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Review
 Diagnostic performance of various radiological modalities in the detection of sarcopenia within Asian populations: a systematic review
Shi Wei Ang1,*orcid, Jacqueline Liew1,*orcid, Vanessa Malishree Dharmaratnam2orcid, Vanessa Yi Jean Yik3orcid, Shawn Kok4orcid, Syed Aftab4orcid, Cherie Tong5orcid, Hui Bing Lee5orcid, Shimin Mah6orcid, Clement Yan6orcid, Bin-Tean Teh7orcid, Frederick H. Koh2orcid

DOI: https://doi.org/10.3393/ac.2024.00080.0011
Published online: December 20, 2024

1Yong Loo Lin School of Medicine, National University of Singapore, Singapore

2Department of General Surgery, Sengkang General Hospital, Singhealth, Singapore

3Duke-NUS Medical School, Singapore

4Department of Radiology, Sengkang General Hospital, Singhealth, Singapore

5Department of Dietetics, Sengkang General Hospital, Singhealth, Singapore

6Department of Physiotherapy, Sengkang General Hospital, Singhealth, Singapore

7National Cancer Centre Singapore, Singhealth, Singapore

Correspondence to: Fredrick H. Koh, MBBS, MMed (Surg), FRCSEd, FAMS (Surg), PhD Department of General Surgery, Sengkang General Hospital, Singhealth, 110 Sengkang East Way, Singapore 544886 Email: frederickkohhx@gmail.com
*Shi Wei Ang and Jacqueline Liew contributed equally to this study as co-first authors.
• Received: January 28, 2024   • Revised: April 22, 2024   • Accepted: May 20, 2024

© 2024 The Korean Society of Coloproctology

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Purpose
    Diagnosing sarcopenia necessitates the measurement of skeletal muscle mass. However, guidelines lack a standardized imaging modality with thresholds validated among Asians. This systematic review compared ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and bioelectrical impedance analysis (BIA)/body composition monitoring in the detection of sarcopenia within Asian populations.
  • Methods
    PubMed and Embase were systematically searched for studies analyzing ultrasonography, CT, MRI, and BIA in diagnosing sarcopenia among Asians. Study quality was assessed using the Newcastle-Ottawa scale.
  • Results
    Pooled findings from 21,598 patients across 25 studies were examined. In receiver operating characteristic analysis, ultrasound displayed a pooled mean area under the curve (AUC) of 0.767 (95% confidence interval [CI], 0.709–0.806), with mean sensitivity of 81.1% (95% CI, 0.744–0.846) and specificity of 73.1% (95% CI, 0.648–0.774), for detecting sarcopenia in Asian populations. CT exhibited an AUC of 0.720 (sensitivity, 54.0%; specificity, 92.0%). MRI demonstrated an AUC of 0.839 (sensitivity, 67.0%; specificity, 66.0%). BIA displayed an AUC of 0.905 (95% CI, 0.842–0.968), 80.7% sensitivity (95% CI, 0.129–0.679), and 82.4% specificity (95% CI, 0.191–0.633).
  • Conclusion
    Various modalities aid in diagnosing sarcopenia, and selection should be individualized. Although only BIA and dual-energy x-ray absorptiometry are recommended by the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People, ultrasound imaging may hold diagnostic value for sarcopenia in the Asian population. In certain groups, diagnostic use of CT and MRI is warranted. Future research can standardize and validate modality-specific thresholds and protocols within Asian populations.
  • Keywords: Sarcopenia; Asian; Risk assessment; Diagnosis
Sarcopenia is an age-associated, progressive, and generalized skeletal muscle disorder characterized by the loss of muscle mass, decreased muscle strength, and/or reduced physical performance [1]. Since its initial description in 1988 by Rosenberg [2], sarcopenia has been increasingly recognized as a clinically important age-related syndrome. In coloproctology, sarcopenia is a known predictor of poor surgical outcomes, including surgical site infection, anastomotic leak [3], postoperative ileus, and extended hospitalization [4]. It is also linked to major adverse outcomes in older adults, such as frailty, more frequent hospitalizations, and increased mortality, all of which contribute to rising healthcare costs [5, 6]. As the most populous continent and one with a rapidly ageing population, Asia must be prepared to address the challenges associated with sarcopenia. With the global incidence of colorectal cancer on the rise [7], the detection of sarcopenia is crucial for optimizing postsurgical outcomes in colorectal surgery. In their latest guidelines, released in 2019, the Asian Working Group for Sarcopenia (AWGS) recommended the implementation of sarcopenia screening and identification in both clinical practice and community healthcare programs. According to the AWGS, these initiatives should be based on specific diagnostic strategies and cutoff values that have been validated in Asian populations [8].
The AWGS 2019 consensus recommends using either dual-energy x-ray absorptiometry (DXA) or multifrequency bioelectrical impedance analysis (BIA) to measure appendicular skeletal muscle mass as part of the diagnostic criteria for sarcopenia, which also include evaluations of muscle strength and physical performance [8]. However, these diagnostic methods have recognized limitations. Specifically, BIA cutoff points for low muscle mass are not universal but rather specific to the population and the device used. Additionally, the accuracy of BIA can be impacted by the hydration status of the tissue. These factors pose challenges for its use in clinical settings [1]. In contrast, while DXA measurements of appendicular skeletal muscle have been validated for lean muscle mass, this approach does not provide insights into muscle quality. Moreover, DXA is less widely accessible in current clinical practice [9]. Consequently, a recent surge in research has focused on the diagnostic capabilities of more broadly available techniques such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), which are included in European guidelines. These studies have aimed to assist in integrating the diagnosis and management of sarcopenia into clinical practice.
An unmet clinical need exists for a standardized imaging modality with validated cutoff values for the diagnosis of sarcopenia in the Asian population. Consequently, our objective was to review the application of BIA, DXA, ultrasonography, CT, and MRI in diagnosing sarcopenia in this population.
Literature search
This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [9]. We performed an electronic systematic literature search across major databases, namely PubMed and Embase, up to May 2023 without imposing a start date restriction. The search strategy utilized Medical Subject Heading (MeSH) terms, specifically: “sarcopenia” AND “Asian” AND (“assessment” OR “diagnosis”). The most recent search was performed on May 22, 2023. The PRISMA flowchart of the study selection process is shown in Fig. 1.
Study selection
Following the database search, two authors (SWA and JL) independently screened the studies based on the title and abstract. The selected studies were subsequently analyzed in full and considered for inclusion in this systematic review. Any discrepancies were resolved by a third party (FHXK).
Peer-reviewed primary research studies—including cross-sectional, cohort, and case-control studies—reporting on the role of ultrasound, CT, MRI, or BIA in the diagnosis and assessment of sarcopenia in exclusively Asian populations were included. Studies not evaluating the accuracy of an imaging modality in assessing muscle quantity or quality, as well as those with non-Asian study populations, were excluded. Additionally, non-English articles, preprint manuscripts, book chapters, letters, abstracts, and review articles were not considered for inclusion.
Data extraction and quality assessment
The following information was extracted from each article: family name of the first author, publication year, country where the study was conducted, name of the journal, sample size, imaging system and imaging analysis tool used, muscle site(s) chosen for assessment with associated anatomic landmark(s), and definition of sarcopenia adopted by the study (Table 1) [10–34]. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS) to assess the risk of bias.
Definitions
Appendicular skeletal muscle mass defined as the total lean muscle mass of the upper and lower limbs, measured in kilograms. The appendicular skeletal muscle index is calculated by dividing appendicular skeletal muscle mass (in kg) by the square of height (in meters), resulting in units of kg/m². The psoas muscle mass index is determined by summing the cross-sectional area of the right and left psoas muscles at the L3 level (in cm²) and dividing by the square of height (in meters), yielding units of cm²/m².
Literature search
A total of 1,889 studies were identified from searches of the PubMed and Embase databases. After the removal of 508 duplicates, 1,381 studies were screened based on their titles and abstracts. Following this initial screening, 1,271 studies were excluded, leaving 110 studies for full-text review. Of these, 85 studies were subsequently excluded for various reasons: 63 studies reported outcomes that were not relevant to our review, 20 studies were not of the appropriate study type, and 2 studies focused on a different patient population. Ultimately, 25 articles met the inclusion criteria and were incorporated into this systematic review [10–34].
Study characteristics
Of the 25 included studies, 9 evaluated the use of ultrasound [10–18], 7 focused on CT [19–25], 2 on MRI [26, 27], and 7 on BIA [28–34]. These included both prospective and retrospective studies from countries across Asia, including Türkiye, China, Thailand, Japan, Korea, India, and Saudi Arabia. Diagnostic tools employed in these studies comprised AWGS and European Working Group on Sarcopenia in Older People (EWGSOP) systems. The 25 studies included 22 cross-sectional studies [10, 11, 13–17, 19–27, 29–34], 1 cohort study [18], and 2 case-control studies [12, 28], with participant numbers ranging from 38 to 11,845. The characteristics of these studies are summarized in Table 1 [10–34].
Based on the NOS assessment of the 25 included studies, none were considered to carry a high risk of bias. Each study achieved a score ranging from 3 to 7 points (Supplementary Tables 1–3) [10–34].
Outcomes
The diagnostic accuracy of various modalities in the detection of sarcopenia is summarized in Table 2 [10–17, 19–22, 26–30]. The correlations of various modalities are summarized in Table 3 [14–16, 18–28, 30–34].

