Prediction of Occult Peritoneal Metastasis of Gastric Cancer Based on CT-Based Body Fat Analysis

Abstract

Introduction

We aimed to investigate the predictive value of body fat composition parameters obtained using computed tomography (CT) for preoperative occult peritoneal metastasis (OPM) in gastric cancer.

Methods

We adopted a single-center case-control design and retrospectively analyzed data from 115 patients with gastric cancer who underwent laparoscopic exploration or radical gastrectomy at our hospital between October 2020 and March 2024. Patients were divided into OPM-positive (n = 35) and OPM-negative (n = 80) groups. The visceral adipose tissue (VAT) area, subcutaneous adipose tissue (SAT) area, and mean attenuation of CT images at the central level of the L3 lumbar spine were measured using the sliceOmatic software, and the clinical and imaging characteristics of the patients were analyzed.

Results

Significant differences were present in the VAT area, VAT/SAT area, mean attenuation of SAT, mean attenuation of VAT, VAT area of different N stages, and lesion sites between the OPM-positive and OPM-negative groups (P < 0.05). Results of a multifactor logistic regression analysis showed that the VAT/SAT area ratio and VAT mean attenuation were independent risk factors for OPM in gastric cancer (P < 0.05). The AUC of the clinical-imaging model in predicting gastric cancer OPM was 0.92 (95% confidence interval, 0.86-0.97). A VAT/SAT area of 1.04 (specificity: 54%, sensitivity: 86%) and a VAT mean attenuation of −83.60 Hounsfield unit (HU) (specificity: 99%, sensitivity:34%) were used as optimal cutoff values for identifying the occurrence of OPM in gastric cancer.

Conclusion

VAT/SAT area ratio and mean VAT attenuation in body fat composition are potential auxiliary parameters for predicting OPM in gastric cancer.

Keywords

gastric cancer occult peritoneal metastasis subcutaneous adipose tissue visceral adipose tissue CT scan

Introduction

Gastric cancer (GC) is a common gastrointestinal malignancy that originates in the epithelial cells of the gastric mucosa. While GC ranks fifth in both incidence and mortality globally, in Asian populations, its mortality rate has been increasing and now ranks third. Notably, China accounts for approximately 37% of all newly diagnosed gastric cancer cases worldwide.^1,2 Peritoneal metastasis (PM) occurs in over 20% of patients at initial diagnosis; these patients are typically not candidates for curative surgery and often receive palliative treatments such as hyperthermic intraperitoneal chemotherapy, targeted therapy, or immunotherapy.³ Computed tomography (CT) is the most commonly used imaging modality for detecting PM in GC because of its high specificity (95%–99%). Typical radiological findings include peritoneal thickening, nodularity, omental caking, and ascites. However, most of these typical signs appear in the mid and late stages of PM, and CT often fails to detect microscopic foci, resulting in a sensitivity of 28.3%–50.9% and a diagnostic accuracy of 62%.^4,5

The term occult peritoneal metastasis (OPM), as defined by the 2023 Chinese Expert Consensus on the Diagnosis and Treatment of Gastric Cancer Peritoneal Metastasis, refers to PM that is not detected by standardized preoperative imaging but is confirmed through laparoscopic exploration, intraoperative findings, or postoperative pathology.⁶ Early and accurate detection of OPM through non-invasive imaging techniques instead of invasive laparoscopic procedures is of great clinical importance for improving prognosis and guiding treatment strategies.

Recent studies have focused on the interactions between adipocytes and tumor cells in omental metastasis. Studies suggest that adipocytes serve as an alternative energy source for cancer cells. In fat-rich tumor microenvironments, adipocytes reprogram cancer cell metabolism by converting glucose into glycerol-3-phosphate, thereby fueling the energy and biosynthetic demands of tumor cells and promoting metastasis.⁷ Radiomics studies have also shown that peritoneal infiltration by tumors can alter the CT features of adjacent visceral fat.⁸ While spectral CT and radiomics have been widely studied in the context of OPM in GC, the few studies that have explored the role of CT-derived body fat composition parameters in predicting OPM have reported inconsistent findings. For example, Cheng et al⁹ demonstrated that the visceral adipose tissue (VAT) area can effectively predict the occurrence of PM in GC, whereas Kim et al¹⁰ suggested that the mean CT attenuation of the VAT may serve as a potential auxiliary predictor of OPM. Therefore, this study aimed to evaluate the predictive value of multiple body fat composition parameters derived from CT images of patients with GC and analyze the relationship between VAT and imaging features, to establish a preoperative prediction model for OPM using logistic regression analysis.

