Visceral adipose tissue 18F-FDG uptake and CT-derived body composition variables for predicting progression-free survival in patients with intermediate- and high-risk neuroblastoma

Visceral adipose tissue 18F-FDG uptake and CT-derived body composition variables for predicting progression-free survival in patients with intermediate- and high-risk neuroblastoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Visceral adipose tissue 18F-FDG uptake and CT-derived body composition variables for predicting progression-free survival in patients with intermediate- and high-risk neuroblastoma Bingyan Zhang, Siqi Li, Ying Kan, Wei Wang, Jigang Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9386029/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objective This study aimed to investigate the impact of body composition and fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT)-based glucose metabolism variables (primarily visceral adipose tissue, VAT) on progression-free survival (PFS) in children with intermediate- to high-risk neuroblastoma. Methods A retrospective study was conducted on 102 pediatric neuroblastoma patients who underwent baseline 18 F-FDG PET/CT scans. Clinical, demographic, and survival data were collected. PET/CT assessments included mean standardized uptake value (SUV mean ) of VAT and skeletal muscle, along with body composition measurements: subcutaneous adipose tissue (SAT) area, SAT radiodensity, VAT area, VAT radiodensity, skeletal muscle area, and skeletal muscle radiodensity. Skeletal muscle index (SMI) was calculated by normalizing skeletal muscle area (SM area) to patient height. Patients were stratified into high- and low-VAT uptake subgroups based on optimal VAT SUV mean cutoff values. Univariate and multivariate regression analyses, along with Kaplan-Meier survival analysis, evaluated associations between metabolic activity, CT-derived body composition variables, and PFS. Results Among 102 patients with a mean age of 3.51 ± 2.38 years, 45 (44.1%) experienced tumor progression during follow-up. Multivariate Cox proportional hazards regression analysis identified International Neuroblastoma Risk Group (INRG) (hazard ratio [HR] 3.367, p < 0.05), VAT SUV mean (HR 3.998, p < 0.05), and VAT radiodensity (HR 1.055, p < 0.05) as adverse prognostic factors for PFS. Notably, patients with elevated VAT uptake demonstrated significantly poorer progression-free survival compared to those with low uptake (p < 0.001). Conclusion Increased 18 F-FDG uptake and increased VAT radiodensity in intermediate- to high-risk neuroblastoma patients correlate with shorter progression-free survival, highlighting their potential as biomarkers for poor outcomes. neuroblastoma pediatric FDG PET/CT Visceral adipose tissue Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Neuroblastoma (NB) is the most common extracranial solid tumor in children. Originating from abnormally differentiated neural crest cells during development, it can arise anywhere along the sympathetic nervous system, most commonly in the adrenal glands[ 1 ]. Neuroblastoma patients may present with widespread metastases or be confined to a single anatomical site. The most frequent sites of metastasis are bone marrow and cortical bone, followed by regional or distant lymph nodes and the liver. Patients can be categorized into distinct risk groups with varying prognoses based on factors such as age at diagnosis, staging, pathological characteristics, and genetic features[ 2 ]. Intermediate- and high-risk patients exhibit poor outcomes and face a high risk of malnutrition [ 3 ]. Adipose tissue consists of various cell types, including mature adipocytes, pre-adipocytes, macrophages, and fibroblasts [ 4 ]. Under inflammatory and hypoxic conditions, dysfunctional adipocytes promote cancer cell progression, invasion, and metastasis through multiple mechanisms, such as secreting various adipokines, maintaining a pro-inflammatory microenvironment, stimulating angiogenesis, and supplying energy to tumor cells [ 5 , 6 ]. Adipocyte browning refers to the complex process whereby white adipocytes transform into beige or brown adipocytes under certain conditions, representing a key metabolic adaptation in AT associated with enhanced glucose utilization and mitochondrial biogenesis [ 7 – 9 ].Research confirms that brown adipocytes promote neuroblastoma cell growth and migration [ 10 ] .Several studies have explored the significance of adipose tissue (AT) composition, including subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in malignancies. Their depletion may correlate with poorer survival rates [ 11 ].Compared with SAT, VAT is a more reliable indicator of the overall status of human adipose tissue and has been shown to be superior in predicting cancer outcomes [ 12 , 13 ]. The prognostic value of VAT has also been validated in several metabolic diseases [ 14 , 15 ]. 18 F-FDG PET/CT is a routine examination for NB patients and can provide human component-related parameters without increasing costs or risks[ 16 ].Due to its ability to visualize cellular glucose metabolism, 18 F-FDG PET/CT is often used in the clinical diagnosis, staging, restaging and efficacy assessment of NB patients[ 17 ].Previous studies have shown that 18 F-FDG PET/CT semi-quantitative parameters (including standard uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) are valuable for NB risk[ 18 ]. In recent years, abnormal body composition characteristics (such as high visceral fat accumulation and sarcopenia) have attracted increasing attention as pre-treatment risk factors associated with prognosis. It is worth noting that previous studies have shown that SUV mean is more suitable for measuring lipid metabolic activity than SUV max , as SUV max is prone to interference from the gastrointestinal tract, and SUV mean offers better reproducibility than SUV max [ 19 ]. Most prior studies evaluated glucose metabolism parameters in adult malignancies. For example, in various types of malignant tumors in adults, including pancreatic cancer, colorectal cancer, and breast cancer, higher 18 F-FDG uptake in adipose tissue has been found to be associated with poor patient prognosis[ 20 – 22 ]. However, data regarding its role in intermediate- and high-risk pediatric neuroblastoma remain scarce. Investigating the relationship between body composition parameters and neuroblastoma is crucial as it may provide valuable insights into prognostic factors and aid in developing tailored therapeutic strategies for this aggressive tumor type. Given the association between body composition parameters and survival outcomes in neuroblastoma, this study aims to evaluate the prognostic value of visceral adipose tissue (VAT) 18 F-FDG uptake and CT-derived body composition parameters for progression-free survival (PFS) in patients with intermediate-to-high-risk neuroblastoma. Methods Patients A cohort of 102 pediatric patients with intermediate-to-high-risk neuroblastoma enrolled at Beijing Friendship Hospital, Capital Medical University, from January 2018 to December 2025. This retrospective study was approved by our hospital’s ethics committee, and informed consent was waived. Inclusion criteria were as follows: (i) Availability of baseline PET/CT images at our institution; (ii) INRG risk classification of intermediate-high risk[ 23 ]; (iii) Children with pathologically confirmed neuroblastoma[ 24 ]. Exclusion criteria were: (i) Surgery or chemotherapy prior to PET/CT scanning; (ii) Suspected rheumatic/immune disorders or other genetic metabolic diseases; (iii) Loss to follow-up. Follow-up assessments were conducted via telephone consultation or medical records. Figure 1 illustrates the patient selection flowchart. PET/CT Scanning 18 F-FDG PET/CT imaging was performed using a Biograph mCT64 PET/CT (Siemens, Knoxville, TN). Patients fasted for at least 6 hours prior to the examination. Intravenous administration of 18 F-FDG (3.7 MBq/kg) was performed after confirming blood glucose levels below 11.1 mmol/L. Following an approximately 60-minute rest period, low-dose attenuation-corrected CT scans were acquired using the following parameters: 100kV, auto-mA, and 3mm collimation. Immediately thereafter, whole-body PET scans were performed head-to-toe per bed, with the head region scanned for 5 minutes and the remaining regions for 2.5 minutes. Reconstructed images were obtained using an ordered subset expectation maximization algorithm with time-of-flight. Image Analysis Images were evaluated by three experienced nuclear medicine physicians at a workstation (syngo Multimodality Workplace, Siemens) to derive conventional semi-quantitative metabolic parameters: SUV mean . SUV mean values for Adipose tissue (AT) and skeletal muscle (erector spinae and psoas major) were derived from the mean uptake values on the left and right sides. Clinical information and follow-up Clinical characteristics and pretreatment laboratory parameters were recorded at the initial admission for PET/CT scanning: patients’ age, sex, body mass index, INRG risk classification, PFS, Neuron-Specific Enolase (NSE), Serum Ferritin (SF), Lactate Dehydrogenase (LDH), and