Ultrasound

The diagnostic accuracy of ultrasonography in detecting sarcopenia within Asian populations was analyzed by pooling findings from 1,990 patients across 9 studies published between 2018 and 2022 [10–18]. Each study utilized a linear transducer, and scanning was performed in the axial and transverse planes.
Seven of the 8 studies diagnosed sarcopenia using BIA in accordance with the AWGS guidelines [10–13, 15–17]. One study utilized BIA to diagnose sarcopenia following the EWGSOP criteria [14], while another employed DXA in line with AWGS guidelines [17]. Ultrasound examination of the thigh muscles, particularly the rectus femoris, was performed in 5 of the 9 studies [11–13, 15, 16]. 1 study conducted ultrasound assessments at the umbilicus [14], and 2 studies applied ultrasound to the calf muscles, including the tibialis anterior, gastrocnemius, and soleus [10, 17]. The parameters employed to evaluate sarcopenia included muscle thickness, muscle cross-sectional area, muscle stiffness and echo intensity.
The pooled mean area under the curve (AUC) across the 8 studies providing AUC values regarding the diagnostic accuracy of ultrasonography for detecting sarcopenia was 0.767, with a 95% confidence interval (CI) of 0.709 to 0.806 [10–17]. The mean sensitivities and specificities pooled from the available studies were 81.1% (95% CI, 0.744–0.846) and 73.1% (95% CI, 0.648–0.774), respectively.
Eight correlation data were derived from 4 studies [14–16, 18]. Five of 8 correlation data reported a low to negligible positive correlation (0.00–0.50) between ultrasound and BIA [14–16]. Three of 8 correlation data reported a moderate to high positive correlation (0.50–0.90) between ultrasound and CT [18].

Computed tomography

Another analysis examined the findings from 14,438 patients, pooled across 7 studies demonstrating the CT-based detection of sarcopenia in Asian populations [19–25]. These studies focused on East Asian populations from China, Japan, and Korea and were published between 2016 and 2022. The cross-sectional areas of selected muscles were measured at the T4, T12, and L3 levels. The muscles assessed for sarcopenia included the erector spinae, latissimus dorsi, rectus abdominis, obliquus externus abdominis, obliquus internus abdominis, internal and external intercostal muscles, pectoralis, paraspinal, serratus, and psoas muscles. All studies diagnosed sarcopenia using BIA in accordance with either the AWGS or the EWGSOP guidelines. One study, conducted by Sato et al. [20], revealed an AUC of 0.720, sensitivity of 54.0%, and specificity of 92.0%. Regarding the relationship between CT and BIA, of the 7 studies, 1 reported a very high positive correlation (0.90–1.00) [22], 4 reported a moderate to high correlation (0.50–0.90) [19, 21, 23, 24], and 2 reported a low to moderate positive correlation (0.30–0.50) [20, 25].

Magnetic resonance imaging

The diagnostic accuracy of MRI in detecting sarcopenia within Asian populations was analyzed in 2 studies, encompassing a total of 574 patients [26, 27]. The assessment of sarcopenia was focused on 2 parameters: the cross-sectional area of the thigh muscles and the fatty degeneration of the lumbar extensor muscles. Nakamura et al. [27] recorded an AUC of 0.839, while Heo et al. [26] presented a sensitivity of 67.0% and specificity of 66.0%. One study reported a negligible correlation (0.00–0.30) between MRI and DXA [26], while the other reported a low to moderate positive correlation (0.30–0.70) between MRI and body mass index [27].

