Abstract
Background
Evidence from recent years suggests that bioelectrical impedance analysis (BIA), or BIA-based assessments of muscle mass, are strongly correlated with magnetic resonance imaging (MRI)-based assessments of muscle mass. Nevertheless, no research has examined how the two approaches relate to kids with high-risk neuroblastoma (HR-NBL). This study examined the clinical relevance of BIA-based and MRI-based muscle mass assessment. Before surgery, we computed the relationship between muscle mass measurements made using BIA and MRI in HR-NBL patients.
Methods
We retrospectively collected data for patients aged 3 to 18 years who were newly diagnosed with HR-NBL at the Children’s Hospital, Zhejiang University School of Medicine from November 2023 to November 2024. L4 lumbar levels were identified on axial MRI images, and we measured skeletal muscle cross-sectional area. The Multi-Frequency Body Composition Analyzer InBody S10 (Biospace Co., Ltd., Seoul, Korea) was used to estimate skeletal muscle mass (SMM). The analysis included 32 children.
Results
The median patient age was 4.8 years (range, 4.03–6.2 years); 56.3% of the patients were boys, and 43.7% were girls. SMM measured by BIA showed a strong positive correlation with MRI-measured SMM in patients (r=0.956, P<0.001). The concordance correlation coefficient for SMM was 0.862, with a 95% confidence interval (CI) of 0.799–0.905. The SMM showed a mean bias of 0.319±1.326 kg, indicating that the BIA method overestimated SMM by 0.319 kg compared to MRI. The Bland-Altman analysis suggests that most participants were within the limits of agreement (LoA). Multiple linear regression analysis revealed that weight was a significant predictor of the outcome variable in the models tested. In the multiple regression model, weight (estimate =0.229, P<0.001) was significantly associated with the outcome, and the model explained 78.8% of the variance (R2=0.788).
Conclusions
In conclusion, in monitoring muscle mass in pediatric HR-NBL patients, BIA measurements showed good correlation and agreement with MRI measurements. The multiple linear regression analysis shows that weight is a significant predictor of the difference in muscle mass in human body composition.
Keywords: Neuroblastoma, skeletal muscle mass (SMM), bioelectrical impedance analysis (BIA), magnetic resonance imaging (MRI)
Highlight box.
Key findings
• Therapeutic advances have transformed the prognosis for high-risk neuroblastoma (HR-NBL) into a curable disease.
• Skeletal muscle plays a crucial role in metabolism and inflammatory responses. In children with cancer, sarcopenia is a common finding that negatively impacts their prognosis.
• Computed tomography and magnetic resonance imaging (MRI) are considered the gold standard for measuring skeletal muscle mass and intramuscular fat due to their ability to identify muscle tissue boundaries.
What is known and what is new?
• Evidence indicates that MRI-based assessments of muscle mass strongly correlated with bioelectrical impedance analysis (BIA) results.
• This study displays that the fast, noninvasive, and repeatable BIA measurements had a strong correlation and a good agreement with MRI measurements in monitoring muscle mass in pediatric HR-NBL patients.
What is the implication, and what should change now?
• BIA may replace MRI as a method for monitoring muscle mass in pediatric HR-NBL patients.
• Because BIA measurement is fast, noninvasive, and repeatable for monitoring muscle mass, it enables the timely detection of patients with low muscle mass and allows for early intervention.
Introduction
Neuroblastoma is the most common extracranial solid tumor in children, typically originating from neural crest cells within the sympathetic nervous system. The disease can present as either localized or widely disseminated and may occasionally undergo spontaneous regression (1). Surgical intervention is employed for tumors classified as low- or intermediate-risk, with or without postoperative chemotherapy. Survival rates vary by risk group: over 95% in the low-risk group and approximately 50% in the high-risk group (2). However, over the past few decades, 3-year survival durations for patients with high-risk disease have increased by more than 60% due to consistent improvements in the efficacy of multimodal treatments studied in clinical trials (3,4). The prognosis for high-risk neuroblastoma (HR-NBL) has improved due to therapeutic advancements.