Materials and Methods

Clinical Data

This retrospective case-control study was approved by the Medical Ethics Committee of our hospital (Approval No. 2025K020), and the requirement for informed consent was waived. This research report complies with the STROBE guidelines.¹¹ A total of 445 patients with pathologically confirmed GC who underwent laparoscopy or surgery at our hospital between October 2020 and March 2024 were screened, and the detailed information of all patients was extracted. The patient enrollment process is illustrated in Figure 1.

Figure 1.

Patient Selection Flow Chart. Abbreviations: CT, Computed Tomography; PM, Peritoneal Metastasis

Inclusion Criteria

1. A pathologically confirmed diagnosis of GC;

2. Those who had undergone a non-contrast and contrast-enhanced whole-abdominal CT scan within 2 weeks before surgery at our hospital.

3. No signs of peritoneal metastasis (PM) on preoperative CT, including peritoneal nodular/plaque-like thickening and density changes, indistinct or striated omental fat planes, omental caking or smudging, nodular peritoneal thickening in the mesentery, or the presence of ascites.

Exclusion Criteria

1. Missing or incomplete preoperative CT images or those containing artifacts resulting in poor image quality.

2. History of liver cirrhosis, abdominopelvic tumors, or other inflammatory diseases;

3. History of prior abdominal surgery;

4. Receipt of systemic or localized treatment before CT enhancement.

Clinical data were collected, including sex, age, height, weight, and tumor markers, such as carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), and carbohydrate antigen 125 (CA125).

Imaging Equipment and Methods

All CT examinations were performed using a 256-slice Revolution CT scanner (GE Healthcare, Little Chalfont, USA). Informed consent for contrast-enhanced CT was obtained from all the patients. Patients were instructed to fast from solid food for 6-8 h and drink 500-800 mL of water before the scan to ensure adequate gastric distension.

The patients were scanned in the supine position from the diaphragm to the pubic symphysis. The scanning parameters were as follows: tube voltage, 120 kVp; tube current, 100 mA; slice thickness, 5 mm; slice interval, 5 mm; matrix size, 512 × 512; rotation time, 0.80 s; total exposure time, 2.94 s; pitch, 0.99; and reconstructed slice thickness, 1.25 mm. A high-pressure injector was used to intravenously administer iodixanol (contrast agent) via the elbow vein at a flow rate of 3.0 mL/s and a dose of 1.5 mg/kg. When the threshold was triggered, an arterial phase scan was performed, completed within 6 s, and a venous phase scan was conducted 25 s after the arterial phase scan ended. All non-contrast and venous-phase CT images were evaluated using the hospital’s picture archiving and communication system (PACS). The specific criteria were as follows: the patient’s gastric cavity should be well-distended with good image quality; the primary GC lesion should be clearly visualized and enhanced in the venous phase; no thickening or density changes should be observed in the peritoneum, omentum, or mesentery; a level 1 smudge sign; and no peritoneal effusion should be present.

Body Fat Composition Measurement

Whole abdomen CT images were retrieved from the PACS, Axial non-contrast CT images at the level of the third lumbar vertebra (L3) were selected, and the corresponding Digital Imaging and Communications in Medicine (DICOM) files were imported into sliceOmatic (developed by TomoVision) software. Body composition components were differentiated based on tissue-specific Hounsfield unit (HU) thresholds. Subcutaneous adipose tissue (SAT) was defined as tissue located outside the muscle boundary with a density range of −190 to −30 HU, while visceral adipose tissue (VAT) was defined as tissue located within the muscle boundary with a density range of −150 to −50 HU. The skeletal muscle had a density range of −29 to +150 HU.¹² A combination of automatic threshold detection and manual boundary identification was used in sliceOmatic to delineate the SAT and VAT regions. The software automatically calculated the SAT and VAT areas, as well as the mean attenuation for both. The resulting measurements were exported for further analysis (Figure 2).

Figure 2.