Vanillylmandelic Acid (VMA). PFS is defined as the time from the date of the baseline PET scan to tumor progression or death from any cause; for patients who are alive and have not progressed, the endpoint is the date of the last follow-up. Body composition measurements SAT was defined as extraperitoneal fat tissue between skin and muscle, VAT was defined as intraabdominal fat tissue and skeletal muscle was defined as psoas major and erector spinae. Cross-sectional areas of skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were delineated by a trained investigator using Slice-O-Matic 5.0 on images at the mid-L3 vertebral level, as shown in Fig. 2 .The threshold for skeletal muscle was defined as -29 to + 150 Hounsfield Units (HU), visceral fat as -150 to -50 HU, and subcutaneous fat as -190 to -30 HU. Automatic segmentation based on these thresholds yielded the SAT area, VAT area, and skeletal muscle area at the mid-level of the L3 vertebral body. The mean HU values of skeletal muscle, SAT, and VAT were defined as skeletal muscle radiodensity, SAT radiodensity, and VAT radiodensity, respectively. SMI (Skeletal Muscle Index) is calculated by dividing skeletal muscle area by the square of height (m 2 ). VSR (Visceral to Subcutaneous Ratio) is calculated by dividing VAT area by SAT area. FMR (Fat to Muscle Ratio) is calculated by dividing total fat area by skeletal muscle area. Statistical analysis We assessed the normality of distribution for each body composition parameter using the Shapiro-Wilk test. Continuous variables with normal distribution were expressed as mean ± standard deviation, while non-normally distributed continuous variables were presented as median (interquartile range). Statistical evaluations were performed using independent samples t-tests or Mann-Whitney U tests, respectively. Categorical variables were reported as percentages and assessed using chi-square or Fisher's exact tests. Progression-free survival (PFS) was calculated in months, starting from the baseline PET scan date and ending at the date of tumor progression or last follow-up. The follow-up cutoff date was December 2025. To identify independent prognostic factors, univariate Cox regression analysis was first performed to screen for potentially significant variables (P < 0.05), which were then incorporated into a multivariate Cox proportional hazards model for further validation. Risk effects were expressed as hazard ratios (HR) with 95% confidence intervals (CI). Survival curves were plotted using the Kaplan-Meier method, and log-rank tests were performed to compare survival differences between groups. The optimal cut-off value for VAT SUV mean predicting tumor progression risk was determined via Receiver operating characteristic (ROC) curve analysis, dividing patients into high-uptake and low-uptake groups. Statistical analyses were primarily performed using SPSS 27.0 and GraphPad Prism 10 software. All hypothesis tests employed two-sided significance testing, with P < 0.05 indicating statistically significant differences. Results Patient characteristics Ultimately, 102 pediatric neuroblastoma patients met the inclusion criteria, comprising 48 males and 54 females. The age range across the entire dataset was 1 to 10 years, with a median age of 3 years. The PFS duration ranged from 1 to 92 months, with a median (interquartile range) of 73.0 (18.0, 87.0) months. Among the 102 patients, 45 (44.1%) experienced disease progression during follow-up, with time to progression ranging from 1 to 74 months (median, 30.5 months), while the remaining 57 (55.9%) patients remained progression-free, with follow-up duration ranging from 18 to 92 months (median, 86 months). Additionally, 35 patients (34.3%) exhibited elevated VAT SUV mean , whereas 67 patients (65.6%) demonstrated low VAT SUV mean . Patient characteristics for the high-uptake and low-uptake groups are summarized in Table 1 . Table 1 Patient characteristics with high and low VAT uptake Total Low VAT uptake High VAT uptake P value n = 102 n = 67 n = 35 Sex # 0.291 Male 48 (47.06) 29 (43.28) 19 (54.29) Female 54 (52.94) 38 (56.72) 16 (45.71) Age (years) 3.00 (2.00, 4.25) 3.00 (2.00, 5.00) 3.00 (1.00, 4.00) 0.650 BMI (kg/m²) 15.44 (14.42, 16.60) 15.43 (14.18, 16.79) 15.51 (14.74, 16.53) 0.885 INRG # 0.234 Intermediate 30 (29.41) 22 (32.84) 8 (22.86) High 72 (70.59) 45 (67.16) 27 (77.14) VMA 428.27 (37.97, 573.19) 473.72 (29.51, 605.26) 348.72 (52.89, 369.99) 0.986 NSE (µg/L) 267.45 (94.75, 651.50) 237.50 (86.20, 649.00) 291.30 (122.00, 750.00) 0.530 SF (µg/L) 139.95 (69.15, 335.73) 131.20 (68.18, 352.85) 189.85 (67.98, 311.75) 0.722 LDH (U/L) 594.50 (358.00, 1125.00) 587.00 (339.00, 1131.00) 629.50 (404.00, 1131.70) 0.483 SM SUV mean 0.55 (0.49, 0.62) 0.54 ± 0.08 0.60 (0.53, 0.69) < 0.001* SAT area (cm²) 16.00 (7.58, 24.94) 15.36 (7.12, 25.53) 16.52 (9.54, 24.74) 0.566 VAT area (cm²) 8.44 (5.52, 10.82) 8.45 (5.53, 11.29) 8.39 ± 3.39 0.786 SM area (cm²) 12.15 (9.68, 15.80) 13.19 ± 5.34 10.95 (9.72, 13.76) 0.259 SAT radiodensity (HU) -75.81 ± 11.35 -76.61 ± 12.36 -72.83 (-81.98, -67.65) 0.313 VAT radiodensity (HU) -84.81 (-87.45, -81.17) -85.61 (-87.92, -81.69) -83.22 ± 5.86 0.137 SM radiodensity (HU) 54.66 (52.13, 57.14) 55.46 (52.74, 57.78) 53.66 (50.69, 55.10) 0.005* SMI (cm²/m²) 13.19 ± 3.14 13.23 ± 2.91 13.13 ± 3.57 0.888 FMR 1.90 (1.23, 3.05) 1.82 (1.15, 3.09) 2.23 (1.36, 3.04) 0.255 V/S ratio 0.51 (0.35, 0.78) 0.51 (0.36, 0.84) 0.50 (0.29, 0.76) 0.413 Independent t-test was used for continuous variables. Data are presented as mean ± SD. Fishers exact test when comparing categorical variables. Mann-Whitney-U test, values presented as median and range BMI: Body mass index; INRG: International Neuroblastoma Risk Group; VMA: Vanillylmandelic Acid; NSE: Neuron-Specific Enolase; SF: Serum Ferritin; LDH: Lactate Dehydrogenase; SM: Skeletal muscle; SUV: Standardized Uptake Value; SAT: Subcutaneous adipose tissue; VAT: Visceral adipose tissue; SMI: Skeletal muscle index; V/S ratio: Visceral to Subcutaneous adipose tissue area ratio; *:p<0.05; # : n (%) Survival analysis The optimal cutoff value for VAT SUV mean was 0.61, dividing all patients into high-uptake and low-uptake groups. Concurrently, a cutoff value of -85.93 HU was used for VAT radiodensity. Multivariate analysis revealed that INRG risk stratification (HR 3.367, 95% CI 1.291–8.780, P = 0.013), VAT radiodensity (HR 1.055, 95% CI 1.000–1.112, P = 0.049), and VAT SUV mean (HR 3.998, 95% CI 1.244–12.852, P = 0.020) were independent prognostic factors affecting PFS, as shown in Table 2 . Table 2 Univariate and multivariate analysis for Progression-Free Survival Characteristics Univariate analysis Hazard ratio (95% CI) p value Multivariate analysis Hazard ratio (95% CI) p value Age (years) 1.160 (1.039–1.295) 0.008* Sex 1.625 (0.901–2.933) 0.107 SAT area (cm²) 1.002 (0.986–1.018) 0.808 VAT area (cm²) 0.996 (0.946–1.049) 0.888 SM area (cm²) 1.035 (0.981–1.092) 0.207 SAT radiodensity (HU) 1.006 (0.979–1.035) 0.664 VAT radiodensity (HU) 1.069 (1.016–1.125) 0.010* 1.055 (1.000–1.112) 0.049* SM radiodensity (HU) 0.969 (0.925–1.015) 0.188 SMI 1.013 (0.921–1.114) 0.788 BMI 0.992 (0.878–1.119) 0.891 VSR 0.799 (0.500–1.276) 0.347 FMR 0.965 (0.823–1.132) 0.661 SAT SUV mean 1.016 (0.986–1.047) 0.290 VAT SUV mean 5.493 (1.822–16.565) 0.002* 3.998 (1.244–12.852) 0.020* SM SUV mean 3.362 (0.865–13.064) 0.080 INRG Risk Groups 4.248 (1.673–10.788) 0.002* 3.367 (1.291–8.780) 0.013* NSE 1.000 (1.000–1.001) 0.060 SF LDH 1.001 (1.000–1.001) 1.000 (1.000–1.001) 0.144 0.474 *: p<0.05 The VAT SUV mean level was below 0.61 in 67 patients, with 1-year, 3-year, and 5-year PFS rates of 91.0% (95% CI: 84.1%-97.9%), 76.0% (95% CI: 65.8%-86.2%), and 69.9% (95% CI: 58.9%-80.9%), respectively. In contrast, among 35 patients with VAT SUV mean ≥0.61, the 1-year, 3-year, and 5-year PFS rates were 82.9% (95% CI: 70.4%-95.4%), 42.9% (95% CI: 26.4%-59.4%), and 31.4% (95% CI: 16.1%-46.7%), respectively. Compared with the low uptake group, elevated VAT SUV mean levels were significantly associated with poorer PFS (P < 0.001). Kaplan-Meier survival analysis revealed that patients in the low VAT uptake and low VAT density groups demonstrated superior prognosis and longer PFS compared to those in the high VAT uptake and high VAT density groups. See Fig. 3 for details. ROC Curve analysis Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of each indicator for progression-free survival (PFS) in children with intermediate- or high-risk neuroblastoma. The area under the curve (AUC) and its 95% confidence interval (CI) were calculated. The AUCs for the predictive probabilities of VAT SUV mean , VAT radiodensity, INRG risk group, and a combined logistic regression model incorporating all three factors were calculated and compared to assess their predictive value. ROC curve analysis revealed that the AUCs for predicting PFS in intermediate- or high-risk neuroblastoma patients using VAT SUV mean , VAT radiodensity, and INRG risk group classification were 0.637 (95% CI: 0.527–0.748), 0.613 (95% CI: 0.504–0.722), and 0.664 (95% CI: 0.559–0.769), respectively. Combining these three factors into a logistic regression model