A pathological condition known as “sarcopenia” is characterized by a decline in the amount, quality, and strength of skeletal muscle mass (SMM) (5). Skeletal muscle plays a vital role in metabolism and inflammatory responses. This recent clinical article reviews the interplay between malnutrition and sarcopenia in pediatric oncology, indicating that muscle mass loss is frequently observed in children undergoing treatment for malignancy and may adversely impact outcomes (6). Standardized diagnostic criteria in this population are still lacking, but emerging research underscores the prognostic relevance of muscle mass loss, particularly in conditions such as leukemia and solid tumors. Rayar et al. found that SMM in pediatric patients with acute lymphoblastic leukemia (ALL) significantly decreased during early treatment, with the degree of reduction correlated with hospitalization duration (7). Suzuki et al. and Itonaga et al. observed a significant decrease in total psoas muscle area (tPMA) following induction therapy (8,9). Furthermore, Suzuki et al. identified sarcopenia as an independent prognostic factor for invasive fungal infections (8). Sarcopenia is closely related to decreased muscle mass, which can be evaluated using computed tomography (CT) and magnetic resonance imaging (MRI), both of which enable precise quantification of skeletal muscle volume (10). Other methods of measuring muscle mass are dual-energy X-ray absorptiometry (DXA), ultrasound, and bioelectrical impedance analysis (BIA). It is interesting to note that BIA has gained acceptance as a reasonably priced, noninvasive, and easy-to-use portable technique for assessing body composition, including muscle mass (11). Recent studies demonstrate that MRI-based quantification of SMM correlates strongly with BIA measurements, supporting BIA as a practical surrogate for muscle mass assessment in both adult and pediatric populations. For example, a population-based cohort study reported that MRI-based and BIA-based measures of muscle mass were strongly correlated (12). Additionally, pediatric imaging studies have shown significant associations between MRI muscle parameters and BIA variables (13). Recently, a study in children with HR-NBL showed that tPMA z-score decreased significantly following a treatment regimen that included induction chemotherapy, tumor resection surgery (14). However, no studies have examined the relationship between the two methods in children with HR-NBL.
To investigate the clinical value of BIA-based muscle mass assessment, we examined the relationship between BIA- and MRI-based muscle mass in patients with HR-NBL before operation. We would advocate for BIA replacing MRI as a method of monitoring muscle mass. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-729/rc).
Methods
Participants
We retrospectively reviewed 32 patients aged 3 to 18 years who were newly diagnosed with HR-NBL at the Children’s Hospital, Zhejiang University School of Medicine from November 2023 to November 2024. These patients completed induction therapy and underwent BIA and MRI examinations simultaneously before the operation. We collected their clinical data, including MRI and BIA data. The diagnosis and treatment of HR-NBL were based on the (Chinese Children’s Cancer Group-Neuroblastoma) CCCG-NB-2021 Regimen (15).
This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Children’s Hospital, Zhejiang University School of Medicine (No. 2024-IRB-0399-P-01), and because it was a retrospective study, informed consent was not required.
Anthropometric measurements and BIA-based SMM measurement
Body weight, height, and body composition were measured by BIA as part of a nutritional assessment by trained dietitians during a standard evaluation in the outpatient clinic. The World Health Organization Anthro Plus software (www.who.int/en/) was used to calculate the height-for-age Z score (HAZ) and the weight-for-age Z score (WAZ). The definition of pediatric malnutrition was a body mass index (BMI)-for-age z-score ≤−1 (16). Each subject’s body composition was measured after a 12-hour fast using an InBody S10 MF-BIA device (Biospace Co. Ltd., Seoul, Korea) in the morning. The patients had not previously received any nutritional therapy. Moreover, the BIA examination was completed within 1 week of the MRI scan. The device was used to assess SMM in the trunk, arms, and legs. Extracellular water (ECW) ratios, defined as the proportion of ECW to total body water (TBW), were categorized into two groups: absence of edema for ratios ≤0.390, and presence of edema for ratios >0.390.
Assessment of SMM using MRI imaging
A radiologist with over 10 years of experience measures the processing of MRI images. Axial MRI images at the mid-L4 vertebral level were used to assess skeletal muscle area (SMA). The mid-L4 level was identified as the axial slice exhibiting the most significant prominence of both transverse processes. All subjects underwent scanning using a T1-weighted gradient-echo sequence with a repetition time of 10 milliseconds, an echo time of 4.5 milliseconds, a field of view of 40 centimeters, and a 256×256 matrix. The protocol involved acquiring approximately forty axial images with a slice thickness of 5 mm, with the subjects positioned supine. MRI images at the L4 vertebral level, acquired using a SOMATOM Force dual-source scanner (Siemens, Germany), were analyzed with post-processing software (Syngo via) to determine the cross-sectional area of skeletal muscle (cm2). The muscle groups selected for SMA assessment comprised the paraspinal muscles, psoas major, quadratus lumborum, external and internal obliques, transversus abdominis, and rectus abdominis (Figure 1).
