Skip to content
2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background:

Non-invasive imaging methods are still lacking for the evaluation of muscle changes in diabetes.

Purpose:

To investigate the feasibility of muscle CT radiomics in evaluating muscle changes in diabetes.

Materials and Methods:

60 diabetics and 60 health controls (HC) were assessed with the method of muscle CT radiomics. 93 CT images of radiomics features of the pectoralis major muscle (PMM) were obtained by using the software 3D Slicer and were then compared between diabetics and HC cases. The least absolute shrinkage and selection operator (LASSO) regression method was used to establish a prediction model. The receiver operating characteristic (ROC) curve was used to determine the performance of the model.

Results:

Diabetics and HC cases differed in 19 radiomics features (P<0.05). By using the LASSO method, 6 features were finally selected. The AUC of the model in the discrimination of diabetics and HC were 0.92 and 0.90, respectively, for the training cohort and validation cohort.

Conclusion:

Muscle CT radiomics is feasible in evaluating muscle changes in diabetes.

© 2024 Li and Wu
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056268543231113051451
2024-01-01
2025-09-11

  • From This Site

    /content/journals/cmir/10.2174/0115734056268543231113051451
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/CMIR-20-E15734056268543.html?itemId=/content/journals/cmir/10.2174/0115734056268543231113051451&mimeType=html&fmt=ahah