BIA/body composition monitor

The diagnostic accuracy of BIA in the diagnosis of sarcopenia in Asian populations was evaluated by analyzing findings from 4,596 patients across 7 studies [28–34]. Appendicular lean mass and appendicular skeletal muscle index were measured to assess sarcopenia.
The pooled mean AUC from the 3 studies assessing the diagnostic accuracy of BIA/body composition monitoring for detecting sarcopenia was 0.905 (95% CI, 0.842–0.968) [28–30]. The mean sensitivities and specificities pooled from the available studies were 80.7% (95% CI, 0.679–0.937) and 82.4% (95% CI, 0.633–1.015), respectively.
Seven correlation data were derived from 6 studies [28, 30–34]. Three of the 7 correlation data reported a very high positive correlation (0.90–1.00) between BIA/body composition monitoring and DXA [28, 30]. Two studies had 3 correlation data that indicated a moderate to high correlation (0.50–0.90) between BIA and DXA [31, 32], whereas another study found a moderate positive correlation (0.50–0.70) between BIA and CT [33].
Sarcopenia is a complex metabolic process characterized by the loss of muscle mass, which may occur alone or in conjunction with changes in fat mass and fatty infiltration of muscle. Imaging plays a key role in the quantification of muscle mass and the subsequent diagnosis of sarcopenia. However, the assessment of sarcopenia should not be confined to evaluating muscle mass alone; it should also include an evaluation of the extent of myosteatosis, myofibrosis, disruption of muscle fibers, and oedema. These additional parameters are useful for determining muscle quality, which is also important for diagnosis. Early detection of sarcopenia can lead to timely interventions that minimize related complications, such as anastomotic leaks and surgical site infections commonly observed in colorectal surgery. Preoperatively, incorporating an appropriate diagnostic modality for sarcopenia into the preoperative assessment for all colorectal procedures could help stratify patients by risk and determine their suitability for surgery. The degree of sarcopenia should inform the extent to which treatment before surgery is recommended. Postoperatively, a sarcopenia diagnosis could inform the aggressiveness of nutritional replacement and physiotherapy required for the patient. The choice of modality would then be tailored to the patient’s profile, including their medical history, and weighed against the advantages and disadvantages detailed in Table 4. According to the AWGS 2019 consensus [8], DXA and multifrequency BIA are the most frequently used imaging modalities for detecting sarcopenia. Although MRI and CT are considered the gold standard for the diagnosis of this condition, their relative cost and restricted accessibility limit their routine use. Consequently, DXA and BIA are the recommended modalities for diagnosing sarcopenia. The results are summarized in Table 5. In this review, we have compared various imaging modalities to determine their utility in diagnosing sarcopenia.
DXA facilitates the measurement of body composition, including lean muscle mass, fat mass, and bone mineral content, which is valuable for assessing sarcopenia and related conditions such as osteoporosis. This modality offers validated cutoff values for identifying low muscle mass and exposes patients to less radiation than CT. Additionally, DXA can provide appendicular skeletal muscle mass results within minutes [1]. However, the clinical utility of DXA for diagnosing sarcopenia is hindered by considerable limitations. Its accuracy is compromised in patients with extracellular fluid accumulation, as it cannot differentiate between water and lean muscle mass. DXA is also unable to assess myosteatosis and does not offer insights into muscle quality. The presence of metal implants, such as those from total knee replacements or pacemakers, further complicates the assessment. The literature includes conflicting findings regarding the impact of metal implants on the estimation of lean muscle mass. While Donlon et al. [35] concluded that metal implants do not substantially impact these DXA measurements, Jang et al. [36] reported that DXA overestimated lean muscle mass in patients with total knee replacement. Additional steps may be required to correct for the presence of metal implants. Although DXA remains the most recognized imaging modality for a confirmatory diagnosis of sarcopenia, DXA machines are not readily available worldwide, especially in developing Asian countries [12]. This scarcity hampers the widespread use of DXA for diagnosing sarcopenia in the Asian population. Therefore, further research to validate DXA-measured appendicular skeletal muscle in various countries should improve the accuracy of sarcopenia diagnosis and help mitigate the risk of its progression to frailty [37].
BIA is a cost-efficient and accessible modality that utilizes whole-body electrical conductivity to estimate muscle mass. With the highest AUC of all imaging modalities in this review, BIA is the most accurate for diagnosing sarcopenia in Asian populations. BIA demonstrated good sensitivity and specificity in detecting sarcopenia when DXA was used as the reference test, with measurements that were positively and significantly correlated with those obtained using DXA. Additionally, validated cutoff values for the diagnosis of sarcopenia using BIA are available in the AWGS 2019 consensus. Further suggestions have proposed the use of BIA as an alternative to DXA. However, to accurately diagnose sarcopenia, adjustments to diagnostic models are necessary to account for variations in body composition among ethnic groups [1]. Given that BIA employs whole-body electrical conductivity, its measurements can be affected by changes in body conductivity. For instance, in patients with obesity, fat acts as a resistant conductor of electricity, which can impact the accuracy of BIA results. Similar to DXA, the accuracy of BIA measurements can also be influenced by the presence of oedema and the hydration status of the patient.
To date, no consensus has been reached on the preparation for and administration of BIA. Given the susceptibility of BIA to confounding factors, a standardized protocol should be recommended to ensure accuracy in sarcopenia diagnosis. Confounding factors that can skew BIA results include height and weight, patient position, hydration status, fasting, exercise, and voiding status [38–40]. The present literature consistently suggests that the patient should be in a supine position, with arms abducted to 30° and legs abducted to 45°, and their height and weight should be recorded at the time of BIA [38, 40, 41]. Moderate to severe physical activity should be avoided for at least 12 hours before the measurement [38, 41]]; additionally, patients should not drink water for at least 4 hours and should have voided beforehand [40–42]. Preferably, BIA should be performed in the morning after an overnight fast [38–40] to reduce variation and ensure feasibility and comfort for the patient, although consistency in the measurement time appears more important [5]. Each of the 2 electrodes should be placed on the hand and foot on the same side of the body [38, 39, 42].
Accordingly, careful patient selection in accordance with a standardized protocol is essential when diagnosing sarcopenia using BIA. While the AWGS recommends BIA for measuring muscle mass in the diagnosis of sarcopenia, its use in clinical practice is constrained by the fact that cutoff values for measurements are specific to both the population and the device. Therefore, given the accessibility of BIA, validating cutoff values in various populations could support its use as a viable alternative or complementary approach to DXA for the diagnosis of sarcopenia.
Ultrasonography is emerging as a promising imaging modality for the assessment of sarcopenia, given that ultrasound machines are inexpensive, portable, and readily available in most institutions. Five easily measurable parameters can be evaluated using ultrasonography to assess muscle quantity and quality. Muscle thickness and cross-sectional area are reliable parameters for assessing muscle mass. A decrease in muscle size also results in a reduction in fascicle length and pennation angle, which is the angle between the longitudinal axis of a muscle belly and its fibers. When measured, muscle echogenicity provides information on muscle quality. Typically, normal muscle appears predominantly hypoechoic, although some interspersed hyperechoic tissue may be present. The presence of myosteatosis and muscle fibrosis leads to increased muscle echogenicity. In patients with sarcopenia, the amount of intramuscular adipose tissue has been identified as an indicator of muscle quality and can be measured using equipment such as the MuscleSound device (MuscleSound Inc), which combines ultrasound and artificial intelligence (AI) technology. This indicates that ultrasonography, with the additional evaluation of muscle quality, can offer a more thorough assessment of sarcopenia. Through pairing with AI, the process of diagnosing sarcopenia could be streamlined, yielding fast and accurate results. AI also has the potential to reduce user variability when evaluating large numbers of patients. This technology has been recently introduced in research studies, such as the HEROS study, to evaluate muscle quality in patients with sarcopenia [10]. Shear-wave elastography—an advanced form of ultrasonography—measures muscle contractility, which is correlated with muscle stiffness. This gives ultrasonography an additional advantage over DXA in its capacity to assess muscle quality, leading to a more informed diagnosis of sarcopenia. However, ultrasonography is not yet associated with a validated protocol or cutoff values for the diagnosis of sarcopenia. The accuracy of the results is limited by technique and patient factors, including body habitus and position. Since ultrasonography is heavily dependent on the operator and technique, standardization using traditional ultrasound may be more challenging to achieve. Although ultrasonography has a lower combined sensitivity and specificity compared to BIA, its low cost and absence of radiation warrant its consideration as an alternative modality in the diagnosis of sarcopenia. The integration of ultrasonography with emerging AI technology to reduce user dependency may improve the accuracy and sensitivity of ultrasonography, while maintaining its cost-effectiveness.
Another upcoming modality for sarcopenia assessment is CT, which similarly evaluates both muscle quantity and quality. The use of CT for detecting sarcopenia has become increasingly popular, particularly due to its routine application in cancer treatment and surveillance, as well as in preoperative planning. Assessing sarcopenia concurrently on CT can determine prognosis and facilitate timely intervention. When diagnosing sarcopenia with CT, the cross-sectional area of the psoas muscle at the L3 vertebral level is commonly measured, as this indicator is known for its accuracy [13] and minimal movement artefacts [14]. This area is typically indexed to the patient’s height to obtain the skeletal muscle index. On the same CT slice, measurements of adipose and subcutaneous tissues can be taken using CT attenuation values to assess the extent of myosteatosis and, consequently, muscle quality. However, the higher radiation dose associated with CT scans suggests that their use should be limited to specific patient groups who undergo routine scans, to avoid unnecessary radiation exposure. Given that other imaging modalities can provide satisfactory diagnostic outcomes, exposing patients to the additional and unnecessary radiation of a CT scan may not be justifiable. Moreover, the manual analysis of body composition on CT is usually performed by radiologists, who must delineate muscle groups for further evaluation. This manual process can be tedious and time-consuming. While automated software add-ons exist to streamline this labor-intensive task, the additional costs must be considered in terms of the financial impact on patients and healthcare systems. Therefore, at present, CT is considered a comparatively unfavorable tool for large-scale sarcopenia assessment, as its disadvantages outweigh those of other available modalities. Interestingly, although BIA and CT demonstrate a good correlation, ultrasonography only correlates well with BIA and not with CT. The element of operator dependence may have resulted in these differing correlations in modalities that are well-correlated themselves. Another potential reason for this discrepancy is the variability in BIA readings due to changes in body electrical conductivity and hydration status.
MRI displays excellent accuracy in the assessment of the quantity and quality of muscle tissue. Employing the Dixon technique [43], MRI can capture in-phase (water + fat) and opposed-phase (water − fat) images, allowing for the derivation of fat- and water-only phase images. This capability enables the assessment of fatty infiltration within muscle tissue, which aids in the evaluation of muscle quality. The accuracy of MRI in determining muscle mass has been further corroborated by studies that have compared MRI findings with data from cadaver analyses [16]. These advantages help to overcome the limitations of BIA and DXA, which cannot distinguish between water, fat, and muscle content. Additionally, MRI can be used to assess muscle cross-sectional area and identify radiological signs indicative of reduced muscle quality, all without exposing patients to the ionizing radiation associated with CT scans. However, the use of MRI is limited by its high cost and resource-intensive nature. Furthermore, not all healthcare institutions have convenient access to MRI equipment, which complicates its adoption as a diagnostic standard. Moreover, MRI scans take considerable time to complete, potentially delaying diagnosis and rendering this modality impractical in certain clinical settings. While waiting for an MRI scan to be completed, any patient movement can introduce motion artefacts, compromising image quality. The use of MRI for diagnosing sarcopenia is also contraindicated in certain patient groups, including those with claustrophobia or those with non–MRI-compatible metallic implants, such as pacemakers, implantable cardioverter defibrillators, hearing aids, and cochlear implants [15]. Despite these limitations, MRI may be a viable option for patients who require routine MRI scans for other comorbidities or when other muscle quantification methods like BIA, DXA, and ultrasonography are confounded by their conditions.
To our knowledge, no large-scale Asian study has compared the diagnostic reliability of various imaging modalities in diagnosing sarcopenia within a single group. Patient characteristics (especially age and underlying disease states), along with the diagnostic cutoff for sarcopenia at various assessment centers, may confound our results. Despite the conclusions drawn from our extensive literature review, cost considerations and resource allocation constraints must be considered when using these modalities to assess sarcopenia and when developing a definitive diagnostic protocol.
In conclusion, each diagnostic modality can contribute to the diagnosis of sarcopenia. The selection of a diagnostic tool must be carefully considered and tailored to the patient. While BIA and DXA represent the currently recommended methods for diagnosing sarcopenia, incorporating additional modalities such as ultrasound, CT, and MRI could improve the diagnostic algorithm for this disease. However, to establish validated cutoffs and confirm their accuracy in diagnosing sarcopenia, further validation studies that compare these modalities within individuals—along with research that stratifies study populations by relevant patient characteristics—are necessary. The inclusion of alternative imaging techniques may enable the diagnostic algorithm for sarcopenia to become more widely applicable and robust across different patient groups.