increased the AUC of the combined predictive probability to 0.758 (95% CI: 0.665–0.850), significantly higher than any single indicator (Fig. 4 ). Discussion It is the first study to confirm the relationship between 18 F-FDG uptake levels in visceral adipose tissue (VAT SUV mean ) and CT radiodensity, and progression-free survival (PFS) in patients with intermediate-to-high-risk neuroblastoma. We found that elevated VAT SUV mean and high VAT radiodensity indicate shorter PFS, suggesting that the functional state of visceral adipose tissue may serve as a potential novel prognostic biomarker for this patient population. Adipose tissue not only serves as a lipid storage and secretory organ but also plays crucial roles in metabolism, immunity, and other functions[ 25 ]. Human adipose tissue is primarily categorized into white adipose tissue (WAT) and brown adipose tissue (BAT), each exhibiting distinct morphological, functional, and metabolic characteristics. In patients with various wasting diseases, including cancer, WAT browning is commonly observed, leading to WAT atrophy. This process precedes skeletal muscle wasting and represents an early hallmark of cachexia[ 26 ].The potential mechanisms by which white adipose tissue browning induces cancer-related cachexia are as follows: First, during browning, expression of its specific marker, uncoupling protein-1 (UCP-1), significantly increases, thereby activating the thermogenic program in adipocytes. This leads to substantial mobilization of lipid reserves and increased energy expenditure. Second, multiple inflammatory mediators, including IL-6 and TNF-α, exhibit heightened expression under chronic inflammatory conditions. This has been demonstrated to induce UCP-1 transcriptional activation, a mechanism closely linked to WAT browning and enhanced lipolysis during cancer-related weight loss. Finally, in various cancer types, the paraneoplastic factor parathyroid hormone-related protein (PTH-R1) increases, [ 27 , 28 ] leading to adipose tissue browning. This browning process enhances energy expenditure[ 29 ]. Prolonged high metabolic drive in cancer patients induces adaptive brownification of adipose tissue, intensifying lipolysis and accelerating energy expenditure. This manifests as enhanced glucose uptake capacity and active glucose metabolism within adipose tissue. This study observed a significant association between elevated VAT glucose uptake and reduced progression-free survival in patients with advanced neuroblastoma, indirectly confirming the presence of WAT browning in this population. ¹⁸F-FDG PET/CT is widely used for tumor metabolic assessment, with SUV mean values effectively reflecting tissue glucose uptake levels [ 30 ]. FDG uptake in adipose tissue simultaneously captures glucose metabolism and inflammatory status. In this study, the finding that elevated VAT SUV mean predicts poorer prognosis aligns with conclusions from studies in colorectal, gastric, and pancreatic cancers[ 22 , 31 , 32 ]. Potential mechanisms may involve upregulation of pro-inflammatory cytokine expression and increased macrophage infiltration within VAT, where this heightened inflammatory state may promote tumor progression [ 33 , 34 ].Furthermore, VAT exhibits heightened sensitivity to inflammation-induced lipolysis and insulin resistance [ 35 , 36 ]. Inflammatory cytokines such as IL-6 and TNF-α secreted by visceral adipocytes induce local and systemic inflammatory states, thereby directly or indirectly stimulating cancer growth[ 37 ]. However, previous studies evaluating the relationship between FDG uptake in VAT and SAT and clinical outcomes have yielded conflicting results. For instance, in a cohort of newly diagnosed pancreatic cancer patients presenting with lymph node involvement and positive metastasis, SAT glucose metabolism was significantly reduced compared to patients without lymph node metastasis. Conversely, VAT exhibits higher glucose metabolic activity than SAT, potentially linked to increased hexokinase-1 expression. [ 38 ]A study on metastatic colorectal cancer reported improved survival in patients with high VAT SUV levels during bevacizumab therapy. [ 39 ]As an explanation, it has been demonstrated that increased VEGF release correlates with pro-inflammatory cytokines and inflammation, and is released at higher levels in visceral adipose tissue, thereby leading to elevated VAT SUV and influencing response to bevacizumab treatment. These inconsistencies may stem from cancer heterogeneity, varying sample sizes, adipose tissue specificity (VAT vs. SAT), or population differences. However, previous studies have primarily focused on adult malignancies such as colorectal, pancreatic, and gastric cancers [ 22 , 31 , 32 , 39 ]. This study evaluated the relationship between glucose metabolism in visceral adipose tissue and prognosis in children with advanced neuroblastoma, suggesting that alterations in VAT glucose metabolism may serve as a potential prognostic biomarker for these patients. Notably, our study also found that patients with high VAT radiodensity exhibited poorer PFS compared to those with low VAT radiodensity. We propose two potential mechanisms to explain the association between increased radiodensity and higher mortality: inflammation and adipose tissue browning. First, increased radiodensity may reflect heightened local inflammation, which correlates with elevated mortality risk. For instance, a study of 40 cardiac surgery patients found that those with higher adipose tissue radiodensity also exhibited increased 18 F-FDG uptake on PET/CT scans, indicating heightened inflammatory activity[ 40 ]. Second, researchers suggest that in cachectic patients, elevated radiodensity may indicate adipose tissue browning. This transformation signifies enhanced lipid metabolism and increased energy expenditure, accelerating catabolism and thereby elevating mortality[ 41 ]. A breast cancer study demonstrated that higher fat density correlates with greater vascular distribution, stronger inflammatory responses, and lipid depletion. Their combined effects may contribute to poorer survival rates following cancer diagnosis[ 42 ]. Animal biopsy studies have shown that elevated SAT radiodensity may be associated with lower lipid content in adipocytes and increased extracellular matrix fibrosis, indicating heightened lipolysis within adipose tissue[ 43 ]. In this study, both VAT radiodensity and its glucose metabolic activity emerged as independent predictors of PFS. Collectively, these findings reflect pathological alterations in adipose tissue—including lipid depletion, fibrosis, and inflammatory activation—suggesting that adipose dysfunction may contribute to tumor progression and adverse outcomes through shared pathological pathways. Certain limitations of the current study must be acknowledged. First, as a single-center retrospective study, selection bias is unavoidable. Second, it should be noted that single-time-point body composition assessment may poorly correlate with dynamic changes over time [ 44 ], suggesting that serial PET/CT scans may be required in future studies to better elucidate the relationship between adipose metabolic activity and tumor progression. Third, no clear consensus exists regarding cutoff values for VAT radiodensity applicable to Asian children. Binary classification using cutoff values for VAT SUV mean and VAT radiodensity may pose potential issues. Future multicenter studies could establish specific cutoff values suitable for pediatric oncology populations. Conclusion In summary, we found that high VAT metabolic activity and/or high VAT radiodensity are associated with shorter progression-free survival in children with advanced-stage neuroblastoma. Integrating adipose tissue biopsy with PET/CT imaging over time may prove crucial for future studies investigating the relationship between tumor progression and fat metabolism. Declarations Author Contribution Z.B.Y. and L.S.Q. contributed equally to this work. All authors conceptualized and designed the study. Z.B.Y. and L.S.Q. performed data curation and statistical analysis. K.Y. and W.W. conducted the PET/CT image acquisition and interpretation. Z.B.Y. and L.S.Q. wrote the main manuscript text. Z.B.Y. prepared tables and figures. Y.J.G. revised the manuscript for English language and content. All authors reviewed and approved the final manuscript. 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Vitam Horm, 2022. 120: p. 179–214. Pitarresi, J.R., et al., PTHrP Drives Pancreatic Cancer Growth and Metastasis and Reveals a New Therapeutic Vulnerability . Cancer Discov, 2021. 11(7): p. 1774–1791. He, Y., et al., The browning of white adipose tissue and body weight loss in primary hyperparathyroidism . EBioMedicine, 2019. 40: p. 56–66. Tan, H., et al., Preoperative Body Composition Combined with Tumor Metabolism Analysis by PET/CT Is Associated with Disease-Free Survival in Patients with NSCLC. Contrast Media Mol Imaging, 2022. 2022: p. 7429319. Lee, J.W., S.M. Lee, and Y.A. Chung, Prognostic value of CT attenuation and FDG uptake of adipose tissue in patients with pancreatic adenocarcinoma. Clin Radiol, 2018. 