L3 Vertebral Center Level, Red = Subcutaneous Adipose Tissue (SAT), Green = Visceral Fat Area (VAT), Yellow = Skeletal Muscle (SM)

Statistical Analysis

All statistical analyses were conducted using SPSS version 25.0. Continuous variables were expressed as mean ± standard deviation (x̄ ± s), and categorical variables were presented as counts and percentages (n [%]). Independent-sample t-tests were used to compare SAT area, VAT area, VAT/SAT area ratio, and the mean attenuation of SAT and VAT between the OPM-positive and OPM-negative groups. Differences in VAT area and mean attenuation across different T and N stages and tumor locations were analyzed using one-way ANOVA. Multivariate logistic regression analysis was conducted to identify independent predictors of OPM. The diagnostic performance of the predictive model was assessed using receiver operating characteristic (ROC) curves and a calibration curve, which was internally validated by resampling 1000 times using the bootstrap self-help method. The optimal cut-off values for predicting OPM were determined based on the Youden index, and the corresponding sensitivity and specificity were calculated. Statistical significance was set at P < 0.05.

Results

General Information

A total of 115 patients with pathologically confirmed GC who showed no evidence of PM on preoperative imaging were included in the study. Based on the results of laparoscopic or surgical exploration to confirm the presence or absence of OPM, the patients were divided into an OPM-positive group (35 cases: 25 males, 10 females) and an OPM-negative group (80 cases: 54 males, 26 females).

Comparison of Clinical, Pathological, and Imaging Parameters

Analysis of clinical, pathological, and imaging variables revealed significant differences between the OPM-positive and OPM-negative groups in terms of body mass index (BMI), T stage, N stage, and tumor location (P < 0.05). However, there were no significant differences between the two groups in terms of sex, age, CEA, CA125, and CA19-9 levels, or pathological results (P > 0.05). Regarding abdominal fat parameters at the L3 level, the results indicated that the VAT area and VAT/SAT ratio were smaller in the OPM-positive group than in the OPM-negative group, whereas the mean attenuation of SAT and VAT was higher in the OPM-positive group. These differences were considered statistically significant (P < 0.05). No significant difference was found in the SAT area between the two groups (P > 0.05) (Table 1).

Table 1.

Comparison of Clinical, Pathological and Imaging Parameters in OPM-Positive Group and OPM-Negative Group of Gastric Cancer

Characteristic		OPM(−)(n = 80)	OPM(+)(n = 35)	P
Sex	Male	54(68.4)	25(31.6)	0.67
Sex	Female	26(72.2)	10(27.8)
Age		60.99 ± 8.11	64.51 ± 9.08	0.052
BMI		24.51 ± 3.38	22.66 ± 2.99	0.04
cT stage	T1	7(100.0)	0(0.0)	<0.001
	T2	15(78.9)	4(21.1)
	T3	33(91.7)	3(8.3)
	T4	25(47.2)	28(52.8)
cN stage	N0	31(93.9)	2(6.1)	0.003
	N1	13(68.4)	3(31.6)
	N2	15(55.6)	12(44.4)
	N3	21(58.3)	15(41.7)
CEA	Normal (<5 ng/mL)	59(72.8)	22(27.2)	0.239
CEA	Elevated (≥5 ng/mL)	21(61.8)	13(38.2)
CA125	Normal (<35 u/mL)	78(70.9)	32(29.1)	0.142
CA125	Elevated (≥35 u/mL)	2(40.0)	3(60.0)
CA199	Normal (<37 u/mL)	71(70.3)	30(29.7)	0.647
CA199	Elevated (≥37 u/mL)	9(64.3)	5(35.7)
Location	Esophagogastric junction	38(82.6)	8(17.4)	0.001
	Body	16(88.9)	2(11.1)
	Antrum	11(57.9)	8(42.1)
	≥2 parts	15(46.9)	17(53.1)
Pathological type	Adenocarcinoma	74(71.8)	29(28.2)	0.243
	Signet-ring cell carcinoma	4(57.1)	3(42.9)
	Hybridization	2(40.0)	3(60.0)
Fat area (HU)	SAT	141.28 ± 41.65	133.10 ± 27.78	0.22
	VAT	154.19 ± 51.34	113.09 ± 30.02	<0.001
	VAT/SAT	1.11 ± 0.30	0.86 ± 0.17	<0.001
Fat mean attenuation (HU)	SAT	−96.53 ± 5.83	−81.23 ± 33.80	<0.001
Fat mean attenuation (HU)	VAT	−96.92 ± 5.60	−84.48 ± 30.92	<0.001

Abbreviations: OPM, occult peritoneal metastasis; BMI, body mass index; cT, clinical T stage; cN, clinical N stage; CEA, carcinoembryonic antigen; CA125, cancer antigen 125; CA199, cancer antigen 19-9; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; HU, hounsfield unit.