Conflict of interest

Frederick H. Koh is an Editorial Board member of Annals of Coloproctology, but was not involved in the reviewing or decision process of this manuscript. No other potential conflict of interest relevant to this article was reported.

Funding

None.

Author contributions

Conceptualization: all authors; Methodology: all authors; Validation: all authors; Writing–original draft: all authors; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Supplementary Table 1.

Newcastle-Ottawa quality assessment scale for cross-sectional studies
ac-2024-00080-0011-Supplementary-Table-1.pdf

Supplementary Table 2.

Newcastle-Ottawa quality assessment scale for cohort study
ac-2024-00080-0011-Supplementary-Table-2.pdf

Supplementary Table 3.

Newcastle-Ottawa quality assessment scale for case-control studies
ac-2024-00080-0011-Supplementary-Table-3.pdf
Supplementary materials are available from https://doi.org/10.3393/ac.2024.00080.0011.
Fig. 1.
Process of study selection in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
ac-2024-00080-0011f1.jpg
Table 1.
Summary of study characteristics
Study Country Study design No. of participants Imaging modality evaluated
Lin et al. [28] (2021) Taiwan Case-control 263 BCM
Cheng et al. [31] (2021) Hong Kong Cross-sectional 1,587 BIA
Wang et al. [32] (2016) China Cross-sectional 944 BIA
Laksmi et al. [29] (2019) Indonesia Cross-sectional 120 BIA
Luengpradidgun et al. [33] (2022) Thai Cross-sectional 50 BIA
Alkahtani [34] (2017) Saudi Arabia Cross-sectional 232 BIA
Kawakami et al. [30] (2022) Japan Cross-sectional 1,313 BIA (FFMI)
Cao et al. [23] (2022) China Cross-sectional 606 CT
Hamaguchi et al. [19] (2016) Japan Cross-sectional 541 CT
Sato et al. [20] (2022) Japan Cross-sectional 38 CT
Kim et al. [24] (2021) Korea Cross-sectional 11,845 CT
Moon et al. [21] (2022) Korea Cross-sectional 335 CT
Ohara et al. [25] (2020) Japan Cross-sectional 504 CT
Tan et al. [22] (2021) China Cross-sectional 569 CT
Nakamura et al. [27] (2022) Japan Cross-sectional 512 MRI
Heo et al. [26] (2020) Korea Cross-sectional 62 MRI
Isaka et al. [10] (2019) Japan Cross-sectional 60 Ultrasonography
Sri-On et al. [11] (2022) Thailand Cross-sectional 1,001 Ultrasonography
Deng et al. [12] (2022) China Case-control 75 Ultrasonography
Deng et al. [13] (2022) China Cross-sectional 235 Ultrasonography
Bahsi et al. [14] (2021) Turkish Cross-sectional 103 Ultrasonography
Hashida et al. [18] (2022) Japan Cohort 68 Ultrasonography
Rao et al. [16] (2022) India Cross-sectional 117 Ultrasonography
Hida et al. [15] (2018) Japan Cross-sectional 201 Ultrasonography
Wang et al. [17] (2023) China Cross-sectional 130 Ultrasonography

BCM, body composition monitor; BIA, bioimpedance analysis; FFMI, fat-free mass index; CT, computed tomography; MRI, magnetic resonance imaging.