73(12): p. 1056.e1-1056.e10. Lee, J.W., et al., Significance of CT attenuation and F-18 fluorodeoxyglucose uptake of visceral adipose tissue for predicting survival in gastric cancer patients after curative surgical resection . Gastric Cancer, 2020. 23(2): p. 273–284. Lee, J.W., et al., Visceral adipose tissue volume and CT-attenuation as prognostic factors in patients with head and neck cancer . Head Neck, 2019. 41(6): p. 1605–1614. Lee, J.W., et al., Effect of adipose tissue volume on prognosis in patients with non-small cell lung cancer . Clin Imaging, 2018. 50: p. 308–313. Arner, P., Differences in lipolysis between human subcutaneous and omental adipose tissues . Ann Med, 1995. 27(4): p. 435–8. Verboven, K., et al., Abdominal subcutaneous and visceral adipocyte size, lipolysis and inflammation relate to insulin resistance in male obese humans . Sci Rep, 2018. 8(1): p. 4677. Donohoe, C.L., et al., The role of obesity in gastrointestinal cancer: evidence and opinion . Therap Adv Gastroenterol, 2014. 7(1): p. 38–50. Van de Wiele, C., et al., Metabolic and morphological measurements of subcutaneous and visceral fat and their relationship with disease stage and overall survival in newly diagnosed pancreatic adenocarcinoma: Metabolic and morphological fat measurements in pancreatic adenocarcinoma . Eur J Nucl Med Mol Imaging, 2017. 44(1): p. 110–116. Karaçelik, T., et al., Prognostic Significance of Adipose Tissue Distribution and Metabolic Activity in PET/CT in Patients with Metastatic Colorectal Cancer . J Gastrointest Cancer, 2023. 54(2): p. 456–466. Antonopoulos, A.S., et al., Detecting human coronary inflammation by imaging perivascular fat . Sci Transl Med, 2017. 9(398). Petruzzelli, M., et al., A switch from white to brown fat increases energy expenditure in cancer-associated cachexia . Cell Metab, 2014. 20(3): p. 433–47. Brown, K.A., Metabolic pathways in obesity-related breast cancer . Nat Rev Endocrinol, 2021. 17(6): p. 350–363. Murphy, R.A., et al., Adipose tissue density, a novel biomarker predicting mortality risk in older adults . J Gerontol A Biol Sci Med Sci, 2014. 69(1): p. 109–17. van Dijk, D.P.J., et al., Ectopic fat in liver and skeletal muscle is associated with shorter overall survival in patients with colorectal liver metastases . J Cachexia Sarcopenia Muscle, 2021. 12(4): p. 983–992. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 03 May, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 11 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9386029","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633717540,"identity":"0d61ad02-3063-4c63-93e5-5451620b4e56","order_by":0,"name":"Bingyan Zhang","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bingyan","middleName":"","lastName":"Zhang","suffix":""},{"id":633717541,"identity":"e110780a-0576-4153-aade-3f1d5729d39a","order_by":1,"name":"Siqi Li","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Li","suffix":""},{"id":633717543,"identity":"7c588e4f-74c3-4aa0-aa08-e888b85dc594","order_by":2,"name":"Ying Kan","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Kan","suffix":""},{"id":633717544,"identity":"b1fd72b4-3ad0-41f6-8a6e-c17d0d3137b2","order_by":3,"name":"Wei Wang","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":633717545,"identity":"55552749-e865-47a2-89ff-33e29f5729a3","order_by":4,"name":"Jigang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYLACCQMQyXwAyk0gWgsbTCkxWiCAx4A4LQbHzx5+YVFQK2/Ov+bjh585hxn42XMMGH7uwKPlTF6ahYTBccOdM95uluzddphBsueNAWPvGTxaDuSYGUgYHGPccOPsNmZGoBaDGzkGzIxteLScfwPWYr/hxplnYC32BLXcyDF+IGFQk7jhfA8bxBYJAlokb7wxAwbygeQNN9iMgX5J55E486zgYC8eLXznc4w/S/yps91w/vDDDz+3WcvxtydvfPATjxaFAwxs0hIMh4HxmQAW4AERB3BrYGCQb2Bg/viBoY6BgR+vulEwCkbBKBjJAACGdFfJKp9EggAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jigang","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-04-11 08:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9386029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9386029/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109077288,"identity":"c552654f-b7be-4886-b9eb-5b7e8c9d6a3f","added_by":"auto","created_at":"2026-05-12 11:07:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":564803,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient selection.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9386029/v1/0231ea7fc106387571722212.jpg"},{"id":109077217,"identity":"64396ef7-dff2-4746-8093-a9a9b35a5dc2","added_by":"auto","created_at":"2026-05-12 11:07:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":614493,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of PET/CT-derived segmentation of muscle and adipose tissue (A-C); PET/CT-derived delineation of volumes of interest in the SAT(D-F); PET/CT-derived delineation of volumes of interest in the VAT (G-I); Psoas muscle and erector spinae muscle (J-L).\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9386029/v1/a9c69b4a5aa9530dd53798c7.jpg"},{"id":109077421,"identity":"86ae94c1-c75c-42e6-8045-7d61fe2db9f0","added_by":"auto","created_at":"2026-05-12 11:08:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":350344,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for progression-free survival. Visceral adipose tissue uptake-related survival curve (A); Visceral adipose tissue radiodensity-related survival curve (B); INRG risk group-related survival curve (C). VAT: Visceral adipose tissue\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9386029/v1/56999c1ba383ae387514de23.jpg"},{"id":109077166,"identity":"1d5ca54d-19b9-4903-bd5d-6e1b5319b0ec","added_by":"auto","created_at":"2026-05-12 11:07:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34264,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for VAT SUV\u003csub\u003emean\u003c/sub\u003e, VAT radiodensity, INRG Risk Group and combined model in predicting progression-free survival in children with intermediate- and high-risk neuroblastoma. The areas under the ROC curve (AUC) for VAT SUV\u003csub\u003emean\u003c/sub\u003e, VAT radiodensity, and INRG Risk Group were 0.637, 0.613, 0.664 and 0.758 respectively.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9386029/v1/b0902ffe6f9dfb36e9e0b263.jpg"},{"id":109078305,"identity":"2a4bc039-f98b-4dc7-acee-307c8eb54452","added_by":"auto","created_at":"2026-05-12 11:14:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1947537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9386029/v1/36aad34b-35dd-4b07-9d4e-c06b7f7398c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Visceral adipose tissue 18F-FDG uptake and CT-derived body composition variables for predicting progression-free survival in patients with intermediate- and high-risk neuroblastoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeuroblastoma (NB) is the most common extracranial solid tumor in children. Originating from abnormally differentiated neural crest cells during development, it can arise anywhere along the sympathetic nervous system, most commonly in the adrenal glands[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Neuroblastoma patients may present with widespread metastases or be confined to a single anatomical site. The most frequent sites of metastasis are bone marrow and cortical bone, followed by regional or distant lymph nodes and the liver. Patients can be categorized into distinct risk groups with varying prognoses based on factors such as age at diagnosis, staging, pathological characteristics, and genetic features[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Intermediate- and high-risk patients exhibit poor outcomes and face a high risk of malnutrition [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdipose tissue consists of various cell types, including mature adipocytes, pre-adipocytes, macrophages, and fibroblasts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Under inflammatory and hypoxic conditions, dysfunctional adipocytes promote cancer cell progression, invasion, and metastasis through multiple mechanisms, such as secreting various adipokines, maintaining a pro-inflammatory microenvironment, stimulating angiogenesis, and supplying energy to tumor cells [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Adipocyte browning refers to the complex process whereby white adipocytes transform into beige or brown adipocytes under certain conditions, representing a key metabolic adaptation in AT associated with enhanced glucose utilization and mitochondrial biogenesis [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].Research confirms that brown adipocytes promote neuroblastoma cell growth and migration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] .Several studies have explored the significance of adipose tissue (AT) composition, including subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in malignancies. Their depletion may correlate with poorer survival rates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].Compared with SAT, VAT is a more reliable indicator of the overall status of human adipose tissue and has been shown to be superior in predicting cancer outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The prognostic value of VAT has also been validated in several metabolic