Relationship Between Visceral Fat Parameters and Imaging Features

The relationship between VAT area, mean attenuation, and imaging features in patients with GC was analyzed. The results showed that the VAT area differed significantly across different N stages and tumor locations (P < 0.05). However, no significant differences were observed in the VAT area across different T stages (P > 0.05). Similarly, no significant differences were found in the mean VAT attenuation across different T and N stages and tumor locations (P > 0.05) (Table 2).

Table 2.

Comparison of Visceral Adipose Tissue Area and Average CT Value Between Different Imaging Features

Characteristic	n	Visceral adipose Tissue(VAT)
Characteristic		Area(cm²)	F	P	Mean attenuation(HU)	F	P
cT stage	T1(7)	151.18 ± 32.58	3.35	0.22	−98.48 ± 2.35	2.48	0.65
	T2(19)	156.52 ± 47.37			−97.47 ± 6.56
	T3(36)	154.58 ± 56.00			−97.11 ± 6.77
	T4(53)	126.35 ± 43.92			−88.17 ± 25.54
cN stage	N0(33)	161.77 ± 44.75	2.86	0.040	−98.01 ± 5.48	1.40	0.25
	N1(19)	127.68 ± 51.92			−91.12 ± 10.87
	N2(27)	138.70 ± 60.14			−93.72 ± 7.91
	N3(36)	132.90 ± 39.03			−93.13 ± 18.44
Location	Esophagogastric junction(46)	151.72 ± 45.10	4.01	0.009	−96.64 ± 6.28	2.59	0.06
	Body(18)	163.41 ± 62.48			−96.19 ± 8.33
	Antrum(19)	124.24 ± 43.64			−96.19 ± 8.33
	≥2 parts(32)	125.40 ± 44.17			−92.11 ± 9.15

Abbreviations: VAT, visceral adipose tissue; HU, hounsfield unit; cT, clinical T stage; cN, clinical N stage.

Univariate and Multivariate Logistic Regression Analysis of Factors Influencing OPM in GC

Univariate logistic regression analysis was performed for sex, age, BMI, SAT area, VAT area, VAT/SAT area ratio, SAT mean attenuation, VAT mean attenuation, T stage, N stage, tumor location, as well as CEA, CA19-9, and CA125 levels. The results showed that BMI, age, VAT area, VAT/SAT area ratio, SAT mean attenuation, VAT mean attenuation, T stage, N stage, and tumor location were significant factors influencing the occurrence of OPM in patients with GC (P < 0.05). Multivariate logistic regression analysis incorporating the aforementioned factors revealed that the VAT/SAT area ratio and VAT mean attenuation were independent predictors of OPM (P < 0.05) (Table 3). The combined prediction model for OPM had an AUC of 0.92 (95% confidence interval (CI): 0.86-0.97). The Bootstrap method was used to internally validate the model and draw a calibration curve. The average AUC value was 0.91 (95% CI: 0.84-0.96), suggesting that the model has good stability and good consistency with the actual observed values (Figure 3). The optimal cutoff values for distinguishing between patients with GC with OPM and those without OPM were a VAT/SAT ratio of 1.04 (specificity: 54%, sensitivity: 86%) and a mean VAT CT value of −83.60 HU (specificity: 99%, sensitivity: 34%).

Table 3.

Multivariate Logistic Regression Analysis of Occult Peritoneal Metastasis of Gastric Cancer

Characteristic	OR	95% CI	P
VAT area	1.004	(0.982–1.025)	0.744
VAT/SAT ratio	0.004	(0.000–0.183)	0.005
VAT mean attenuation (HU)	1.129	(1.021–1.248)	0.018
SAT mean attenuation (HU)	0.940	(0.826–1.071)	0.354
Age	1.036	(0.956–1.123)	0.386
cT stage			0.118
T2	0.000	(0.000-.)	0.999
T3	0.514	(0.075–3.513)	0.497
T4	0.124	(0.023–0.675)	0.016
cN stage			0.439
N1	0.408	(0054–3.085)	0.385
N2	0.384	(0.060–2.483)	0.315
N3	1.433	(0.357–5.753)	0.612
Location			0.307
Body	0.450	(0.101–2.015)	0.297
Antrum	0.065	(0.003–1.409)	0.082
≥2 parts	0.781	(0.155–3.944)	0.765

Abbreviations: OR, odds ratio; CI, confidence interval; VAT, visceral adipose tissue; SAT, subcutaneous adipose tissue; HU, hounsfield unit; cT, clinical T stage; cN, clinical N stage.

