Evidence map›Paper›PMID 32314543›Full record

ArticleJournal of cachexia, sarcopenia and muscle2020

Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients.

Elizabeth M Cespedes Feliciano, Karteek Popuri, Dana Cobzas, Vickie E Baracos, Mirza Faisal Beg, Arafat Dad Khan, Cydney Ma, Vincent Chow, Carla M Prado, Jingjie Xiao and 5 more

Open access · goldAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 3 pooled it
9.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

79 citing papers in PubMed, 3 syntheses or guidelines pooled it, 136 citations in OpenAlex.

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  16. Poor muscle quality: A hidden and detrimental health condition in obesity.Reviews in endocrine & metabolic disorders · 2025
    Review
  17. Article
  18. Review
  19. Sarcopenia and Skeletal Muscle Loss after CAR T-cell Therapy in Diffuse Large B-cell Lymphoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025
    Article
  20. Article

19 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 6 institutions in 2 countries.

Elizabeth M Cespedes FelicianoDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.ORCID 0000-0003-1192-4017
Karteek PopuriSchool of Engineering, Simon Fraser University, Vancouver, Canada.
Dana CobzasDepartment of Computing Science, MacEwan University, Edmonton, Canada.
Vickie E BaracosCross Cancer Institute, University of Alberta, Edmonton, Canada.
Mirza Faisal BegSchool of Engineering, Simon Fraser University, Vancouver, Canada.
Arafat Dad KhanSchool of Engineering, Simon Fraser University, Vancouver, Canada.
Cydney MaSchool of Engineering, Simon Fraser University, Vancouver, Canada.
Vincent ChowSchool of Engineering, Simon Fraser University, Vancouver, Canada.
Carla M PradoDepartment of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada.
Jingjie XiaoCovenant Health Palliative Institute, Edmonton, Canada.
Vincent LiuDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Wendy Y ChenDepartment of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.
Jeffrey MeyerhardtDepartment of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.
Kathleen B AlbersDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Bette J CaanDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Simon Fraser University · CAKaiser Permanente · USHarvard University · USUniversity of Alberta · CACovenant Health · CAMacEwan University · CA

Funding

Identifying pre-sepsis opportunities for early, targeted intervention - Equipment SupplementR35GM128672 · NIGMS · KAISER FOUNDATION RESEARCH INSTITUTE · PI Vincent Liu · 2018 to 2026
$4.3M
Body Composition, Weight, and Colon Cancer SurvivalR01CA175011 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI CAAN, BETTE J · 2013 to 2017
$3.1M
(PQA2) Exploring the role of sarcopenia in obesity and breast cancer survivalR01CA184953 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI CAAN, BETTE J · 2014 to 2017
$2.5M
Beyond Body Mass Index: Bringing Body Composition into Oncology PracticeK01CA226155 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI FELICIANO, ELIZABETH MARJORIE CESPEDES · 2018 to 2022
$894k
NCI NIH HHS K01 CA226155NCI NIH HHS R01 CA175011NCI NIH HHS R01CA175011NCI NIH HHS R01 CA184953NCI NIH HHS R01CA184953NIGMS NIH HHS R35 GM128672
6 · The paper itself

Abstract

backgroundBody composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset.

methodsAmong patients with non-metastatic colorectal (n = 3102) and breast (n = 2888) 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-level image overlap using Jaccard scores and agreement between methods using intra-class 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.

resultsMean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 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-class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1-2% versus manual analysis: mean differences were small at -2.35, -1.97 and -2.38 cm

conclusionsIn 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-metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.

Indexed as

Body CompositionBreast NeoplasmsColorectal NeoplasmsAdipose TissueAgedAutomationFemaleHumansMaleMiddle AgedSubcutaneous FatTomography, X-Ray ComputedAdiposityAutomationBody compositionCancerMuscleObesitySarcopeniaSoftware

Identifiers

PMID32314543
PMCPMC7567141
OpenAlexW3016734965

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.