diseases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT is a routine examination for NB patients and can provide human component-related parameters without increasing costs or risks[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].Due to its ability to visualize cellular glucose metabolism, \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT is often used in the clinical diagnosis, staging, restaging and efficacy assessment of NB patients[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Previous studies have shown that \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT semi-quantitative parameters (including standard uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) are valuable for NB risk[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In recent years, abnormal body composition characteristics (such as high visceral fat accumulation and sarcopenia) have attracted increasing attention as pre-treatment risk factors associated with prognosis. It is worth noting that previous studies have shown that SUV\u003csub\u003emean\u003c/sub\u003e is more suitable for measuring lipid metabolic activity than SUV\u003csub\u003emax\u003c/sub\u003e, as SUV\u003csub\u003emax\u003c/sub\u003e is prone to interference from the gastrointestinal tract, and SUV\u003csub\u003emean\u003c/sub\u003e offers better reproducibility than SUV\u003csub\u003emax\u003c/sub\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Most prior studies evaluated glucose metabolism parameters in adult malignancies. For example, in various types of malignant tumors in adults, including pancreatic cancer, colorectal cancer, and breast cancer, higher \u003csup\u003e18\u003c/sup\u003eF-FDG uptake in adipose tissue has been found to be associated with poor patient prognosis[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, data regarding its role in intermediate- and high-risk pediatric neuroblastoma remain scarce. Investigating the relationship between body composition parameters and neuroblastoma is crucial as it may provide valuable insights into prognostic factors and aid in developing tailored therapeutic strategies for this aggressive tumor type. Given the association between body composition parameters and survival outcomes in neuroblastoma, this study aims to evaluate the prognostic value of visceral adipose tissue (VAT) \u003csup\u003e18\u003c/sup\u003eF-FDG uptake and CT-derived body composition parameters for progression-free survival (PFS) in patients with intermediate-to-high-risk neuroblastoma.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA cohort of 102 pediatric patients with intermediate-to-high-risk neuroblastoma enrolled at Beijing Friendship Hospital, Capital Medical University, from January 2018 to December 2025. This retrospective study was approved by our hospital\u0026rsquo;s ethics committee, and informed consent was waived. Inclusion criteria were as follows: (i) Availability of baseline PET/CT images at our institution; (ii) INRG risk classification of intermediate-high risk[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; (iii) Children with pathologically confirmed neuroblastoma[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Exclusion criteria were: (i) Surgery or chemotherapy prior to PET/CT scanning; (ii) Suspected rheumatic/immune disorders or other genetic metabolic diseases; (iii) Loss to follow-up. Follow-up assessments were conducted via telephone consultation or medical records. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the patient selection flowchart.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePET/CT Scanning\u003c/h3\u003e\n\u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging was performed using a Biograph mCT64 PET/CT (Siemens, Knoxville, TN). Patients fasted for at least 6 hours prior to the examination. Intravenous administration of \u003csup\u003e18\u003c/sup\u003eF-FDG (3.7 MBq/kg) was performed after confirming blood glucose levels below 11.1 mmol/L. Following an approximately 60-minute rest period, low-dose attenuation-corrected CT scans were acquired using the following parameters: 100kV, auto-mA, and 3mm collimation. Immediately thereafter, whole-body PET scans were performed head-to-toe per bed, with the head region scanned for 5 minutes and the remaining regions for 2.5 minutes. Reconstructed images were obtained using an ordered subset expectation maximization algorithm with time-of-flight.\u003c/p\u003e\n\u003ch3\u003eImage Analysis\u003c/h3\u003e\n\u003cp\u003eImages were evaluated by three experienced nuclear medicine physicians at a workstation (syngo Multimodality Workplace, Siemens) to derive conventional semi-quantitative metabolic parameters: SUV\u003csub\u003emean\u003c/sub\u003e. SUV\u003csub\u003emean\u003c/sub\u003e values for Adipose tissue (AT) and skeletal muscle (erector spinae and psoas major) were derived from the mean uptake values on the left and right sides.\u003c/p\u003e\n\u003ch3\u003eClinical information and follow-up\u003c/h3\u003e\n\u003cp\u003eClinical characteristics and pretreatment laboratory parameters were recorded at the initial admission for PET/CT scanning: patients\u0026rsquo; age, sex, body mass index, INRG risk classification, PFS, Neuron-Specific Enolase (NSE), Serum Ferritin (SF), Lactate Dehydrogenase (LDH), and Vanillylmandelic Acid (VMA). PFS is defined as the time from the date of the baseline PET scan to tumor progression or death from any cause; for patients who are alive and have not progressed, the endpoint is the date of the last follow-up.\u003c/p\u003e\n\u003ch3\u003eBody composition measurements\u003c/h3\u003e\n\u003cp\u003eSAT was defined as extraperitoneal fat tissue between skin and muscle, VAT was defined as intraabdominal fat tissue and skeletal muscle was defined as psoas major and erector spinae. Cross-sectional areas of skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were delineated by a trained investigator using Slice-O-Matic 5.0 on images at the mid-L3 vertebral level, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.The threshold for skeletal muscle was defined as -29 to +\u0026thinsp;150 Hounsfield Units (HU), visceral fat as -150 to -50 HU, and subcutaneous fat as -190 to -30 HU. Automatic segmentation based on these thresholds yielded the SAT area, VAT area, and skeletal muscle area at the mid-level of the L3 vertebral body. The mean HU values of skeletal muscle, SAT, and VAT were defined as skeletal muscle radiodensity, SAT radiodensity, and VAT radiodensity, respectively. SMI (Skeletal Muscle Index) is calculated by dividing skeletal muscle area by the square of height (m\u003csup\u003e2\u003c/sup\u003e). VSR (Visceral to Subcutaneous Ratio) is calculated by dividing VAT area by SAT area. FMR (Fat to Muscle Ratio) is calculated by dividing total fat area by skeletal muscle area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe assessed the normality of distribution for each body composition parameter using the Shapiro-Wilk test. Continuous variables with normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while non-normally distributed continuous variables were presented as median (interquartile range). Statistical evaluations were performed using independent samples t-tests or Mann-Whitney U tests, respectively. Categorical variables were reported as percentages and assessed using chi-square or Fisher's exact tests.\u003c/p\u003e \u003cp\u003eProgression-free survival (PFS) was calculated in months, starting from the baseline PET scan date and ending at the date of tumor progression or last follow-up. The follow-up cutoff date was December 2025. To identify independent prognostic factors, univariate Cox regression analysis was first performed to screen for potentially significant variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which were then incorporated into a multivariate Cox proportional hazards model for further validation. Risk effects were expressed as hazard ratios (HR) with 95% confidence intervals (CI). Survival curves were plotted using the Kaplan-Meier method, and log-rank tests were performed to compare survival differences between groups.\u003c/p\u003e \u003cp\u003eThe optimal cut-off value for VAT SUV\u003csub\u003emean\u003c/sub\u003e predicting tumor progression risk was determined via Receiver operating characteristic (ROC) curve analysis, dividing patients into high-uptake and low-uptake groups. Statistical analyses were primarily performed using SPSS 27.0 and GraphPad Prism 10 software. All hypothesis tests employed two-sided significance testing, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistically significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eUltimately, 102 pediatric neuroblastoma patients met the inclusion criteria, comprising 48 males and 54 females. The age range across the entire dataset was 1 to 10 years, with a median age of 3 years. The PFS duration ranged from 1 to 92 months, with a median (interquartile range) of 73.0 (18.0, 87.0) months. Among the 102 patients, 45 (44.1%) experienced disease progression during follow-up, with time to progression ranging from 1 to 74 months (median, 30.5 months), while the remaining 57 (55.9%) patients remained progression-free, with follow-up duration ranging from 18 to 92 months (median, 86 months).