Evidence map›Paper›PMID 34490899›Full record

ArticleJournal of surgical oncology2021

Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

Jaehyuk Kim, Seung Hee Han, Hyoung-Il Kim

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 4 pooled it
2.8field-weighted citation impact, top 9% 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

20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 32 citations in OpenAlex.

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  7. Simple clinical parameters to identify sarcopenia 1 year after gastrectomy for gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
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  20. Understanding the development of sarcopenic obesity.Expert review of endocrinology & metabolism
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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

3 authors at 1 institution in 2 countries.

Jaehyuk KimMolecular Imaging, Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0001-9391-4749
Seung Hee HanMolecular Imaging, Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0003-4900-3360
Hyoung-Il KimMolecular Imaging, Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0002-6134-4523
University Health Network · CA

Funding

National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) 2016R1A2B4014984National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) 2019R1H1A2079953
6 · The paper itself

Abstract

backgroundPrevious studies evaluating the prognostic value of computed tomography (CT)-derived body composition data have included few patients. Thus, we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of gastric cancer patients using preoperative CT, as nutritional status is a predictor of long-term survival after gastric cancer surgery.

methodsPreoperative CT images were analyzed for 840 gastric cancer patients who underwent gastrectomy between March 2009 and June 2018. Machine learning algorithms were used to automatically detect the third lumbar (L3) vertebral level and segment the body composition. Visceral fat area and skeletal muscle index at L3 were determined and used to classify patients into obesity, sarcopenia, or sarcopenic obesity groups.

resultsOut of 840 patients (mean age = 60.4 years; 526 [62.6%] men), 534 (63.5%) had visceral obesity, 119 (14.2%) had sarcopenia, and 48 (5.7%) patients had sarcopenic obesity. Patients with sarcopenic obesity had a poorer prognosis than those without sarcopenia (hazard ratio [HR] = 3.325; 95% confidence interval [CI] = 1.698-6.508). Multivariate analysis identified sarcopenic obesity as an independent risk factor for increased mortality (HR = 2.608; 95% CI = 1.313-5.179). Other risk factors were greater extent of gastrectomy (HR = 1.928; 95% CI = 1.260-2.950), lower prognostic nutritional index (HR = 0.934; 95% CI = 0.901-0.969), higher neutrophil count (HR = 1.101; 95% CI = 1.031-1.176), lymph node metastasis (HR = 6.291; 95% CI = 3.498-11.314), and R1/2 resection (HR = 4.817; 95% CI = 1.518-9.179).

conclusionBody composition analysis automated by machine learning predicted long-term survival in patients with gastric cancer.

Indexed as

Machine LearningBody CompositionBody Mass IndexCase-Control StudiesCross-Sectional StudiesFemaleFollow-Up StudiesGastrectomyHumansMaleMiddle AgedMuscle, SkeletalObesityPrognosisRisk FactorsSarcopeniabody mass indexgastric cancermachine learningnutrition processsarcopenic obesitysurvival

Identifiers

PMID34490899
PMCPMC9290491
OpenAlexW3197832936

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.