Figure 3.

(A) Receiver Operating Characteristic Curves for VAT/SAT Area and VAT Average CT Value in Predicting Occult Peritoneal Metastasis of Gastric Cancer. (B) Calibration Curve: The Predicted Value of the Model is Close to the Actual Value. (C) Bootstrap ROC Curves: The Average AUC Value was 0.91 (95% Confidence Interval: 0.84-0.96), Suggesting that the Model has Good Stability. Abbreviations: VAT, Visceral Adipose Tissue; SAT, Subcutaneous Adipose Tissue; CT, Computed Tomography; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve

Discussion

The “seed-soil” hypothesis is widely accepted in the academic community as the underlying mechanism for the development of PM in GC. The occurrence of PM in GC is primarily determined by the dissemination and spread of tumor cells (seeds) and the microenvironment of the peritoneum (soil). Cancer cells with occult metastasis have stronger immune evasion capabilities and can spread to the peritoneum through peritoneal shedding or lymph node metastasis. Fibrosis and angiogenesis in the peritoneal microenvironment promoted the survival of occult lesions (Figure 4A).¹³ The tumor microenvironment includes resident adipocytes, fibroblasts, various recruited hematopoietic cells, newly formed blood vessels, and associated cells. Adipocytes located near cancer cells exhibit significant morphological and functional changes, such as a reduction in lipid content and overexpression of interleukin (IL)-6.¹⁴ In recent years, there has been increasing research on the role of adipose tissue in the tumor microenvironment. Body fat composition has shown significant value in predicting the risk of PM and assessing prognosis in gastrointestinal diseases, including GC.¹⁵ In terms of accuracy and scanning time, CT has advantages over ultrasound and magnetic resonance imaging for measuring body fat composition, making it particularly useful in clinical settings for patients with cancer.¹⁶ Therefore, CT is widely used for evaluating body fat composition in cancer patients due to its superior accuracy and accessibility.

Figure 4.

(A) Tumor Peritoneal Transplantation Metastasis, (B) Fat Cells and Tumor Cells Action Diagram

Some researchers believe that after tumor metastasis to the omentum, the adipocytes surrounding the tumor alter their adipokine profile, increasing leptin expression and reducing adiponectin expression. This activates the PI3K/AKT signaling pathway, promoting endothelial cell proliferation and the expression of genes related to angiogenesis, thereby inducing angiogenesis. Additionally, leptin binds to leptin receptors on the surface of adipocytes and activates hormone-sensitive lipase within adipocytes to promote lipolysis. Furthermore, adipocytes undergo metabolic reprogramming with insulin resistance and a high free fatty acid environment in VAT, providing abundant energy for tumor cell peritoneal dissemination. These factors collectively lead to a reduction in the areas of VAT and SAT.¹⁷ The results of this study also indicate that the VAT area in the OPM-positive group was smaller than in the OPM-negative group, and the VAT/SAT area ratio showed statistically significant differences between the two groups. This conclusion contradicts the findings of Cheng et al⁹ possibly because our study population included a higher proportion of patients with advanced GC. In advanced GC. Large amounts of pro-inflammatory cytokines are secreted, which strongly activate lipolysis and mobilize the VAT pool to provide energy for tumors, leading to the rapid depletion of visceral fat and a sharp decline in VAT. However, between diagnosis and surgery, patients with GC often experience reduced dietary intake and diminished physical activity due to tumor-induced catabolism, impaired digestive function, and psychological factors. Reduced intake leads to an overall decline in total body fat mass; however, VAT is relatively preserved as it maintains energy reserves for core organs, with a corresponding reduction in SAT. Concurrently, decreased physical activity results in slower metabolism, which can also contribute to VAT accumulation. Cheng et al conducted lumbar quantitative CT scans on 389 healthy participants and analyzed the correlation between the VAT area at each intervertebral disc level from T12/L1 to L5/S1 and the total abdominal fat volume (T12-S1). The results showed that the VAT area measured at the L₂/L₃ level had the strongest correlation with the total VAT volume.¹⁸ Therefore, this study extracted the adipose area from the central slice of the L3 vertebral body as the optimal anatomical site for abdominal fat measurement. However, whether this measurement represents the overall abdominal fat distribution requires further investigation, and it is noteworthy that the VAT area also showed statistical significance across different N stages and primary tumor locations. Our results revealed a negative correlation between N staging and VAT area, indicating that patients with larger VAT areas may be at a lower risk of lymph node metastasis and have earlier N staging. Previous studies have demonstrated that as N staging increases, the tumor burden increases and the systemic inflammatory response intensifies, resulting in the rapid depletion of VAT and a significant decrease in VAT.¹⁹ Previous studies have also confirmed the value of VAT and SAT in predicting the occurrence of gastrointestinal tumors and distant metastases after surgery. Nagata et al²⁰ conducted a study on the correlation between quantitative CT assessments of VAT, SAT, and colorectal adenomas and demonstrated that higher VAT levels were associated with an increased risk of adenoma formation. Pahk et al²¹ found that the VAT/SAT ratio has an independent influence on distant metastasis in colorectal cancer. Monitoring the VAT/SAT area ratio has shown good predictive value for distant metastasis during postoperative follow-up.