Table 2.
Diagnostic accuracy of various modalities in the detection of sarcopenia
Study Diagnosis of sarcopenia Muscle parameter AUC Cutoff Sensitivity (%) Specificity (%) P-value
Ultrasonography
 Isaka et al. [10] (2019) BIA (AWGS) Prediction of aSMI - -
 Tibialis anterior thickness 0.820 14.4 mm <0.01
 Gastrocnemius thickness 0.480 14.4 mm 0.49a
 Soleus thickness 0.760 22.9 mm 0.48a
Prediction of low grip strength - -
 Tibialis anterior echo intensity 0.680 59.0 <0.01
 Gastrocnemius echo intensity 0.740 40.7 0.01a
 Soleus echo intensity 0.540 63.2 0.15
 Sri-On et al. [11] (2022) BIA (AWGS) Rectus femoris thickness -
 Sarcopenia 0.920 ≤11 mm 90.9 92.2
 Severe sarcopenia 0.950 ≤10 mm 92.5 97.4
Male - -
Female - -
 Deng et al. [12] (2022) BIA (AWGS) Rectus femoris thickness 0.802 - - - <0.001
Rectus femoris CSA 0.816 - - - <0.001
SWE mean 0.863 - - - <0.001
 Deng et al. [13] (2022) BIA (AWGS) Rectus femoris thickness
 Male 0.839 52.2 mm 76.4 75.0 <0.001
 Female 0.805 49.9 mm 89.5 60.0 <0.001
Rectus femoris CSA
 Male 0.872 6.805 cm2 90.9 67.9 <0.001
 Female 0.791 6.996 cm2 73.7 86.7 <0.001
 Bahsi et al. [14] (2021) BIA (EWGSOP) Umbilicus visceral fat thickness
 Male 0.921 ≤22.57 mm 89.0 80.0 0.003
 Female 0.834 ≤24.97 mm 92.0 70.0 <0.001
 Hida et al. [15] (2018) BIA (AWGS) Thigh muscle thickness -
 Male 0.710 <36 mm 72.0 73.9
 Female 0.740 <34 mm 72.2 72.4
 Rao et al. [16] (2022) BIA (AWGS) Thigh muscle thickness
 Male 0.748 19 mm 70.0 70.0 <0.001
 Female 0.594 17 mm 70.0 55.0 0.10
Rectus femoris thickness - -
 Male 0.749 76.0 64.0 <0.001
 Female 0.628 70.0 55.0 0.09
 Wang et al. [17] (2023) DXA (AWGS) Stiffness -
 Tibialis anterior 0.856 3.02 m/sec 84.6 81.5
 Gastrocnemius (medial) 0.864 2.26 m/sec 83.1 81.5
 Soleus 0.722 3.33 m/sec 86.2 59.5
Computed tomography
 Hamaguchi et al. [19] (2016) BIA L3 psoas - - - -
 Male 6.36 cm2/m2
 Female 3.92 cm2/m2
 Sato et al. [20] (2022) BIA (AWGS) T12 erector spinae CSA -
 Sarcopenia 0.720 9.41 cm2/m2 54.0 92.0
 Severe sarcopenia 0.770 8.43 cm2/m2 75.0 94.0
Pectoralis CSA - - - - -
 Moon et al. [21] (2022) BIA (EWGSOP) T4 pectoralis, intercostalis, paraspinal, serratus, and latissimus muscle CSA - - - -
 Male 100.06 cm2
 Female 66.93 cm2
T4MI - - - -
 Male 33.69 cm2/m2
 Female 26.01 cm2/m2
Pectoralis muscle CSA - - - -
 Male 29.00 cm2
 Female 18.29 cm2
PMI - - - -
 Male 10.17 cm2/m2
 Female 7.31 cm2/m2
 Tan et al. [22] (2021) BIA (EWGSOP2) T12 erector spinae, latissimus dorsi, rectus abdominis, obliquus externus, internus abdominis, and internal and external intercostal muscle CSA - - - -
 Male <75.67 cm2
 Female <40.39 cm2
Magnetic resonance imaging
 Heo et al. [26] (2020) DXA (AWGS) L3 paravertebral - 0.319 67.0 66.0 0.01
 Nakamura et al. [27] (2022) ROC analysis of PSMI and PDFF for low BMI (GLIM criteria) PSMI (level of the superior mesenteric artery) - - <0.001
 Male 0.906 <12.62 cm2/m2
 Female 0.771 <9.77 cm2/m2
PDFF 0.823 - - - <0.001
BIA/BCM
 Lin et al. [28] (2021) DXA (AWGS) BCM - 94.1 98.8 -
 Male 0.945
 Female 0.940
 Laksmi et al. [29] (2019) DXA (AWGS) Tanita MC-780MA 0.729 - 79.2 66.7 -
 Male 0.767 <6.9 kg/m2 70.6 82.8
 Female 0.914 <5.0 kg/m2 85.7 97.0
 Kawakami et al. [30] (2022) DXA (AWGS) BIA -
 Male 0.920 7.7 kg/m2 87.0 83.0
 Female 0.890 6.1 kg/m2 84.0 80.0
FFMI -
 Male 0.950 17.5 89.0 88.0
 Female 0.910 14.6 80.0 86.0

AUC, area under the curve; BIA, bioimpedance analysis; AWGS, Asian Working Group for Sarcopenia; aSMI, appendicular skeletal muscle index; CSA, cross-sectional area; SWE, shear-wave elastography; EWGSOP, European Working Group on Sarcopenia in Older People; DXA, dual-energy x-ray absorptiometry; T4MI, T4 muscle index (T4CSA [CSA of pectoralis, intercostalis, paraspinal, serratus, and latissimus muscles] divided by height2); PMI, pectoralis muscle index; ROC, receiver operating characteristic; PSMI, paraspinal muscle area/height index; PDFF, proton density fat fraction; BMI, body mass index; GLIM, Global Leadership Initiative on Malnutrition; BCM, body composition monitor; FFMI, fat-free mass index.

aNot significant.

