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Most studies manually segment CT scans, but <jats:styled-content>A<\/jats:styled-content>utomatic <jats:styled-content>B<\/jats:styled-content>ody composition <jats:styled-content>A<\/jats:styled-content>nalyser using <jats:styled-content>C<\/jats:styled-content>omputed tomography image <jats:styled-content>S<\/jats:styled-content>egmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Among patients with non\u2010metastatic colorectal (<jats:italic>n<\/jats:italic>\u00a0=\u00a03102) and breast (<jats:italic>n<\/jats:italic>\u00a0=\u00a02888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel\u2010level image overlap using Jaccard scores and agreement between methods using intra\u2010class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Mean\u00a0\u00b1\u00a0SD age was 63\u00a0\u00b1\u00a011\u00a0years for colorectal cancer patients and 56\u00a0\u00b1\u00a012 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra\u2010class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1\u20132% versus manual analysis: mean differences were small at \u22122.35, \u22121.97 and \u22122.38\u00a0cm<jats:sup>2<\/jats:sup>, respectively. ABACS's performance was lowest for the &lt;2% of patients who were underweight or had anatomic abnormalities. ABACS and manual analysis produced similar associations with mortality; comparing the lowest to highest tertile of skeletal muscle from ABACS versus manual analysis, the HRs were 1.23 (95% CI: 1.00\u20131.52) versus 1.38 (95% CI: 1.11\u20131.70) for colorectal cancer patients and 1.30 (95% CI: 1.01\u20131.66) versus 1.29 (95% CI: 1.00\u20131.65) for breast cancer patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non\u2010metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcsm.12573","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:20:14Z","timestamp":1587442814000},"page":"1258-1269","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":131,"title":["Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1192-4017","authenticated-orcid":false,"given":"Elizabeth M.","family":"Cespedes Feliciano","sequence":"first","affiliation":[{"name":"Division of Research Kaiser Permanente Northern California  Oakland CA 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