689e3</citedby><cites>FETCH-LOGICAL-c447t-2aa672044da59361ecdb02b73dea13b2697fcab7ef9785d9667f6072858c689e3</cites><orcidid>0000-0001-9164-1855 ; 0000-0001-7132-7818 ; 0000-0002-0357-8604</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2605238389?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3548,27922,27923,45993,64383,64385,64387,72239</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34638102$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Matsuyama, Remi</creatorcontrib><creatorcontrib>Maeda, Keisuke</creatorcontrib><creatorcontrib>Yamanaka, Yosuke</creatorcontrib><creatorcontrib>Ishida, Yuria</creatorcontrib><creatorcontrib>Nonogaki, Tomoyuki</creatorcontrib><creatorcontrib>Kato, Ryoko</creatorcontrib><creatorcontrib>Shimizu, Akio</creatorcontrib><creatorcontrib>Ueshima, Junko</creatorcontrib><creatorcontrib>Kazaoka, Yoshiaki</creatorcontrib><creatorcontrib>Hayashi, Tomio</creatorcontrib><creatorcontrib>Ito, Kunihiro</creatorcontrib><creatorcontrib>Furuhashi, Akifumi</creatorcontrib><creatorcontrib>Ono, Takayuki</creatorcontrib><creatorcontrib>Mori, Naoharu</creatorcontrib><title>Evaluation of skeletal muscle mass using prediction formulas at the level of the 12th thoracic vertebra</title><title>Nutrition (Burbank, Los Angeles County, Calif.)</title><addtitle>Nutrition</addtitle><description>•Two different formulas have been reported to predict L3 cross-sectional area (CSA) from Th12 CSA.•We evaluated the prediction accuracy of the two formulas.•With both formulas, the predicted L3 CSA correlated with the measured L3 CSA.
People with cancer have a high risk of cachexia and sarcopenia, which are associated with worse clinical outcomes. We evaluated the prediction accuracy of the Matsuyama et al. and Ishida et al. formulas using computed tomography (CT) slices from the twelfth thoracic vertebra (Th12) level in people with cancer.
This retrospective study included patients with advanced cancer who underwent thoracic and abdominal CT scans (n = 173). The cross-sectional area (CSA) on CT images was measured at the levels of Th12 and the third lumbar vertebra (L3). The Matsuyama et al. formula used the Th12 CSA, whereas the Ishida et al. formula used only the Th12 CSA of the spinal erectors; thus, the measurements were performed separately. The correlation between predicted and actual L3 CSA was assessed using r and the intraclass correlation coefficient. A prediction-accuracy analysis of the predicted values was also performed.
The mean participant age was 66.2 ± 12.8 y; 50.3% of participants were women and 49.7% were men. Strong correlations were observed between the predicted and measured L3 values calculated from the two prediction formulas. The prediction-accuracy analysis using previously reported cutoff values showed that the Ishida et al. method had high sensitivity and the Matsuyama et al. method had high specificity for low skeletal muscle index determined by the predicted and measured L3 skeletal muscle index.