Table 3.
Correlations of various modalities
Study Correlation score Correlation coefficient
Ultrasound and BIA/CT
 Bahsi et al. [14] (2021) VFT (ultrasound) and SMI (BIA): r=0.356, P<0.001 Pearson, Spearman
SFT (ultrasound) and SMI (BIA): r=0.267, P=0.006
 Hashida et al. [18] (2022) Muscle mass (ultrasound) and psoas muscle mass (CT) Pearson
 Quadriceps thickness: r=0.546, P<0.001
 Biceps thickness: r=0.730, P<0.001
 Suprahyoid CSA: r=0.579, P<0.001
 Hida et al. [15] (2018) TMT (ultrasound) and aSMI (BIA): r=0.38, P<0.001 Pearson
Male: r=0.25, P<0.001
Female: r=0.44, P<0.0001
Controlling for sex with partial correlation analysis: r=0.35, P<0.000
 Rao et al. [16] (2022) TMT (ultrasound) and aSMI (BIA): r=0.402, P<0.001 Pearson
RFT (ultrasound) and aSMI (BIA): r=0.438, P<0.001
CT and BIA
 Cao et al. [23] (2022) CT-derived BIA and BIA: R2=0.897, P<0.0001 Pearson
Male: R2=0.790, 95% CI, 0.775–0.853, P<0.0001
Female: R2=0.711, CI 0.674–0.803, P<0.0001
 Hamaguchi et al. [19] (2016) PMI (CT) and SMI (BIA): r=0.737, P< 0.001 Pearson
 Sato et al. [20] (2022) ESM (CT) and SMI (BIA): r=0.40 Pearson
PM (CT) and SMI (BIA): r=0.51
 Kim et al. [24] (2021) SMA (CT) and ASM (BIA) Pearson
 Male: r=0.725, P<0.001
 Female: r=0.659, P<0.001
 Moon et al. [21] (2022) T4CSA (CT) and aSMI (BIA): r=0.82, P<0.001 Pearson, Spearman
T4MI (CT) and aSMI (BIA): r=0.68, P<0.001
PMCSA (CT) and aSMI (BIA): r=0.72, P<0.001
PMI (CT) and aSMI (BIA): r=0.63, P<0.001
 Ohara et al. [25] (2020) PMI (CT) and SMI (BIA) Spearman
 Male: r=0.367, P<0.01
 Female: r=0.382, P<0.01
 Tan et al. [22] (2021) SMA (CT) and BSM (BIA): r=0.90, P<0.001 Pearson
MRI and DXA/BMI
 Heo et al. [26] (2020) Fat degeneration (MRI) and SMI (DXA): r=–0.22, P=0.08 Pearson
 Nakamura et al. [27] (2022) PSMI (MRI) and BMI Spearman
 Male: r=0.477, P<0.001
 Female r=0.534, P<0.001
PDFF (MRI) and BMI
 Male: r=0.552, P<0.001
 Female: r=0.651, P<0.001
BIA/BCM and DXA
 Lin et al. [28] (2021) BCM and DXA Pearson
 Development: r=0.956, P<0.001
 Validation: r=0.953, P<0.001
 Cheng et al. [31] (2021) aSMI (BIA) and DXA Pearson
 InBody 120a: r2=0.867, P<0.05
 InBody 720a: r2=0.893, P<0.05
 Wang et al. [32] (2016) BIA and DXA: r2=0.892, P<0.001 Pearson
 Luengpradidgun et al. [33] (2022) BIA and CT: r=0.54, P=0.002 Spearman
 Kawakami et al. [30] (2022) FFMI (BIA) and DXA: r=0.95 Pearson
 Alkahtani [34] (2017) BIA and DXA Cohen kappa
 Inbodyb: κ=0.80, P<0.001
 Tanitac: κ=0.61, P<0.001

BIA, bioimpedance analysis; CT, computed tomography; VFT, visceral fat thickness; SMI, skeletal muscle index; SFT, subcutaneous fat thicknesses; CSA, cross-sectional area; TMT, thigh muscle thickness; aSMI, appendicular skeletal muscle index; RFT, rectus femoris thickness; PMI, pectoralis muscle index; ESM, erector spinae muscle; PM, pectoralis muscle; SMA, skeletal muscle area; ASM, appendicular skeletal muscle mass; T4CSA, cross-sectional area of pectoralis, intercostalis, paraspinal, serratus, and latissimus muscles; T4MI, T4 muscle index (T4CSA divided by height2); PMCSA, cross-sectional area of pectoralis muscles; BSM, whole-body skeletal muscle mass; MRI, magnetic resonance imaging; DXA, dual-energy x-ray absorptiometry; BMI, body mass index; PSMI, paraspinal muscle area/height index; PDFF, proton density fat fraction; BCM, body composition monitor; FFMI, fat-free mass index.

aInBody Co.

bInbody 770 (InBody Co).

cTanita MC-980MA (Tanita Corp).

Table 4.
Advantages and disadvantages of various modalities in the detection of sarcopenia
Modality Advantage Disadvantage
Ultrasound Cheap Operator-dependent
No ionizing radiation No validated cutoff values
Quick to perform Limited use in patients with obesity
Enables evaluation of muscle quantity and quality
CT Objective Expensive
Routinely used in the diagnosis and staging of cancers Exposes patient to ionizing radiation
Requires additional interpretation
MRI Objective Expensive
No ionizing radiation Long duration
Limited availability
BIA Validated cutoff values Affected by hydration status and oedema
Portable Not widely available
Cheap
Enables evaluation of muscle quantity
DXA Validated cutoff values Affected by metallic implants and obesity
Lower radiation compared to CT Affected by hydration status and oedema
Fast Not widely available in developing countries
Enables evaluation of muscle quantity

CT, computed tomography; MRI, magnetic resonance imaging; BIA, bioelectrical impedance analysis; DXA, dual-energy x-ray absorptiometry.

Table 5.
Results for diagnostic modalities
Modality AUC Sensitivity (%) Specificity (%) Comment
Ultrasound 0.767 (0.709–0.806)a 81.1 (0.744–0.846)a 73.1 (0.648–0.774)a Highest sensitivity
CT 0.720 54.0 92.0 Highest specificity
MRI 0.839 67.0 66.0 -
BIA 0.905 (0.842–0.968)a 80.7 (0.129–0.679)a 82.4 (0.191–0.633)a Most accurate

AUC, area under the curve; CT, computed tomography; MRI, magnetic resonance imaging; BIA, bioelectrical impedance analysis.

a95% Confidence interval.

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      Diagnostic performance of various radiological modalities in the detection of sarcopenia within Asian populations: a systematic review
      Image
      Fig. 1. Process of study selection in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

      Fig. 1.

