Evidence map›Paper›PMID 39144051›Full record

ArticleQuantitative imaging in medicine and surgery2024

Computed tomography (CT)-based skeletal muscle vertebral-related index to assess low muscle mass in patients with non-small cell lung cancer.

Yilong Huang, Feng Yuan, Lei Yang, Honglei Guo, Yuanming Jiang, Hanxue Cun, Zhanglin Mou, Jiaxin Chen, Chunli Li, Zhenguang Zhang and 1 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

11 authors.

Yilong Huang *Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Feng Yuan *Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Lei YangDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Honglei GuoDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Yuanming JiangDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Hanxue CunDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Zhanglin MouDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Jiaxin ChenDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Chunli LiDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Zhenguang ZhangDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Bo HeDepartment of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients with lung cancer accompanied by sarcopenia may have a poor prognosis. Normally, low muscle mass associated with sarcopenia is assessed using the skeletal muscle index (SMI). It remains unclear whether the standardized skeletal muscle area (SMA) using 2-dimensional (2D) vertebral metrics (called the skeletal muscle vertebral related index, SMVI) could substitute for SMI when it is missing. The aim of this study was to investigate the feasibility of SMVI as an alternative to SMI, and their associations with overall survival (OS) in patients with non-small cell lung cancer (NSCLC). Methods: In this single-center study, a retrospective analysis was conducted on 433 NSCLC patients who underwent computed tomography (CT) scans. At the third lumbar vertebra (L3) level, measurements were taken for SMA, vertebral body area, transverse vertebral diameter (TVD), longitudinal vertebral diameter (LVD), and vertebral height (VH). The 4 SMVIs were skeletal muscle vertebral ratio (SMVR) (SMA/vertebral body area), skeletal muscle transverse vertebral diameter index (SMTVDI) (SMA/TVD2), skeletal muscle longitudinal vertebral diameter index (SMLVDI) (SMA/LVD2), and skeletal muscle vertebral height index (SMVHI) (SMA/VH2). The patients were categorized into low and high muscle mass groups based on SMI, and the differences in SMVIs between the 2 groups were compared to assess their correlation with SMI. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were utilized to assess the discriminatory ability. Kaplan-Meier curves were employed to compare the survival disparity between the 2 groups. Results: We included 191 male and 242 female patients in this study. Compared to the high muscle mass group, patients in the low muscle mass group exhibited significantly lower SMVR, SMTVDI, SMLVDI, and SMVHI (all P<0.05). All 4 SMVIs showed a positive correlation with SMI, with Spearman correlation coefficients of 0.83, 0.76, 0.75, and 0.67, respectively (all P<0.001). The AUC for diagnosing low muscle mass was higher than 0.8 for all 4 SMVI parameters. The Kaplan-Meier curve revealed that the low-risk group had a better survival probability than the high-risk group in the SMVR, SMTVDI, and SMLVDI. Conclusions: The SMVI functions as an alternative metric for evaluating skeletal muscle mass in the assessment of NSCLC based on SMI.

Indexed as

computed tomography (CT)Lung canceroverall survival (OS)sarcopenia

Identifiers

PMID39144051
PMCPMC11320558

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.