Both the Matsuyama et al. and Ishida et al. formulas had good reliability on CT slices at the Th12 level in people with advanced cancer, indicating that these formulas can be applied in clinical practice.</description><subject>Accuracy</subject><subject>Age</subject><subject>Cachexia</subject><subject>Cancer</subject><subject>Computed tomography</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Evaluation</subject><subject>Female</subject><subject>Females</subject><subject>Humans</subject><subject>Lumbar Vertebrae - diagnostic imaging</subject><subject>Lung cancer</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Males</subject><subject>Medical imaging</subject><subject>Muscle, Skeletal - diagnostic imaging</subject><subject>Muscle, Skeletal - pathology</subject><subject>Muscles</subject><subject>Musculoskeletal system</subject><subject>Palliative care</subject><subject>Predictions</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Sarcopenia</subject><subject>Sarcopenia - diagnostic imaging</subject><subject>Sarcopenia - pathology</subject><subject>Skeletal muscle</subject><subject>Th12</subject><subject>Thorax</subject><subject>Vertebrae</subject><issn>0899-9007</issn><issn>1873-1244</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kUtvFDEQhK0IRJbAD8gFWeKSyyx-zPghTihKCFIkLnC2PJ6exItnvPixEv8eLxs4cMipu6WvSq0qhC4p2VJCxYfddq1lywijW0ppL4cztKFK8o6yvn-BNkRp3WlC5Dl6nfOOEEK10K_QOe8FV5SwDXq4OdhQbfFxxXHG-QcEKDbgpWYXAC82Z1yzXx_wPsHk3R9wjmmpwWZsCy6PgAMcIBzlx4Oy8tiWmKzzDh8gFRiTfYNezjZkePs0L9D325tv13fd_dfPX64_3Xeu72XpmLVCMtL3kx00FxTcNBI2Sj6BpXxkQsvZ2VHCrKUaJi2EnAWRTA3KCaWBX6Crk-8-xZ8VcjGLzw5CsCvEmg0bFJVaazY09P1_6C7WtLbvDBNkYFxxpRtFT5RLMecEs9knv9j0y1Biji2YnWktmGML5tRC07x7cq7jAtM_xd_YG_DxBECL4uAhmew8rK4lnMAVM0X_jP1vomeXfQ</recordid><startdate>202201</startdate><enddate>202201</enddate><creator>Matsuyama, Remi</creator><creator>Maeda, Keisuke</creator><creator>Yamanaka, Yosuke</creator><creator>Ishida, Yuria</creator><creator>Nonogaki, Tomoyuki</creator><creator>Kato, Ryoko</creator><creator>Shimizu, Akio</creator><creator>Ueshima, Junko</creator><creator>Kazaoka, Yoshiaki</creator><creator>Hayashi, Tomio</creator><creator>Ito, Kunihiro</creator><creator>Furuhashi, Akifumi</creator><creator>Ono, Takayuki</creator><creator>Mori, Naoharu</creator><general>Elsevier Inc</general><general>Elsevier Limited</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>7RQ</scope><scope>7RV</scope><scope>7TS</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AN0</scope><scope>ASE</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FPQ</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K6X</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9164-1855</orcidid><orcidid>https://orcid.org/0000-0001-7132-7818</orcidid><orcidid>https://orcid.org/0000-0002-0357-8604</orcidid></search><sort><creationdate>202201</creationdate><title>Evaluation of skeletal muscle mass using prediction formulas at the level of the 12th thoracic vertebra</title><author>Matsuyama, Remi ; Maeda, Keisuke ; Yamanaka, Yosuke ; Ishida, Yuria ; Nonogaki, Tomoyuki ; Kato, Ryoko ; Shimizu, Akio ; Ueshima, Junko ; Kazaoka, Yoshiaki ; Hayashi, Tomio ; Ito, Kunihiro ; Furuhashi, Akifumi ; Ono, Takayuki ; Mori, Naoharu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-2aa672044da59361ecdb02b73dea13b2697fcab7ef9785d9667f6072858c689e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Age</topic><topic>Cachexia</topic><topic>Cancer</topic><topic>Computed tomography</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Evaluation</topic><topic>Female</topic><topic>Females</topic><topic>Humans</topic><topic>Lumbar Vertebrae - diagnostic imaging</topic><topic>Lung cancer</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Males</topic><topic>Medical imaging</topic><topic>Muscle, Skeletal - diagnostic imaging</topic><topic>Muscle, Skeletal - pathology</topic><topic>Muscles</topic><topic>Musculoskeletal system</topic><topic>Palliative care</topic><topic>Predictions</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Sarcopenia</topic><topic>Sarcopenia - diagnostic imaging</topic><topic>Sarcopenia - pathology</topic><topic>Skeletal muscle</topic><topic>Th12</topic><topic>Thorax</topic><topic>Vertebrae</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Matsuyama, Remi</creatorcontrib><creatorcontrib>Maeda, Keisuke</creatorcontrib><creatorcontrib>Yamanaka, Yosuke</creatorcontrib><creatorcontrib>Ishida, Yuria</creatorcontrib><creatorcontrib>Nonogaki, Tomoyuki</creatorcontrib><creatorcontrib>Kato, Ryoko</creatorcontrib><creatorcontrib>Shimizu, Akio</creatorcontrib><creatorcontrib>Ueshima, Junko</creatorcontrib><creatorcontrib>Kazaoka, Yoshiaki</creatorcontrib><creatorcontrib>Hayashi, Tomio</creatorcontrib><creatorcontrib>Ito, Kunihiro</creatorcontrib><creatorcontrib>Furuhashi, Akifumi</creatorcontrib><creatorcontrib>Ono, Takayuki</creatorcontrib><creatorcontrib>Mori, Naoharu</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>Career & Technical Education Database</collection><collection>Nursing & Allied Health Database</collection><collection>Physical Education Index</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech 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>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>British Nursing Database</collection><collection>British Nursing Index</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>British Nursing Index (BNI) (1985 to Present)</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>British Nursing Index</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>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</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 Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Nutrition (Burbank, Los Angeles County, Calif.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Matsuyama, Remi</au><au>Maeda, Keisuke</au><au>Yamanaka, Yosuke</au><au>Ishida, Yuria</au><au>Nonogaki, Tomoyuki</au><au>Kato, Ryoko</au><au>Shimizu, Akio</au><au>Ueshima, Junko</au><au>Kazaoka, Yoshiaki</au><au>Hayashi, Tomio</au><au>Ito, Kunihiro</au><au>Furuhashi, Akifumi</au><au>Ono, Takayuki</au><au>Mori, Naoharu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of skeletal muscle mass using prediction formulas at the level of the 12th thoracic vertebra</atitle><jtitle>Nutrition (Burbank, Los Angeles County, Calif.)</jtitle><addtitle>Nutrition</addtitle><date>2022-01</date><risdate>2022</risdate><volume>93</volume><spage>111475</spage><epage>111475</epage><pages>111475-111475</pages><artnum>111475</artnum><issn>0899-9007</issn><eissn>1873-1244</eissn><abstract>•Two different formulas have been reported to predict L3 cross-sectional area (CSA) from Th12 CSA.•We evaluated the prediction accuracy of the two formulas.•With both formulas, the predicted L3 CSA correlated with the measured L3 CSA.
People with cancer have a high risk of cachexia and sarcopenia, which are associated with worse clinical outcomes. We evaluated the prediction accuracy of the Matsuyama et al. and Ishida et al. formulas using computed tomography (CT) slices from the twelfth thoracic vertebra (Th12) level in people with cancer.
This retrospective study included patients with advanced cancer who underwent thoracic and abdominal CT scans (n = 173). The cross-sectional area (CSA) on CT images was measured at the levels of Th12 and the third lumbar vertebra (L3). The Matsuyama et al. formula used the Th12 CSA, whereas the Ishida et al. formula used only the Th12 CSA of the spinal erectors; thus, the measurements were performed separately. The correlation between predicted and actual L3 CSA was assessed using r and the intraclass correlation coefficient. A prediction-accuracy analysis of the predicted values was also performed.
The mean participant age was 66.2 ± 12.8 y; 50.3% of participants were women and 49.7% were men. Strong correlations were observed between the predicted and measured L3 values calculated from the two prediction formulas. The prediction-accuracy analysis using previously reported cutoff values showed that the Ishida et al. method had high sensitivity and the Matsuyama et al. method had high specificity for low skeletal muscle index determined by the predicted and measured L3 skeletal muscle index.
Both the Matsuyama et al. and Ishida et al. formulas had good reliability on CT slices at the Th12 level in people with advanced cancer, indicating that these formulas can be applied in clinical practice.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>34638102</pmid><doi>10.1016/j.nut.2021.111475</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-9164-1855</orcidid><orcidid>https://orcid.org/0000-0001-7132-7818</orcidid><orcidid>https://orcid.org/0000-0002-0357-8604</orcidid></addata></record> |
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subjects | Accuracy Age Cachexia Cancer Computed tomography Correlation coefficient Correlation coefficients Evaluation Female Females Humans Lumbar Vertebrae - diagnostic imaging Lung cancer Magnetic resonance imaging Male Males Medical imaging Muscle, Skeletal - diagnostic imaging Muscle, Skeletal - pathology Muscles Musculoskeletal system Palliative care Predictions Reproducibility of Results Retrospective Studies Sarcopenia Sarcopenia - diagnostic imaging Sarcopenia - pathology Skeletal muscle Th12 Thorax Vertebrae |
title | Evaluation of skeletal muscle mass using prediction formulas at the level of the 12th thoracic vertebra |
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