      Diagnostic performance of various radiological modalities in the detection of sarcopenia within Asian populations: a systematic review
      Study Country Study design No. of participants Imaging modality evaluated
      Lin et al. [28] (2021) Taiwan Case-control 263 BCM
      Cheng et al. [31] (2021) Hong Kong Cross-sectional 1,587 BIA
      Wang et al. [32] (2016) China Cross-sectional 944 BIA
      Laksmi et al. [29] (2019) Indonesia Cross-sectional 120 BIA
      Luengpradidgun et al. [33] (2022) Thai Cross-sectional 50 BIA
      Alkahtani [34] (2017) Saudi Arabia Cross-sectional 232 BIA
      Kawakami et al. [30] (2022) Japan Cross-sectional 1,313 BIA (FFMI)
      Cao et al. [23] (2022) China Cross-sectional 606 CT
      Hamaguchi et al. [19] (2016) Japan Cross-sectional 541 CT
      Sato et al. [20] (2022) Japan Cross-sectional 38 CT
      Kim et al. [24] (2021) Korea Cross-sectional 11,845 CT
      Moon et al. [21] (2022) Korea Cross-sectional 335 CT
      Ohara et al. [25] (2020) Japan Cross-sectional 504 CT
      Tan et al. [22] (2021) China Cross-sectional 569 CT
      Nakamura et al. [27] (2022) Japan Cross-sectional 512 MRI
      Heo et al. [26] (2020) Korea Cross-sectional 62 MRI
      Isaka et al. [10] (2019) Japan Cross-sectional 60 Ultrasonography
      Sri-On et al. [11] (2022) Thailand Cross-sectional 1,001 Ultrasonography
      Deng et al. [12] (2022) China Case-control 75 Ultrasonography
      Deng et al. [13] (2022) China Cross-sectional 235 Ultrasonography
      Bahsi et al. [14] (2021) Turkish Cross-sectional 103 Ultrasonography
      Hashida et al. [18] (2022) Japan Cohort 68 Ultrasonography
      Rao et al. [16] (2022) India Cross-sectional 117 Ultrasonography
      Hida et al. [15] (2018) Japan Cross-sectional 201 Ultrasonography
      Wang et al. [17] (2023) China Cross-sectional 130 Ultrasonography
      Study Diagnosis of sarcopenia Muscle parameter AUC Cutoff Sensitivity (%) Specificity (%) P-value
      Ultrasonography
       Isaka et al. [10] (2019) BIA (AWGS) Prediction of aSMI - -
       Tibialis anterior thickness 0.820 14.4 mm <0.01
       Gastrocnemius thickness 0.480 14.4 mm 0.49a
       Soleus thickness 0.760 22.9 mm 0.48a
      Prediction of low grip strength - -
       Tibialis anterior echo intensity 0.680 59.0 <0.01
       Gastrocnemius echo intensity 0.740 40.7 0.01a
       Soleus echo intensity 0.540 63.2 0.15
       Sri-On et al. [11] (2022) BIA (AWGS) Rectus femoris thickness -
       Sarcopenia 0.920 ≤11 mm 90.9 92.2
       Severe sarcopenia 0.950 ≤10 mm 92.5 97.4
      Male - -
      Female - -
       Deng et al. [12] (2022) BIA (AWGS) Rectus femoris thickness 0.802 - - - <0.001
      Rectus femoris CSA 0.816 - - - <0.001
      SWE mean 0.863 - - - <0.001
       Deng et al. [13] (2022) BIA (AWGS) Rectus femoris thickness
       Male 0.839 52.2 mm 76.4 75.0 <0.001
       Female 0.805 49.9 mm 89.5 60.0 <0.001
      Rectus femoris CSA
       Male 0.872 6.805 cm2 90.9 67.9 <0.001
       Female 0.791 6.996 cm2 73.7 86.7 <0.001
       Bahsi et al. [14] (2021) BIA (EWGSOP) Umbilicus visceral fat thickness
       Male 0.921 ≤22.57 mm 89.0 80.0 0.003
       Female 0.834 ≤24.97 mm 92.0 70.0 <0.001
       Hida et al. [15] (2018) BIA (AWGS) Thigh muscle thickness -
       Male 0.710 <36 mm 72.0 73.9
       Female 0.740 <34 mm 72.2 72.4
       Rao et al. [16] (2022) BIA (AWGS) Thigh muscle thickness
       Male 0.748 19 mm 70.0 70.0 <0.001
       Female 0.594 17 mm 70.0 55.0 0.10
      Rectus femoris thickness - -
       Male 0.749 76.0 64.0 <0.001
       Female 0.628 70.0 55.0 0.09
       Wang et al. [17] (2023) DXA (AWGS) Stiffness -
       Tibialis anterior 0.856 3.02 m/sec 84.6 81.5
       Gastrocnemius (medial) 0.864 2.26 m/sec 83.1 81.5
       Soleus 0.722 3.33 m/sec 86.2 59.5
      Computed tomography
       Hamaguchi et al. [19] (2016) BIA L3 psoas - - - -
       Male 6.36 cm2/m2
       Female 3.92 cm2/m2
       Sato et al. [20] (2022) BIA (AWGS) T12 erector spinae CSA -
       Sarcopenia 0.720 9.41 cm2/m2 54.0 92.0
       Severe sarcopenia 0.770 8.43 cm2/m2 75.0 94.0
      Pectoralis CSA - - - - -
       Moon et al. [21] (2022) BIA (EWGSOP) T4 pectoralis, intercostalis, paraspinal, serratus, and latissimus muscle CSA - - - -
       Male 100.06 cm2
       Female 66.93 cm2
      T4MI - - - -
       Male 33.69 cm2/m2
       Female 26.01 cm2/m2
      Pectoralis muscle CSA - - - -
       Male 29.00 cm2
       Female 18.29 cm2
      PMI - - - -
       Male 10.17 cm2/m2
       Female 7.31 cm2/m2
       Tan et al. [22] (2021) BIA (EWGSOP2) T12 erector spinae, latissimus dorsi, rectus abdominis, obliquus externus, internus abdominis, and internal and external intercostal muscle CSA - - - -
       Male <75.67 cm2
       Female <40.39 cm2
      Magnetic resonance imaging
       Heo et al. [26] (2020) DXA (AWGS) L3 paravertebral - 0.319 67.0 66.0 0.01
       Nakamura et al. [27] (2022) ROC analysis of PSMI and PDFF for low BMI (GLIM criteria) PSMI (level of the superior mesenteric artery) - - <0.001
       Male 0.906 <12.62 cm2/m2
       Female 0.771 <9.77 cm2/m2
      PDFF 0.823 - - - <0.001
      BIA/BCM
       Lin et al. [28] (2021) DXA (AWGS) BCM - 94.1 98.8 -
       Male 0.945
       Female 0.940
       Laksmi et al. [29] (2019) DXA (AWGS) Tanita MC-780MA 0.729 - 79.2 66.7 -
       Male 0.767 <6.9 kg/m2 70.6 82.8
       Female 0.914 <5.0 kg/m2 85.7 97.0
       Kawakami et al. [30] (2022) DXA (AWGS) BIA -
       Male 0.920 7.7 kg/m2 87.0 83.0
       Female 0.890 6.1 kg/m2 84.0 80.0
      FFMI -
       Male 0.950 17.5 89.0 88.0
       Female 0.910 14.6 80.0 86.0
      Study Correlation score Correlation coefficient
      Ultrasound and BIA/CT
       Bahsi et al. [14] (2021) VFT (ultrasound) and SMI (BIA): r=0.356, P<0.001 Pearson, Spearman
      SFT (ultrasound) and SMI (BIA): r=0.267, P=0.006
       Hashida et al. [18] (2022) Muscle mass (ultrasound) and psoas muscle mass (CT) Pearson
       Quadriceps thickness: r=0.546, P<0.001
       Biceps thickness: r=0.730, P<0.001
       Suprahyoid CSA: r=0.579, P<0.001
       Hida et al. [15] (2018) TMT (ultrasound) and aSMI (BIA): r=0.38, P<0.001 Pearson
      Male: r=0.25, P<0.001
      Female: r=0.44, P<0.0001
      Controlling for sex with partial correlation analysis: r=0.35, P<0.000
       Rao et al. [16] (2022) TMT (ultrasound) and aSMI (BIA): r=0.402, P<0.001 Pearson
      RFT (ultrasound) and aSMI (BIA): r=0.438, P<0.001
      CT and BIA
       Cao et al. [23] (2022) CT-derived BIA and BIA: R2=0.897, P<0.0001 Pearson
      Male: R2=0.790, 95% CI, 0.775–0.853, P<0.0001
      Female: R2=0.711, CI 0.674–0.803, P<0.0001
       Hamaguchi et al. [19] (2016) PMI (CT) and SMI (BIA): r=0.737, P< 0.001 Pearson
       Sato et al. [20] (2022) ESM (CT) and SMI (BIA): r=0.40 Pearson
      PM (CT) and SMI (BIA): r=0.51
       Kim et al. [24] (2021) SMA (CT) and ASM (BIA) Pearson
       Male: r=0.725, P<0.001
       Female: r=0.659, P<0.001
       Moon et al. [21] (2022) T4CSA (CT) and aSMI (BIA): r=0.82, P<0.001 Pearson, Spearman
      T4MI (CT) and aSMI (BIA): r=0.68, P<0.001
      PMCSA (CT) and aSMI (BIA): r=0.72, P<0.001
      PMI (CT) and aSMI (BIA): r=0.63, P<0.001
       Ohara et al. [25] (2020) PMI (CT) and SMI (BIA) Spearman
       Male: r=0.367, P<0.01
       Female: r=0.382, P<0.01
       Tan et al. [22] (2021) SMA (CT) and BSM (BIA): r=0.90, P<0.001 Pearson
      MRI and DXA/BMI
       Heo et al. [26] (2020) Fat degeneration (MRI) and SMI (DXA): r=–0.22, P=0.08 Pearson
       Nakamura et al. [27] (2022) PSMI (MRI) and BMI Spearman
       Male: r=0.477, P<0.001
       Female r=0.534, P<0.001
      PDFF (MRI) and BMI
       Male: r=0.552, P<0.001
       Female: r=0.651, P<0.001
      BIA/BCM and DXA
       Lin et al. [28] (2021) BCM and DXA Pearson
       Development: r=0.956, P<0.001
       Validation: r=0.953, P<0.001
       Cheng et al. [31] (2021) aSMI (BIA) and DXA Pearson
       InBody 120a: r2=0.867, P<0.05
       InBody 720a: r2=0.893, P<0.05
       Wang et al. [32] (2016) BIA and DXA: r2=0.892, P<0.001 Pearson
       Luengpradidgun et al. [33] (2022) BIA and CT: r=0.54, P=0.002 Spearman
       Kawakami et al. [30] (2022) FFMI (BIA) and DXA: r=0.95 Pearson
       Alkahtani [34] (2017) BIA and DXA Cohen kappa
       Inbodyb: κ=0.80, P<0.001
       Tanitac: κ=0.61, P<0.001
      Modality Advantage Disadvantage
      Ultrasound Cheap Operator-dependent
      No ionizing radiation No validated cutoff values
      Quick to perform Limited use in patients with obesity
      Enables evaluation of muscle quantity and quality
      CT Objective Expensive
      Routinely used in the diagnosis and staging of cancers Exposes patient to ionizing radiation
      Requires additional interpretation
      MRI Objective Expensive
      No ionizing radiation Long duration
      Limited availability
      BIA Validated cutoff values Affected by hydration status and oedema
      Portable Not widely available
      Cheap
      Enables evaluation of muscle quantity
      DXA Validated cutoff values Affected by metallic implants and obesity
      Lower radiation compared to CT Affected by hydration status and oedema
      Fast Not widely available in developing countries
      Enables evaluation of muscle quantity
      Modality AUC Sensitivity (%) Specificity (%) Comment
      Ultrasound 0.767 (0.709–0.806)a 81.1 (0.744–0.846)a 73.1 (0.648–0.774)a Highest sensitivity
      CT 0.720 54.0 92.0 Highest specificity
      MRI 0.839 67.0 66.0 -
      BIA 0.905 (0.842–0.968)a 80.7 (0.129–0.679)a 82.4 (0.191–0.633)a Most accurate
      Table 1. Summary of study characteristics