Figure 1.
The measurement of SMA from an MRI scan. (A) SMA measured at axial MRI images at the mid-L4 vertebral level. (B) SMA was measured as the paraspinal muscles [1], quadratus lumborum [2], internal oblique [3], external oblique [6], rectus abdominis [4], transverse abdominal muscles [5], psoas major [7,8], including both the right and left sides (surrounded by a dotted line). MRI, magnetic resonance imaging; SMA, skeletal muscle area.
Skeletal muscle volume (L) was estimated utilizing the regression equation proposed by Shen et al. [0.166 × L4 area (cm2) + 2.142] (17). Subsequently, SMM derived from MRI was calculated by multiplying the estimated skeletal muscle volume by 1.06 kg/L, corresponding to the density of mammalian skeletal muscle tissue.
Statistical analysis
Continuous variables were reported as mean ± standard deviation (SD) when normally distributed and analyzed using t-tests; otherwise, they were presented as median (interquartile range) and evaluated with nonparametric tests. Differences between BIA and MRI measurements were assessed using paired sample tests. Comparisons of these parameters were conducted using the Wilcoxon rank-sum test or Wilcoxon signed-rank test, as appropriate. Spearman’s correlation coefficient was used to examine the association between SMM, as measured by BIA and MRI. Lin’s concordance correlation coefficients (CCCs) were calculated to assess the accuracy and precision of SMM estimates by BIA compared to those obtained by MRI. The CCC values were categorized as poor (<0.50), moderate (0.50 to 0.69), substantial (0.70 to 0.89), and almost perfect (0.90 to 1.0) (18). Bland-Altman analyses were performed to evaluate agreement and potential bias between methods for all body composition measures. To evaluate proportional bias, univariate and multiple linear regression analysis was performed for each variable, with the mean difference between models as the dependent variable.
Continuous variables were checked for multicollinearity using the variance inflation factor (VIF) (0.1< VIF <3.0). All assumptions for linear regression were met, including independence of residuals, homoscedasticity, normality of residuals, and no multicollinearity. Thus, linear regression analyses with various approaches were conducted to identify the best predictors of SMM. Regression methods included manual insertion, automated analysis (backward and forward), and stepwise addition and removal. Age, sex, weight, height, and edema were included as their potential relationship with skeletal muscle.
Data collection was performed in Microsoft Excel, and graphical representations were generated using GraphPad Prism (version 9.3.1). Statistical analyses were conducted with IBM SPSS software (version 26.0). A P value less than 0.05 was regarded as indicative of statistical significance.
Results
Patient characteristics
The study included 32 pediatric participants. Table 1 displays the demographic details of the group. The patients had a median age of 4.8 years, ranging from 4.03 to 6.2 years. Males accounted for 56.3% of the group, while females accounted for 43.7%. The median WAZ was −0.72 (interquartile range, −1.12 to −0.18), and the median HAZ was −0.38 (interquartile range, −0.87 to 0.13). Among patients with HR-NBL, 25 children (78.13%) had histology classified accordingly. The retroperitoneum was the most common site of the primary tumor, seen in 15 cases (40.6%). Among these patients, 11 (34.4%) were malnourished, and 23 (71.9%) had edema. The median SMM measured by BIA was 8.92 kg (interquartile range, 7.64 to 10.51 kg). The mean SMA and SMM measured by MRI were 38.359 cm2 and 9.019 kg, respectively.
Table 1. Patient and characteristics (n=32).
| Characteristics | Values |
|---|---|
| Sex | |
| Boys | 18 (56.3) |
| Girls | 14 (43.7) |
| Age (years) | 4.8 [4.03, 6.2] |
| Weight for age Z score | −0.72 [−1.12, −0.18] |
| Height for age Z score | −0.38 [−0.87, 0.13] |
| Malnutrition | 11 (34.4) |
| Disease characteristics | |
| Histology | |
| Neuroblastoma | 25 (78.13) |
| Ganglioneuroblastoma | 7 (21.87) |
| Primary tumor site | |
| Adrenal gland | 14 (43.8) |
| Retroperitoneal | 15 (46.9) |
| Mediastinum | 3 (9.4) |
| BIA data | |
| SMM (kg) | 8.92 [7.64, 10.51] |
| Edema | 23 (71.9) |
| ECW/TBW | 0.395 [0.390, 0.398] |
| MRI data | |
| SMA (cm2) | 38.359±11.210 |
| SMM (kg) | 9.019±1.972 |
Data are presented as n (%), median [interquartile range] or mean ± standard deviation. BIA, bioelectrical impedance analysis; ECW, extracellular water; MRI, magnetic resonance imaging; SMA, skeletal muscle area; SMM, skeletal muscle mass; TBW, total body water.