References

  1. YinL. LiN. JiaW. WangN. LiangM. YangX. DuG. Skeletal muscle atrophy: From mechanisms to treatments.Pharmacol. Res.202117210580710.1016/j.phrs.2021.10580734389456
     [Google Scholar]
  2. WilkinsonD.J. PiaseckiM. AthertonP.J. The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans.Ageing Res. Rev.20184712313210.1016/j.arr.2018.07.00530048806
     [Google Scholar]
  3. HowardE.E. PasiakosS.M. FussellM.A. RodriguezN.R. Skeletal muscle disuse atrophy and the rehabilitative role of protein in recovery from musculoskeletal injury.Adv. Nutr.2020114989100110.1093/advances/nmaa01532167129
     [Google Scholar]
  4. OkunJ.G. RusuP.M. ChanA.Y. WuY. YapY.W. SharkieT. SchumacherJ. SchmidtK.V. Roberts-ThomsonK.M. RussellR.D. ZotaA. HilleS. JungmannA. MaggiL. LeeY. BlüherM. HerzigS. KeskeM.A. HeikenwalderM. MüllerO.J. RoseA.J. Liver alanine catabolism promotes skeletal muscle atrophy and hyperglycaemia in type 2 diabetes.Nat. Metab.20213339440910.1038/s42255‑021‑00369‑933758419
     [Google Scholar]
  5. EhrmannD. KulzerB. RoosT. HaakT. Al-KhatibM. HermannsN. Risk factors and prevention strategies for diabetic ketoacidosis in people with established type 1 diabetes.Lancet Diabetes Endocrinol.20208543644610.1016/S2213‑8587(20)30042‑532333879
     [Google Scholar]
  6. SaeediP. PetersohnI. SalpeaP. MalandaB. KarurangaS. UnwinN. ColagiuriS. GuariguataL. MotalaA.A. OgurtsovaK. ShawJ.E. BrightD. WilliamsR. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition.Diabetes Res. Clin. Pract.201915710784310.1016/j.diabres.2019.10784331518657
     [Google Scholar]
  7. CabelkaC.A. BaumannC.W. CollinsB.C. NashN. LeG. LindsayA. SpangenburgE.E. LoweD.A. Effects of ovarian hormones and estrogen receptor α on physical activity and skeletal muscle fatigue in female mice.Exp. Gerontol.201911515516410.1016/j.exger.2018.11.00330415069
     [Google Scholar]
  8. LeeH. HaT.Y. JungC.H. NirmalaF.S. ParkS.Y. HuhY.H. AhnJ. Mitochondrial dysfunction in skeletal muscle contributes to the development of acute insulin resistance in mice.J. Cachexia Sarcopenia Muscle20211261925193910.1002/jcsm.1279434605225
     [Google Scholar]
  9. NisrR.B. ShahD.S. GanleyI.G. HundalH.S. Proinflammatory NFkB signalling promotes mitochondrial dysfunction in skeletal muscle in response to cellular fuel overloading.Cell. Mol. Life Sci.201976244887490410.1007/s00018‑019‑03148‑831101940
     [Google Scholar]
  10. EdalatiM. HastingsM.K. SorensenC.J. ZayedM. MuellerM.J. HildeboltC.F. ZhengJ. Diffusion tensor imaging of the calf muscles in subjects with and without diabetes mellitus.J. Magn. Reson. Imaging20194951285129510.1002/jmri.2628630230096
     [Google Scholar]
  11. StarmansM.P.A. HoL.S. SmitsF. BeijeN. de KruijffI. de JongJ.J. SomfordD.M. BoevéE.R. te SlaaE. CaubergE.C.C. KlaverS. van der HeijdenA.G. WijburgC.J. van de LuijtgaardenA.C.M. van MelickH.H.E. CauffmanE. de VriesP. JacobsR. NiessenW.J. VisserJ.J. KleinS. BoormansJ.L. van der VeldtA.A.M. Optimization of preoperative lymph node staging in patients with muscle-invasive bladder cancer using radiomics on computed tomography.J. Pers. Med.202212572610.3390/jpm1205072635629148
     [Google Scholar]
  12. MehtaP SinhaS KashidS Exploring texture analysis to optimize bladder preservation in muscle invasive bladder cancer.Clin Genitourin Cancer2022S1558-767322002415
     [Google Scholar]
  13. LingZ. LiX. WuG. Radiomics of CTA is feasible in identifying muscle ischemia.Acta Radiol.2022284185122111988436050936
     [Google Scholar]
  14. KangJ. ChoiY.J. KimI. LeeH.S. KimH. BaikS.H. KimN.K. LeeK.Y. LASSO-based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer.Cancer Res. Treat.202153377378310.4143/crt.2020.97433421980
     [Google Scholar]
  15. DongX. DanX. YawenA. HaiboX. HuanL. MengqiT. LinglongC. ZhaoR. Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning.Thorac. Cancer20201192650265910.1111/1759‑7714.1359832767522
     [Google Scholar]
  16. LuC.Q. WangY.C. MengX.P. ZhaoH.T. ZengC.H. XuW. GaoY.T. JuS. Diabetes risk assessment with imaging: A radiomics study of abdominal CT.Eur. Radiol.20192952233224210.1007/s00330‑018‑5865‑530523453
     [Google Scholar]
  17. HandaS. ChiaA. HtoonH.M. LamP.M. YapF. LingY. Myopia in young patients with type 1 diabetes mellitus.Singapore Med. J.201556845045410.11622/smedj.201512226310273
     [Google Scholar]
  18. IzzoA. MassiminoE. RiccardiG. Della PepaG. A narrative review on sarcopenia in type 2 diabetes mellitus: prevalence and associated factors.Nutrients202113118310.3390/nu1301018333435310
     [Google Scholar]
  19. NishikawaH. FukunishiS. AsaiA. YokohamaK. OhamaH. NishiguchiS. HiguchiK. Sarcopenia, frailty and type 2 diabetes mellitus.Mol. Med. Rep.202124685410.3892/mmr.2021.1249434651658
     [Google Scholar]
  20. HongS. ChoiK.M. Sarcopenic obesity, insulin resistance, and their implications in cardiovascular and metabolic consequences.Int. J. Mol. Sci.202021249410.3390/ijms2102049431941015
     [Google Scholar]
  21. ShangJ. GuoY. MaY. HouY. Cardiac computed tomography radiomics: A narrative review of current status and future directions.Quant. Imaging Med. Surg.20221263436345310.21037/qims‑21‑102235655815
     [Google Scholar]
  22. RajA. DehingiaN. SinghA. McAuleyJ. McDougalL. Machine learning analysis of non-marital sexual violence in India.EClinicalMedicine20213910104610.1016/j.eclinm.2021.10104634401685
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056268543231113051451
Loading
Chest CT Radiomics is Feasible in Evaluating Muscle Change in Diabetes Patients
Curr. Med. Imaging 20, e15734056268543 (2024); https://doi.org/10.2174/0115734056268543231113051451
/content/journals/cmir/10.2174/0115734056268543231113051451
/content/journals/cmir/10.2174/0115734056268543231113051451
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): CT; Diabetes; Muscle; Radiomics; Regression; Skeletal muscle

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test