      BCM, body composition monitor; BIA, bioimpedance analysis; FFMI, fat-free mass index; CT, computed tomography; MRI, magnetic resonance imaging.

      Table 2. Diagnostic accuracy of various modalities in the detection of sarcopenia

      AUC, area under the curve; BIA, bioimpedance analysis; AWGS, Asian Working Group for Sarcopenia; aSMI, appendicular skeletal muscle index; CSA, cross-sectional area; SWE, shear-wave elastography; EWGSOP, European Working Group on Sarcopenia in Older People; DXA, dual-energy x-ray absorptiometry; T4MI, T4 muscle index (T4CSA [CSA of pectoralis, intercostalis, paraspinal, serratus, and latissimus muscles] divided by height2); PMI, pectoralis muscle index; ROC, receiver operating characteristic; PSMI, paraspinal muscle area/height index; PDFF, proton density fat fraction; BMI, body mass index; GLIM, Global Leadership Initiative on Malnutrition; BCM, body composition monitor; FFMI, fat-free mass index.

      Not significant.

      Table 3. Correlations of various modalities

      BIA, bioimpedance analysis; CT, computed tomography; VFT, visceral fat thickness; SMI, skeletal muscle index; SFT, subcutaneous fat thicknesses; CSA, cross-sectional area; TMT, thigh muscle thickness; aSMI, appendicular skeletal muscle index; RFT, rectus femoris thickness; PMI, pectoralis muscle index; ESM, erector spinae muscle; PM, pectoralis muscle; SMA, skeletal muscle area; ASM, appendicular skeletal muscle mass; T4CSA, cross-sectional area of pectoralis, intercostalis, paraspinal, serratus, and latissimus muscles; T4MI, T4 muscle index (T4CSA divided by height2); PMCSA, cross-sectional area of pectoralis muscles; BSM, whole-body skeletal muscle mass; MRI, magnetic resonance imaging; DXA, dual-energy x-ray absorptiometry; BMI, body mass index; PSMI, paraspinal muscle area/height index; PDFF, proton density fat fraction; BCM, body composition monitor; FFMI, fat-free mass index.

      InBody Co.

      Inbody 770 (InBody Co).

      Tanita MC-980MA (Tanita Corp).

      Table 4. Advantages and disadvantages of various modalities in the detection of sarcopenia

      CT, computed tomography; MRI, magnetic resonance imaging; BIA, bioelectrical impedance analysis; DXA, dual-energy x-ray absorptiometry.

      Table 5. Results for diagnostic modalities

      AUC, area under the curve; CT, computed tomography; MRI, magnetic resonance imaging; BIA, bioelectrical impedance analysis.

      95% Confidence interval.

      Table 1.

      Table 2.

      Table 3.

      Table 4.

      Table 5.

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