Test the difference and correlation coefficient between SMM from BIA and MRI in subgroup analysis
We compared SMM measurements obtained from MRI and BIA across different patient subgroups, including sex, nutritional status, and edema status (Table 2). No significant differences were observed between MRI and BIA in measuring SMM across these subgroups. Specifically, there were no statistically significant differences between sexes (female vs. male), nutritional status (no malnutrition vs. malnutrition), or edema status (no edema vs. edema) when comparing MRI and BIA (P>0.05 for all comparisons). Additionally, the overall comparison of total SMM showed no significant difference between the two methods (P=0.74). These results suggest that MRI and BIA provide comparable measurements of SMM across various demographic and clinical conditions. Strong positive correlations were observed between MRI and BIA measurements of SMM across subgroups (Figure 2). In the sex comparison, significant correlations were found for both females (r=0.942, P<0.001) and males (r=0.997, P<0.001). Regarding edema status, the correlation was also significant, stronger in individuals with edema (r=0.946, P<0.001) than in those without edema (r=0.900, P=0.002). Similarly, in the nutritional status subgroup, high correlations were observed for both individuals with malnutrition (r=0.955, P<0.001) and those without malnutrition (r=0.936, P<0.001).
Table 2. Test of difference of SMM between MRI and BIA in different groups.
| SMM | P value |
|---|---|
| Female vs. male | |
| BIA | 0.83 |
| MRI | 0.82 |
| No malnutrition vs. malnutrition | |
| BIA | 0.39 |
| MRI | 0.34 |
| No edema vs. edema | |
| BIA | 0.56 |
| MRI | 0.92 |
| Total (BIA vs. MRI) | 0.74 |
| Female (BIA vs. MRI) | 0.71 |
| Male (BIA vs. MRI) | 0.41 |
| No malnutrition (BIA vs. MRI) | 0.72 |
| Malnutrition (BIA vs. MRI) | 0.60 |
| No edema (BIA vs. MRI) | 0.55 |
| Edema (BIA vs. MRI) | 0.94 |
BIA, bioelectrical impedance analysis; MRI, magnetic resonance imaging; SMM, skeletal muscle mass.
Figure 2.
Correlation coefficient between SMM from BIA and MRI measurements in subgroup analysis. (A) Correlation coefficient of SMM in male and female groups. (B) Correlation coefficient of SMM in edema and no edema groups. (C) Correlation coefficient of SMM in malnutrition and no malnutrition groups. BIA, bioelectrical impedance analysis; MRI, magnetic resonance imaging; SMM, skeletal muscle mass.
Assess the agreement between SMM measurements from BIA and MRI
Table 3 presents the agreement between the SMM measurements obtained from BIA and MRI. The correlation coefficient for SMM (BIA vs. MRI) was 0.956, indicating a stronger positive relationship between the two methods. The CCC for SMM was 0.862, with a 95% confidence interval (CI) of 0.799–0.905. The SMM showed a mean bias of 0.319±1.326 kg, indicating that the BIA method overestimated SMM by 0.319 kg compared to MRI, with 95% limits of agreement (LoA) ranging from −2.281 to 2.918 kg. Two data points fell outside the 95% LoA range, including one from a severely obese pediatric patient (Figure 3). Overall, BIA overestimated SMM by approximately 3.5% (mean difference =0.319 kg).
Table 3. Agreement between the measurement results of SMM.
| Differential value | Correlation coefficient | Mean difference (95% LoA) | CCC (95% CI) |
|---|---|---|---|
| SMM_BIA-SMM_MRI | 0.956 | 0.319 (−2.281, 2.918) | 0.862 (0.799, 0.905) |
BIA, bioelectrical impedance analysis; CCC, concordance correlation coefficient; CI, confidence interval; LoA, limits of agreement; MRI, magnetic resonance imaging; SMM_BIA, skeletal muscle mass from BIA measurement; SMM_MRI, skeletal muscle mass from MRI measurement.
Figure 3.
