{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T16:38:14Z","timestamp":1788799094836,"version":"build-2803163510"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T00:00:00Z","timestamp":1566950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2020,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Body fat and\/or muscle composition influences prognosis in several cancer types. For advanced gastric and gastroesophageal junction cancer, we investigated which body composition parameters carry prognostic information beyond well\u2010established clinical parameters using robust model selection strategy such that parameters identified can be expected to generalize and to be reproducible beyond our particular data set. Then we modelled how differences in these parameters translate into survival outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Fat and muscle parameters were measured on baseline computed tomography scans in 761 patients with advanced gastric or gastroesophageal junction cancer from the phase III EXPAND trial, undergoing first\u2010line chemotherapy. Cox regression analysis for overall survival (OS) and progression\u2010free survival (PFS) included body composition parameters and clinical prognostic factors. All continuous variables were entered linearly into the model as there was no evidence of non\u2010linear prognostic impact. For transferability, the final model included only parameters that were picked by Bayesian information criterion model selection followed by bootstrap analysis to identify the most robust model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Muscle and fat parameters formed correlation clusters without relevant between\u2010cluster correlation. Mean muscle attenuation (MA) clusters with the fat parameters. In multivariate analysis, MA was prognostic for OS (<jats:italic>P<\/jats:italic> &lt; 0.0001) but not for PFS, while skeletal muscle index was prognostic for PFS (<jats:italic>P<\/jats:italic> = 0.02) but not for OS. Worse performance status Eastern Cooperative Oncology Group (ECOG 1\/0), younger age (on a linear scale), and the number of metastatic sites were strong negative clinical prognostic factors for both OS and PFS. MA remained in the model for OS (<jats:italic>P<\/jats:italic> = 0.0001) following Bayesian information criterion model selection in contrast to skeletal muscle index that remained prognostic for PFS (<jats:italic>P<\/jats:italic> = 0.009). Applying stricter criteria for transferability, MA represented the only prognostic body composition parameter for OS, selected in &gt;80% of bootstrap replicates. Finally, Cox model\u2010derived survival curves indicated that large differences in MA translate into only moderate differences in expected OS in this cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Among body composition parameters, only MA has robust prognostic impact for OS. Data suggest that treatment approaches targeting muscle quality are unlikely to prolong OS noticeably on their own in advanced gastric cancer patients, indicating that multimodal approaches should be pursued in the future.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcsm.12484","type":"journal-article","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T03:53:58Z","timestamp":1567050838000},"page":"135-144","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Prognostic role of body composition parameters in gastric\/gastroesophageal junction cancer patients from the EXPAND trial"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3253-8206","authenticated-orcid":false,"given":"Ulrich T.","family":"Hacker","sequence":"first","affiliation":[{"name":"1st Medical Department University Cancer Center Leipzig (UCCL), University Leipzig Medical Center  Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Hasenclever","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Epidemiology (IMISE) Medical Faculty of the University Leipzig  Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Linder","sequence":"additional","affiliation":[{"name":"Department of Radiology University Leipzig Medical Center  Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gertraud","family":"Stocker","sequence":"additional","affiliation":[{"name":"1st Medical Department University Cancer Center Leipzig (UCCL), University Leipzig Medical Center  Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun\u2010Cheol","family":"Chung","sequence":"additional","affiliation":[{"name":"Yonsei Cancer Center Yonsei University College of Medicine  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoon\u2010Koo","family":"Kang","sequence":"additional","affiliation":[{"name":"Division Oncology Department Medical Center  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Moehler","sequence":"additional","affiliation":[{"name":"First Department of Internal Medicine University Medical Center of the Johannes Gutenberg University Mainz  Mainz Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harald","family":"Busse","sequence":"additional","affiliation":[{"name":"Department of Radiology University Leipzig Medical Center  Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Lordick","sequence":"additional","affiliation":[{"name":"1st Medical Department University Cancer Center Leipzig (UCCL), University Leipzig Medical Center  Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,8,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcsm.12379"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1152\/japplphysiol.00744.2004"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdx271"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2015.02.031"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-015-4432-4"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2010-0227"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/iju.12716"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(08)70153-0"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2012.45.2722"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2018.0137"},{"key":"e_1_2_8_12_1","unstructured":"WilliamsBA MandarekarJN MandrekarSJ MandrekarSJ ChaSS FurthAF.Finding optimal cutpoints for continuous covariates with binary and time\u2010to\u2010event outcomes. 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