Volumetric versus single slice measurements of core abdominal muscle for sarcopenia

Waduud, Mohammed Abdul; Adusumilli, Pratik; Drozd, Michael; Bailey, Marc A.; Cuthbert, Gary Andrew; Hammond, Christopher J; Scott, Julian

doi:10.1259/bjr.20180434

Cited by 17 publications

(10 citation statements)

References 11 publications

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“…Anatomical landmarks, such as the vertebrae and other bony structures, are used as boundaries when performing volumetric measurements of adipose and muscle tissue compartments. The boundaries used for volumetric analyses include the areas between the 2 nd lumbar vertebra and sacrum [47], the 12 th thoracic vertebra and the superior aspect of the femoral head for adipose tissue and the inferior surface of the 1 st sacral vertebra for the determination of the paraspinal muscles [48], the 10 th thoracic vertebra and the 5 th lumbar vertebra [49] and the 1 st and the 5 th lumbar vertebra [50]. The literature lacks studies that would have compared the performance of different levels used for 2D single-slice BCA and volumes used for BCA in the evaluation of the likelihood of different outcomes in different diseases.…”

Section: D and 3d Measurements And The Applied Cut-off-values For The Ct-based Body Composition Metricsmentioning

confidence: 99%

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review

Tolonen

Pakarinen

Sassi

et al. 2021

European Journal of Radiology

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Section: D and 3d Measurements And The Applied Cut-off-values For The Ct-based Body Composition Metricsmentioning

confidence: 99%

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review

Tolonen

Pakarinen

Sassi

et al. 2021

European Journal of Radiology

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“…Furthermore, we measured the VBA at the third lumbar vertebrae instead of the fourth lumbar vertebrae, as previously evaluated by Oura et al, as we have previously shown morphometric measurements at this level to be most representative of structural volume. 20 Despite this, the margin of error in estimating patient height was comparable. Oura et al demonstrated volumetric quantification of the VBA to provide the most accurate representation of patient height.…”

Section: Discussionmentioning

confidence: 98%

Validation of two-dimensional vertebral body parameters in estimating patient height in elderly patients

Waduud

Sucharitkul

Drozd

et al. 2019

The British Journal of Radiology

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Objectives Standardised comparison of abdominal muscle and adipose tissue is often utilised in morphometric clinical research. Whilst measurements are traditionally standardised against the patient’s height, this may not be always practically feasible. The aim of this study was to investigate the relationship between measurements of the vertebral body and patient height. Methods We analysed cross-sectional CT scans. Measurements of the vertebral body area (VBA), anteroposterior vertebral body diameter (APVBD) and lateral vertebral body diameter (LVBD) were made by two independent investigators by manual tracing. Patients were randomly divided into two groups: Group 1 standardisation and Group 2 validation. We compared height and vertebral body parameters from patients in Group 1 and mathematically modelled this relationship. We then utilised the model to predict the height of patients in Group 2 and compared this with their actual height. Observer variability was assessed using Bland–Altman plots and t-tests of differences. Results CT scans from 382 patients were analysed. No significant intraobserver or interobserver differences were apparent when measuring vertebral body parameters. We describe models which enable the prediction of the patients’ height using the measured VBA, APVBD and LVBD. No significant differences were observed between the patients predicted and actual heights in the validation group. Conclusions We demonstrate an important relationship between measurements of the patient’s height and the vertebral body. This can be utilised in future research when the patient’s height has not been measured. Advances in knowledge In the absence of the patient’s height, we demonstrate that two-dimensional vertebral body parameters may be reliably used to standardise morphometric measurements.

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“…The cross-sectional area (CSA) of muscle tissue at the level of the 3 rd lumbar spine (L3) provides reproducible evaluation of muscle size in cancer patients without the need for additional examinations. The measurements collected from a single slice CT image reveal solid evidence that correlates strongly with whole-body adipose tissue and skeletal muscle [ 15 – 17 ]. The common method is to manually draw the total CSA of all muscle groups at L3 or to quantify the CSA using thresholds of Hounsfield units (HU) from -29 to 150 for skeletal muscle using the available software [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer

et al. 2022

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Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

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Volumetric versus single slice measurements of core abdominal muscle for sarcopenia

Abstract: Article:Waduud, MA orcid.org/0000-0001-5567-9952, Adusumilli, P, Drozd, M orcid.org/0000-0003-0255-4624 et al. (4 more authors) (2019) Volumetric versus single slice measurements of core abdominal muscle for Sarcopenia. British Journal of Radiology, 92

Cited by 17 publications

References 11 publications

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review

Validation of two-dimensional vertebral body parameters in estimating patient height in elderly patients

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer

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