Correlation coefficient and Bland-Altman plots between SMM from BIA and MRI measurements. (A) Correlation coefficient between SMM from BIA and MRI measurements. (B) Bland-Altman plots between SMM from BIA and MRI measurements. BIA, bioelectrical impedance analysis; MRI, magnetic resonance imaging; SMM, skeletal muscle mass.
Univariate and multiple linear regression analyses
In the univariate analysis, significant predictors included age (estimate =0.455, P<0.001), height (estimate =0.077, P<0.001), and weight (estimate =0.217, P<0.001). The variables sex (P=0.63) and edema (P=0.86) were not significantly associated with the difference between BIA and MRI measurements (Table 4). Multiple linear regression analysis revealed that weight was a significant predictor of the outcome variable in the models tested (Table 4). In the multiple regression model, weight (estimate =0.229, P<0.001) was significantly associated with the outcome, accounting for 78.8% of the variance (Table 4).
Table 4. Univariate and multiple linear regression analysis of the difference between the BIA and MRI measurements.
| Analysis | Variable | Estimate | 95% CI lower | 95% CI upper | P value | R2 |
|---|---|---|---|---|---|---|
| Univariate linear regression | Sex (M vs. F) | 0.234 | −0.743 | 1.211 | 0.63 | – |
| Age | 0.455 | 0.214 | 0.700 | <0.001 | ||
| Edema (yes vs. no) | −0.098 | −1.180 | 0.984 | 0.86 | ||
| Height | 0.016 | 0.044 | 0.109 | <0.001 | ||
| Weight | 0.023 | 0.171 | 0.263 | <0.001 | ||
| Multiple linear regression | Intercept | −3.340 | −4.268 | −2.411 | <0.001 | 0.788 |
| Sex (M vs. F) | 0.124 | −0.361 | 0.609 | 0.60 | ||
| Age | −0.028 | −0.231 | 0.174 | 0.78 | ||
| Edema (yes vs. no) | −0.527 | −1.070 | 0.016 | 0.057 | ||
| Weight | 0.229 | 0.165 | 0.293 | <0.001 |
Due to maximum collinearity in height, it was manually excluded. BIA, bioelectrical impedance analysis; CI, confidence interval; F, female; M, male; MRI, magnetic resonance imaging.
Discussion
At present, a range of imaging modalities, including ultrasound, CT, MRI, DXA, and BIA, are utilized to evaluate muscle mass in pediatric populations (19). CT and MRI are regarded as the gold standard techniques for assessing SMM and intramuscular fat, owing to their capacity to delineate muscle tissue boundaries (20) precisely. Owing to substantial radiation exposure, elevated costs, and operational complexity, these methods are restricted in pediatric populations. Conversely, BIA can be employed in a wide range of settings without the necessity for highly specialized personnel, offering a noninvasive, rapid, cost-effective, and user-friendly alternative. Recent research has increasingly focused on comparing SMM measurements obtained via BIA with those derived from DXA, CT, and MRI. In pediatric patients with spinal muscular atrophy, BIA assessments have demonstrated strong concordance with DXA measurements (21). In children with obesity and nonalcoholic fatty liver disease, BIA measures of muscle and fat mass correlate strongly with MRI measures of tPMA and fat areas (22).
To our knowledge, this study is one of the few that compare BIA and MRI in measuring SMM in pediatric cancer patients. In the present study, the SMM of MRI is estimated using the muscle area at the L4 vertebral level measured by MRI, applying the regression equation proposed by Shen et al. (17). The MRI-derived and BIA-derived SMM demonstrated a strong linear association and high agreement, as evidenced by a correlation coefficient of 0.956 and a CCC of 0.862 (95% CI: 0.799–0.905). BIA underestimated SMM by approximately 3.5% (mean difference =0.319 kg). The 95% LoA ranged from −2.281 to 2.918; two data points fell outside the 95% LoA. In a study of 507 Korean adults aged 50–70 years with a BMI range of 20–30 kg/m2, a BIA device (InBody770®) overestimated SMM by 1.97 kg compared with DXA (23). Another study of gastric cancer patients overestimated skeletal muscle index (SMI) by 1.18 kg compared to CT measurements (24). When comparing SMM data derived from BIA with data obtained from DXA, CT, and MRI, the error values are smallest with MRI. These findings suggest that BIA can provide muscle mass estimates that are broadly consistent with MRI measurements in these populations. These results are concordant with prior research indicating significant correlations between MRI-based and BIA-based muscle mass measures. For example, the results of Kiefer et al. demonstrate a prediction error of muscle mass by BIA in obesity, with considerably stronger correlations between BIA-based measurements and normal values in individuals (12). However, we also identified one case of a severely obese patient whose measured data fell outside the 95% LoA interval. As noted by Kiefer et al. (12), BIA tends to show stronger correlations with reference measurements in individuals with typical body composition. In contrast, its prediction error increases in obese patients due to altered body fat distribution, hydration status, and other factors that may affect electrical impedance measurements.
