Evidence map›Paper›PMID 39738489›Full record

ArticleScientific reports2024

Automated chest CT three-dimensional quantification of body composition: adipose tissue and paravertebral muscle.

Akinori Hata, Yohei Muraguchi, Minoru Nakatsugawa, Xinan Wang, Jiyeon Song, Noriaki Wada, Takuya Hino, Kota Aoyagi, Masami Kawagishi, Takuo Negishi and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
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

21 authors.

Akinori HataDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. a-hata@radiol.med.osaka-u.ac.jp.
Yohei MuraguchiCanon Medical Systems Corporation, Tochigi, Japan.
Minoru NakatsugawaCanon Medical Systems Corporation, Tochigi, Japan.
Xinan WangDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
Jiyeon SongDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
Noriaki WadaDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Takuya HinoDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kota AoyagiCanon Medical Systems Corporation, Tochigi, Japan.
Masami KawagishiR&D Headquarters, Canon Inc., Tokyo, Japan.
Takuo NegishiCanon Medical Systems Corporation, Tochigi, Japan.
Vladimir I ValtchinovDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Mizuki NishinoDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Akihiro KogaCanon Medical Systems Corporation, Tochigi, Japan.
Naoki SugiharaCanon Medical Systems Corporation, Tochigi, Japan.
Masahiro OzakiCanon Medical Systems Corporation, Tochigi, Japan.
Gary M HunninghakePulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Noriyuki TomiyamaDiagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Mark L SchieblerDepartment of Radiology, UW Madison School of Medicine and Public Health, Madison, WI, USA.
Yi LiDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
David C ChristianiDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
Hiroto HatabuDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Clinical Genetics and Screening for Pulmonary FibrosisR01HL130974 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI GARY MATTHEW HUNNINGHAKE, Benjamin Alexander Raby · 2016 to 2026
$10.0M
Genetic and Genomic Characterization of the Occurrence and Progression of Interstitial Lung AbnormalitiesR01HL135142 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO, GARY MATTHEW HUNNINGHAKE · 2017 to 2026
$7.7M
Interstitial Lung Abnormalities: Defining the Phenotype, Causes, and Consequences.R01HL111024 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI HUNNINGHAKE, GARY MATTHEW · 2013 to 2022
$6.4M
Automated in vivo analysis of tumor growth rate as a guide for therapeutic decisions to advance personalized cancer treatmentR01CA203636 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI NISHINO, MIZUKI · 2017 to 2021
$2.9M
Detecting racial disparities in cancer survival by integrating multiple high-dimensional observational studiesR01CA269398 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GUHA, SUBHARUP, LI, YI · 2022 to 2025
$1.3M
NCI NIH HHS R01 CA203636NCI NIH HHS R01 CA269398NCI NIH HHS R35 CA197449NCI NIH HHS U01 CA209414NHLBI NIH HHS R01 HL111024NHLBI NIH HHS R01 HL130974NHLBI NIH HHS R01 HL135142NIH/NCI U01CA209414NIH/NHLBI R01HL111024
6 · The paper itself

Abstract

This retrospective study developed an automated algorithm for 3D segmentation of adipose tissue and paravertebral muscle on chest CT using artificial intelligence (AI) and assessed its feasibility. The study included patients from the Boston Lung Cancer Study (2000-2011). For adipose tissue quantification, 77 patients were included, while 245 were used for muscle quantification. The data were split into training and test sets, with manual segmentation as the ground truth. Subcutaneous and visceral adipose tissues (SAT and VAT) were segmented separately. Muscle area, mean attenuation value, and intermuscular adipose tissue percentage (IMAT%) were calculated in the paravertebral muscle segmentation. The AI algorithm was trained on the training sets, and its performance was evaluated on the test sets. The AI achieved Dice scores above 0.87 and showed excellent correlations for VAT/SAT ratios, muscle attenuation value, and IMAT% (correlation coefficients > 0.98, p < 0.001). The mean differences between the AI and ground truth were minimal (VAT/SAT ratio: 0.7%; muscle attenuation value: 1 HU; IMAT%: <1%). In conclusion, we developed a feasible AI algorithm for automated 3D segmentation of adipose tissue and paravertebral muscle on chest CT.

Indexed as

Adipose TissueAlgorithmsBody CompositionImaging, Three-DimensionalTomography, X-Ray ComputedAgedArtificial IntelligenceFemaleHumansMaleMiddle AgedRetrospective StudiesAdipose tissueArtificial intelligenceSarcopeniaThoraxTomographyX-ray computed

Identifiers

PMID39738489
PMCPMC11686299

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Registered trials

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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.