\u003c/p\u003e \u003cp\u003eAdditionally, 35 patients (34.3%) exhibited elevated VAT SUV\u003csub\u003emean\u003c/sub\u003e, whereas 67 patients (65.6%) demonstrated low VAT SUV\u003csub\u003emean\u003c/sub\u003e. Patient characteristics for the high-uptake and low-uptake groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics with high and low VAT uptake\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow VAT uptake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh VAT uptake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;102\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;67\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;35\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (47.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (43.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (54.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54 (52.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (56.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (45.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (2.00, 4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 (1.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.44 (14.42, 16.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.43 (14.18, 16.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.51 (14.74, 16.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINRG \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (29.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (32.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (22.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72 (70.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (67.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (77.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e428.27 (37.97, 573.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e473.72 (29.51, 605.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348.72 (52.89, 369.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267.45 (94.75, 651.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237.50 (86.20, 649.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e291.30 (122.00, 750.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.95 (69.15, 335.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131.20 (68.18, 352.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189.85 (67.98, 311.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e594.50 (358.00, 1125.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e587.00 (339.00, 1131.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e629.50 (404.00, 1131.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM SUV\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55 (0.49, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.53, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT area (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.00 (7.58, 24.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.36 (7.12, 25.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.52 (9.54, 24.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT area (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.44 (5.52, 10.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.45 (5.53, 11.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM area (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.15 (9.68, 15.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.95 (9.72, 13.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT radiodensity (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-75.81\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-76.61\u0026thinsp;\u0026plusmn;\u0026thinsp;12.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-72.83 (-81.98, -67.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT radiodensity (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-84.81 (-87.45, -81.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-85.61 (-87.92, -81.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-83.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM radiodensity (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.66 (52.13, 57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.46 (52.74, 57.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.66 (50.69, 55.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (cm\u0026sup2;/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.90 (1.23, 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82 (1.15, 3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.23 (1.36, 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV/S ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.35, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.36, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.29, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIndependent t-test was used for continuous variables. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Fishers exact test when comparing categorical variables. Mann-Whitney-U test, values presented as median and range\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI: Body mass index; INRG: International Neuroblastoma Risk Group; VMA: Vanillylmandelic Acid; NSE: Neuron-Specific Enolase; SF: Serum Ferritin; LDH: Lactate Dehydrogenase; SM: Skeletal muscle; SUV: Standardized Uptake Value; SAT: Subcutaneous adipose tissue; VAT: Visceral adipose tissue; SMI: Skeletal muscle index; V/S ratio: Visceral to Subcutaneous adipose tissue area ratio; *:p\u0026lt;0.05; \u003csup\u003e#\u003c/sup\u003e: n (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eThe optimal cutoff value for VAT SUV mean was 0.61, dividing all patients into high-uptake and low-uptake groups. Concurrently, a cutoff value of -85.93 HU was used for VAT radiodensity.\u003c/p\u003e \u003cp\u003eMultivariate analysis revealed that INRG risk stratification (HR 3.367, 95% CI 1.291\u0026ndash;8.780, P\u0026thinsp;=\u0026thinsp;0.013), VAT radiodensity (HR 1.055, 95% CI 1.000\u0026ndash;1.112, P\u0026thinsp;=\u0026thinsp;0.049), and VAT SUV\u003csub\u003emean\u003c/sub\u003e (HR 3.998, 95% CI 1.244\u0026ndash;12.852, P\u0026thinsp;=\u0026thinsp;0.020) were independent prognostic factors affecting PFS, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis for Progression-Free Survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analysis Hazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate analysis Hazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.160 (1.039\u0026ndash;1.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.625 (0.901\u0026ndash;2.933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT area (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.002 (0.986\u0026ndash;1.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT area (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.996 (0.946\u0026ndash;1.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM area (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.035 (0.981\u0026ndash;1.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT radiodensity (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.006 (0.979\u0026ndash;1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT radiodensity (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.069 (1.016\u0026ndash;1.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.010*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.055 (1.000\u0026ndash;1.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.049*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM radiodensity (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.969 (0.925\u0026ndash;1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.013 (0.921\u0026ndash;1.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992 (0.878\u0026ndash;1.119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.799 (0.500\u0026ndash;1.276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.965 (0.823\u0026ndash;1.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT SUV\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.016 (0.986\u0026ndash;1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT SUV\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.493 (1.822\u0026ndash;16.565)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.998 (1.244\u0026ndash;12.852)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM SUV\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.362 (0.865\u0026ndash;13.