As mentioned above, muscle quality may be influenced by hydration status, nutrition, and sex; therefore, we conducted a subgroup analysis. Furthermore, we compared SMM measurements obtained from MRI and BIA across different patient subgroups, including sex, nutritional status, and edema status. No significant differences were observed between MRI and BIA in measuring SMM across these subgroups. The results indicate that both MRI and BIA show a high correlation in measuring SMM across different demographic and clinical conditions, with robust correlations observed in males, individuals with edema, and those with malnutrition. The stronger correlation in males may be attributed to gender-specific differences in body composition, with men typically having higher muscle mass, which makes impedance measurements more accurate. The total amounts of intracellular water (ICW) and ECW can be quantified by BIA, thereby providing a more comprehensive estimate of TBW and enabling more accurate predictions of SMM (25). Obese people show greater relative expansion of ECW than ICW, and the ECW/ICW ratio is highly variable in people with higher BMI, whereas ECW/ICW is more consistent at lower BMI values (26). Although 23% of the patients in our study had edema, the median value was 0.395. This data indicates that the child’s edema is not severe. In our study, both MRI and BIA effectively detected muscle loss in individuals with edema or malnutrition. Furthermore, in the multiple regression model, weight (estimate =0.229, P<0.001) was significantly associated with the outcome, accounting for 78.8% of the variance. The result implies that weight is a meaningful predictor of the outcome and that a change in weight may have a substantial impact on the outcome variable. The positive coefficient suggests that an increase in weight would be associated with a corresponding increase in the outcome.
While MRI scans have conventionally been used as a standard diagnostic tool for tumor evaluation, their primary limitations include substantial financial costs, considerable time requirements, and the need for patients to travel to the imaging facility and undergo exposure to contrast agents. Additionally, radiologists must calculate SMA to utilize this method. SMA calibration from a single MRI image may be inaccurate, leading to measurement errors. BIA is not as accurate as MRI for measuring body composition. Nonetheless, the guidelines established by the Asian Working Group for Sarcopenia specify that the critical cutoff values for muscle mass assessment via BIA are 5.7 kg/m2 for females and 7.0 kg/m2 for males (27). However, no established critical values currently exist for children, representing a key focus for our future research. Reference values are available for assessing muscle function in children aged 4 years and older and for SMM in Chinese children and adolescents aged 3 to 17 years (28,29). Children with tumors experience a significant reduction in SMM during treatment and its effects, which impacts disease prognosis (6-9,12). Future research should consider a prospective cohort design to further validate our findings, particularly through same-day BIA and imaging techniques (CT/MRI) for evaluating the consistency and reliability of muscle mass measurements. Additionally, future studies should incorporate muscle strength evaluations, such as handgrip strength tests or sit-to-stand time tests, to better elucidate the relationship between muscle mass and functionality. Furthermore, incorporating sarcopenia-related outcomes (e.g., infection rates, hospitalization) will offer a more comprehensive understanding of the long-term impact of muscle mass on health outcomes. These improvements in study design will provide more substantial evidence of the clinical utility of muscle mass assessment tools.
To the best of our knowledge, this study is the first to establish a correlation and concordance between MRI and BIA measurements of muscle mass in pediatric cancer patients. However, we acknowledge several limitations of this study. Data availability and consistency were limited due to the retrospective design, small sample size, and single-institution setting. Additionally, BIA and MRI examinations were not performed on the same day, which may have allowed for changes in muscle mass. Moreover, muscle strength, a critical parameter for diagnosing sarcopenia, was not evaluated in this study.
Conclusions
BIA measurements of muscle mass in pediatric HR-NBL patients showed good correlation and agreement with MRI measurements. The multiple linear regression analysis shows that weight is a significant predictor of muscle mass in human body composition.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Children’s Hospital, Zhejiang University School of Medicine (No. 2024-IRB-0399-P-01), and because it was a retrospective study, informed consent was not required.
Footnotes
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-729/rc
Funding: None.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-729/coif). The authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-aw-729/dss
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