{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T07:16:10Z","timestamp":1790752570071,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Skeletal muscle metrics on computed tomography (CT) correlate with clinical and patient\u2010reported outcomes. We hypothesize that aggregating skeletal muscle measurements from multiple vertebral levels and skeletal muscle gauge (SMG) better predict outcomes than skeletal muscle radioattenuation (SMRA) or \u2010index (SMI) at a single vertebral level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a secondary analysis of prospectively collected clinical (overall survival, hospital readmission, time to unplanned hospital readmission or death, and readmission or death within 90\u00a0days) and patient\u2010reported outcomes (physical and psychological symptom burden captured as Edmonton Symptom Assessment Scale and Patient Health Questionnaire) of patients with advanced cancer who experienced an unplanned admission to Massachusetts General Hospital from 2014 to 2016. First, we assessed the correlation of skeletal muscle cross\u2010sectional area, SMRA, SMI, and SMG at one or more of the following thoracic (T) or lumbar (L) vertebral levels: T5, T8, T10, and L3 on CT scans obtained \u226450\u00a0days before index assessment. Second, we aggregated measurements across all available vertebral levels using percentile\u2010based averaging (PBA) to create the average percentile. Third, we constructed one regression model adjusted for age, sex, sociodemographic factors, cancer type, body mass index, and intravenous contrast for each combination of (i) vertebral level and average percentile, (ii) muscle metrics (SMRA, SMI, &amp; SMG), and (iii) clinical and patient\u2010reported outcomes. Fourth, we compared the performance of vertebral levels and muscle metrics by ranking otherwise identical models by concordance statistic, number of included patients, coefficient of determination, and significance of muscle metric.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We included 846 patients (mean age: 63.5\u00a0\u00b1\u00a012.9\u00a0years, 50.5% males) with advanced cancer [predominantly gastrointestinal (32.9%) or lung (18.9%)]. The correlation of muscle measurements between vertebral levels ranged from 0.71 to 0.84 for SMRA and 0.67 to 0.81 for SMI. The correlation of individual levels with the average percentile was 0.90\u20130.93 for SMRA and 0.86\u20130.92 for SMI. The intrapatient correlation of SMRA with SMI was 0.21\u20130.40. PBA allowed for inclusion of 8\u201347% more patients than any single\u2010level analysis. PBA outperformed single\u2010level analyses across all comparisons with average ranks 2.6, 2.9, and 1.6 for concordance statistic, coefficient of determination, and significance (range 1\u20135,\n                      <jats:italic>\u03bc<\/jats:italic>\n                      \u00a0=\u00a03), respectively. On average, SMG outperformed SMRA and SMI across outcomes and vertebral levels: the average rank of SMG was 1.4, 1.4, and 1.4 for concordance statistic, coefficient of determination, and significance (range 1\u20133,\n                      <jats:italic>\u03bc<\/jats:italic>\n                      \u00a0=\u00a02), respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Multivertebral level skeletal muscle analyses using PBA and SMG independently and additively outperform analyses using individual levels and SMRA or SMI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.12848","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T01:30:33Z","timestamp":1635903033000},"page":"190-202","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Percentile\u2010based averaging and skeletal muscle gauge improve body composition analysis: validation at multiple vertebral levels"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5596-1357","authenticated-orcid":false,"given":"J. Peter","family":"Marquardt","sequence":"first","affiliation":[{"name":"Department of Radiology RWTH Aachen University  Aachen Germany"},{"name":"Department of Radiology, Division of Thoracic Imaging and Intervention Massachusetts General Hospital  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric J.","family":"Roeland","sequence":"additional","affiliation":[{"name":"Knight Cancer Institute Oregon Health and Sciences University  Portland OR USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily E.","family":"Van Seventer","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Hematology and Oncology Massachusetts General Hospital Cancer Center and Harvard Medical School  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Till D.","family":"Best","sequence":"additional","affiliation":[{"name":"Department of Radiology, Division of Thoracic Imaging and Intervention Massachusetts General Hospital  Boston MA USA"},{"name":"Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health, Institute of Radiology  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nora K.","family":"Horick","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Hematology and Oncology Massachusetts General Hospital Cancer Center and Harvard Medical School  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan D.","family":"Nipp","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Hematology and Oncology Massachusetts General Hospital Cancer Center and Harvard Medical School  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0119-3903","authenticated-orcid":false,"given":"Florian J.","family":"Fintelmann","sequence":"additional","affiliation":[{"name":"Department of Radiology, Division of Thoracic Imaging and Intervention Massachusetts General Hospital  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2012.45.2722"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1139\/H08-075"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/RTI.0000000000000428"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2013.11.025"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/ageing\/afy169"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30025-X"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-29825-5"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20180451"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1152\/japplphysiol.00744.2004"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-019-01912-9"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12603\u2010017\u20100983\u20103"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2018.0479"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-68797-3"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.16866"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/tbj.12952"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-16-0940"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcsm.12604"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000004040"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06630-w"},{"key":"e_1_2_7_21_1","unstructured":"BridgeCP RosenthalM WrightB KotechaG FintelmannF&TroschelFFully\u2010automated analysis of body composition from CT in cancer patients using convolutional neural networks. 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