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINRG Risk Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.248 (1.673\u0026ndash;10.788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.367 (1.291\u0026ndash;8.780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.013*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF\u003c/p\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001 (1.000\u0026ndash;1.001)\u003c/p\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*: p\u0026lt;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe VAT SUV\u003csub\u003emean\u003c/sub\u003e level was below 0.61 in 67 patients, with 1-year, 3-year, and 5-year PFS rates of 91.0% (95% CI: 84.1%-97.9%), 76.0% (95% CI: 65.8%-86.2%), and 69.9% (95% CI: 58.9%-80.9%), respectively. In contrast, among 35 patients with VAT SUV\u003csub\u003emean\u003c/sub\u003e \u0026ge;0.61, the 1-year, 3-year, and 5-year PFS rates were 82.9% (95% CI: 70.4%-95.4%), 42.9% (95% CI: 26.4%-59.4%), and 31.4% (95% CI: 16.1%-46.7%), respectively. Compared with the low uptake group, elevated VAT SUV\u003csub\u003emean\u003c/sub\u003e levels were significantly associated with poorer PFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eKaplan-Meier survival analysis revealed that patients in the low VAT uptake and low VAT density groups demonstrated superior prognosis and longer PFS compared to those in the high VAT uptake and high VAT density groups. See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eROC Curve analysis\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves were used to evaluate the predictive performance of each indicator for progression-free survival (PFS) in children with intermediate- or high-risk neuroblastoma. The area under the curve (AUC) and its 95% confidence interval (CI) were calculated. The AUCs for the predictive probabilities of VAT SUV\u003csub\u003emean\u003c/sub\u003e, VAT radiodensity, INRG risk group, and a combined logistic regression model incorporating all three factors were calculated and compared to assess their predictive value. ROC curve analysis revealed that the AUCs for predicting PFS in intermediate- or high-risk neuroblastoma patients using VAT SUV\u003csub\u003emean\u003c/sub\u003e, VAT radiodensity, and INRG risk group classification were 0.637 (95% CI: 0.527\u0026ndash;0.748), 0.613 (95% CI: 0.504\u0026ndash;0.722), and 0.664 (95% CI: 0.559\u0026ndash;0.769), respectively. Combining these three factors into a logistic regression model increased the AUC of the combined predictive probability to 0.758 (95% CI: 0.665\u0026ndash;0.850), significantly higher than any single indicator (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is the first study to confirm the relationship between \u003csup\u003e18\u003c/sup\u003eF-FDG uptake levels in visceral adipose tissue (VAT SUV\u003csub\u003emean\u003c/sub\u003e) and CT radiodensity, and progression-free survival (PFS) in patients with intermediate-to-high-risk neuroblastoma. We found that elevated VAT SUV\u003csub\u003emean\u003c/sub\u003e and high VAT radiodensity indicate shorter PFS, suggesting that the functional state of visceral adipose tissue may serve as a potential novel prognostic biomarker for this patient population.\u003c/p\u003e \u003cp\u003eAdipose tissue not only serves as a lipid storage and secretory organ but also plays crucial roles in metabolism, immunity, and other functions[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Human adipose tissue is primarily categorized into white adipose tissue (WAT) and brown adipose tissue (BAT), each exhibiting distinct morphological, functional, and metabolic characteristics. In patients with various wasting diseases, including cancer, WAT browning is commonly observed, leading to WAT atrophy. This process precedes skeletal muscle wasting and represents an early hallmark of cachexia[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].The potential mechanisms by which white adipose tissue browning induces cancer-related cachexia are as follows: First, during browning, expression of its specific marker, uncoupling protein-1 (UCP-1), significantly increases, thereby activating the thermogenic program in adipocytes. This leads to substantial mobilization of lipid reserves and increased energy expenditure. Second, multiple inflammatory mediators, including IL-6 and TNF-α, exhibit heightened expression under chronic inflammatory conditions. This has been demonstrated to induce UCP-1 transcriptional activation, a mechanism closely linked to WAT browning and enhanced lipolysis during cancer-related weight loss. Finally, in various cancer types, the paraneoplastic factor parathyroid hormone-related protein (PTH-R1) increases, [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] leading to adipose tissue browning. This browning process enhances energy expenditure[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Prolonged high metabolic drive in cancer patients induces adaptive brownification of adipose tissue, intensifying lipolysis and accelerating energy expenditure. This manifests as enhanced glucose uptake capacity and active glucose metabolism within adipose tissue. This study observed a significant association between elevated VAT glucose uptake and reduced progression-free survival in patients with advanced neuroblastoma, indirectly confirming the presence of WAT browning in this population.\u003c/p\u003e \u003cp\u003e\u0026sup1;⁸F-FDG PET/CT is widely used for tumor metabolic assessment, with SUV mean values effectively reflecting tissue glucose uptake levels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. FDG uptake in adipose tissue simultaneously captures glucose metabolism and inflammatory status. In this study, the finding that elevated VAT SUV mean predicts poorer prognosis aligns with conclusions from studies in colorectal, gastric, and pancreatic cancers[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Potential mechanisms may involve upregulation of pro-inflammatory cytokine expression and increased macrophage infiltration within VAT, where this heightened inflammatory state may promote tumor progression [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].Furthermore, VAT exhibits heightened sensitivity to inflammation-induced lipolysis and insulin resistance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Inflammatory cytokines such as IL-6 and TNF-α secreted by visceral adipocytes induce local and systemic inflammatory states, thereby directly or indirectly stimulating cancer growth[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, previous studies evaluating the relationship between FDG uptake in VAT and SAT and clinical outcomes have yielded conflicting results. For instance, in a cohort of newly diagnosed pancreatic cancer patients presenting with lymph node involvement and positive metastasis, SAT glucose metabolism was significantly reduced compared to patients without lymph node metastasis. Conversely, VAT exhibits higher glucose metabolic activity than SAT, potentially linked to increased hexokinase-1 expression. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]A study on metastatic colorectal cancer reported improved survival in patients with high VAT SUV levels during bevacizumab therapy. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]As an explanation, it has been demonstrated that increased VEGF release correlates with pro-inflammatory cytokines and inflammation, and is released at higher levels in visceral adipose tissue, thereby leading to elevated VAT SUV and influencing response to bevacizumab treatment. These inconsistencies may stem from cancer heterogeneity, varying sample sizes, adipose tissue specificity (VAT vs. SAT), or population differences. However, previous studies have primarily focused on adult malignancies such as colorectal, pancreatic, and gastric cancers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This study evaluated the relationship between glucose metabolism in visceral adipose tissue and prognosis in children with advanced neuroblastoma, suggesting that alterations in VAT glucose metabolism may serve as a potential prognostic biomarker for these patients.\u003c/p\u003e \u003cp\u003eNotably, our study also found that patients with high VAT radiodensity exhibited poorer PFS compared to those with low VAT radiodensity. We propose two potential mechanisms to explain the association between increased radiodensity and higher mortality: inflammation and adipose tissue browning. First, increased radiodensity may reflect heightened local inflammation, which correlates with elevated mortality risk. For instance, a study of 40 cardiac surgery patients found that those with higher adipose tissue radiodensity also exhibited increased \u003csup\u003e18\u003c/sup\u003eF-FDG uptake on PET/CT scans, indicating heightened inflammatory activity[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Second, researchers suggest that in cachectic patients, elevated radiodensity may indicate adipose tissue browning. This transformation signifies enhanced lipid metabolism and increased energy expenditure, accelerating catabolism and thereby elevating mortality[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A breast cancer study demonstrated that higher fat density correlates with greater vascular distribution, stronger inflammatory responses, and lipid depletion. Their combined effects may contribute to poorer survival rates following cancer diagnosis[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Animal biopsy studies have shown that elevated SAT radiodensity may be associated with lower lipid content in adipocytes and increased extracellular matrix fibrosis, indicating heightened lipolysis within adipose tissue[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In this study, both VAT radiodensity and its glucose metabolic activity emerged as independent predictors of PFS. Collectively, these findings reflect pathological alterations in adipose tissue\u0026mdash;including lipid depletion, fibrosis, and inflammatory activation\u0026mdash;suggesting that adipose dysfunction may contribute to tumor progression and adverse outcomes through shared pathological pathways.\u003c/p\u003e \u003cp\u003eCertain limitations of the current study must be acknowledged. First, as a single-center retrospective study, selection bias is unavoidable. Second, it should be noted that single-time-point body composition assessment may poorly correlate with dynamic changes over time [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], suggesting that serial PET/CT scans may be required in future studies to better elucidate the relationship between adipose metabolic activity and tumor progression. Third, no clear consensus exists regarding cutoff values for VAT radiodensity applicable to Asian children. Binary classification using cutoff values for VAT SUV mean and VAT radiodensity may pose potential issues. Future multicenter studies could establish specific cutoff values suitable for pediatric oncology populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we found that high VAT metabolic activity and/or high VAT radiodensity are associated with shorter progression-free survival in children with advanced-stage neuroblastoma. Integrating adipose tissue biopsy with PET/CT imaging over time may prove crucial for future studies investigating the relationship between tumor progression and fat metabolism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.B.Y. and L.S.Q. contributed equally to this work. All authors conceptualized and designed the study. Z.B.Y. and L.S.Q. performed data curation and statistical analysis. K.Y. and W.W. conducted the PET/CT image acquisition and interpretation. Z.B.Y. and L.S.Q. wrote the main manuscript text. Z.B.Y. prepared tables and figures. Y.J.G. revised the manuscript for English language and content. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData and materials are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;rber, V., et al., \u003cem\u003eNeuroblastoma arises in early fetal development and its evolutionary duration predicts outcome.\u003c/em\u003e (1546\u0026ndash;1718 (Electronic)).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, M.A.-O., et al., \u003cem\u003eSarcopenia and preserved bone mineral density in paediatric survivors of high-risk neuroblastoma with growth failure.\u003c/em\u003e (2190\u0026ndash;6009 (Electronic)).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauer, J., M.C. 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Chung, \u003cem\u003ePrognostic value of CT attenuation and FDG uptake of adipose tissue in patients with pancreatic adenocarcinoma.\u003c/em\u003e Clin Radiol, 2018. 73(12): p. 1056.e1-1056.e10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J.W., et al., \u003cem\u003eSignificance of CT attenuation and F-18 fluorodeoxyglucose uptake of visceral adipose tissue for predicting survival in gastric cancer patients after curative surgical resection\u003c/em\u003e. Gastric Cancer, 2020. 23(2): p. 273\u0026ndash;284.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J.W., et al., \u003cem\u003eVisceral adipose tissue volume and CT-attenuation as prognostic factors in patients with head and neck cancer\u003c/em\u003e. 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Therap Adv Gastroenterol, 2014. 7(1): p. 38\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan de Wiele, C., et al., \u003cem\u003eMetabolic and morphological measurements of subcutaneous and visceral fat and their relationship with disease stage and overall survival in newly diagnosed pancreatic adenocarcinoma: Metabolic and morphological fat measurements in pancreatic adenocarcinoma\u003c/em\u003e. Eur J Nucl Med Mol Imaging, 2017. 44(1): p. 110\u0026ndash;116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKara\u0026ccedil;elik, T., et al., \u003cem\u003ePrognostic Significance of Adipose Tissue Distribution and Metabolic Activity in PET/CT in Patients with Metastatic Colorectal Cancer\u003c/em\u003e. J Gastrointest Cancer, 2023. 54(2): p. 456\u0026ndash;466.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntonopoulos, A.S., et al., \u003cem\u003eDetecting human coronary inflammation by imaging perivascular fat\u003c/em\u003e. Sci Transl Med, 2017. 9(398).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetruzzelli, M., et al., \u003cem\u003eA switch from white to brown fat increases energy expenditure in cancer-associated cachexia\u003c/em\u003e. Cell Metab, 2014. 20(3): p. 433\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, K.A., \u003cem\u003eMetabolic pathways in obesity-related breast cancer\u003c/em\u003e. Nat Rev Endocrinol, 2021. 17(6): p. 350\u0026ndash;363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy, R.A., et al., \u003cem\u003eAdipose tissue density, a novel biomarker predicting mortality risk in older adults\u003c/em\u003e. J Gerontol A Biol Sci Med Sci, 2014. 69(1): p. 109\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Dijk, D.P.J., et al., \u003cem\u003eEctopic fat in liver and skeletal muscle is associated with shorter overall survival in patients with colorectal liver metastases\u003c/em\u003e. J Cachexia Sarcopenia Muscle, 2021. 12(4): p. 983\u0026ndash;992.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"neuroblastoma, pediatric, FDG, PET/CT, Visceral adipose tissue","lastPublishedDoi":"10.21203/rs.3.rs-9386029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9386029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the impact of body composition and fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (\u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT)-based glucose metabolism variables (primarily visceral adipose tissue, VAT) on progression-free survival (PFS) in children with intermediate- to high-risk neuroblastoma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective study was conducted on 102 pediatric neuroblastoma patients who underwent baseline \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT scans. Clinical, demographic, and survival data were collected. PET/CT assessments included mean standardized uptake value (SUV\u003csub\u003emean\u003c/sub\u003e) of VAT and skeletal muscle, along with body composition measurements: subcutaneous adipose tissue (SAT) area, SAT radiodensity, VAT area, VAT radiodensity, skeletal muscle area, and skeletal muscle radiodensity. Skeletal muscle index (SMI) was calculated by normalizing skeletal muscle area (SM area) to patient height. Patients were stratified into high- and low-VAT uptake subgroups based on optimal VAT SUV\u003csub\u003emean\u003c/sub\u003e cutoff values. Univariate and multivariate regression analyses, along with Kaplan-Meier survival analysis, evaluated associations between metabolic activity, CT-derived body composition variables, and PFS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 102 patients with a mean age of 3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38 years, 45 (44.1%) experienced tumor progression during follow-up. Multivariate Cox proportional hazards regression analysis identified International Neuroblastoma Risk Group (INRG) (hazard ratio [HR] 3.367, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), VAT SUV\u003csub\u003emean\u003c/sub\u003e (HR 3.998, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and VAT radiodensity (HR 1.055, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as adverse prognostic factors for PFS. Notably, patients with elevated VAT uptake demonstrated significantly poorer progression-free survival compared to those with low uptake (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIncreased \u003csup\u003e18\u003c/sup\u003eF-FDG uptake and increased VAT radiodensity in intermediate- to high-risk neuroblastoma patients correlate with shorter progression-free survival, highlighting their potential as biomarkers for poor outcomes.\u003c/p\u003e","manuscriptTitle":"Visceral adipose tissue 18F-FDG uptake and CT-derived body composition variables for predicting progression-free survival in patients with intermediate- and high-risk neuroblastoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 10:49:00","doi":"10.21203/rs.3.rs-9386029/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-03T20:24:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T10:11:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T10:11:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2026-04-11T08:20:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a357de7b-0529-41ac-b1ff-a15e0a05f99b","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"7","date":"2026-05-03T20:24:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:49:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 10:49:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9386029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9386029","identity":"rs-9386029","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}