The mean attenuation reflects the chronic inflammatory state of the adipocytes. Because adipocytes are a major component of the tumor microenvironment in omental metastasis, it is reasonable to hypothesize that adipocytes play a crucial role in the omental metastasis of GC. The results of this study indicate that the mean attenuations of VAT and SAT in the OPM-positive group were significantly higher than those in the OPM-negative group. This finding is supported by that of Li et al²² who showed that VAT could predict OPM. Furthermore, Xiang et al²³ demonstrated that omental adipocytes could enhance the invasiveness of GC cells by activating the PI3K-Akt signaling pathway via oleic acid. Therefore, in the presence of OPM, the mean attenuation of both the VAT and SAT increased, indicating that the peritoneum was in an inflammatory state.

Many factors influence GC omental metastasis (OPM), including tumor markers and intrinsic characteristics of the tumor.²⁴ In this study, statistical significance was observed in BMI, T stage, N stage, and tumor location between the OPM-positive and OPM-negative groups. Generally, deeper tumor invasion, a higher number of enlarged lymph nodes, and more extensive involvement of the primary lesion are associated with a higher likelihood of metastasis. Mesothelial cells of the peritoneum release CA125 into the bloodstream when invaded by tumor cells.²⁵ Therefore, theoretically, CEA, CA199, and CA125 could serve as potential markers for detecting PM. However, in this study, there were no statistically significant differences in age, CEA, CA199, or CA125 levels between the OPM-positive and OPM-negative groups. The differences in the pathological types were also not statistically significant. This may be due to peritoneal metastases, primarily consisting of micrometastases or scattered cancer cells that have a weaker ability to secrete CEA, CA199, or CA125, or because the tumor cells may exhibit a low-secretory phenotype, resulting in no significant difference in blood concentrations between the two groups. Another possibility is that the detection time point for occult metastases was earlier, before the markers significantly increased. Additionally, 89% of the cases were adenocarcinomas, with other pathological types being less frequent, which may have introduced bias.

Univariate logistic regression analysis showed that BMI, age, VAT area, VAT/SAT area ratio, SAT mean attenuation, VAT mean attenuation, T stage, N stage, and tumor location were risk factors for OPM in GC. Liu et al²⁶ also demonstrated that T stage reflects tumor invasiveness and is a predictor of OPM in GC. According to the “seed-soil” hypothesis, the lymphatic fenestrations and lymphatic nipple areas of the peritoneum facilitate the “seeding” of free tumor cells within the abdominal cavity, making OPM more likely when there are more lymph nodes involved. Thus, the N stage is a predictive factor for OPM in GC.²⁷ Multivariate logistic regression analysis revealed that the VAT/SAT area ratio and VAT mean attenuation are independent risk factors for OPM in GC. When these two factors were incorporated into the body fat parameter prediction model, the results showed an AUC value of 0.92, indicating that body fat parameters have good predictive power for OPM in GC. The calibration curve of the model demonstrates that the predicted values were close to the actual values. When the VAT/SAT area ratio was greater than 1.04 and the VAT mean attenuation was greater than −83.60 HU, the likelihood of GC developing OPM was significantly higher. However, the specificity of the VAT/SAT area ratio was only 54%, indicating a high false-positive rate, which may lead to overintervention by clinicians, resulting in unnecessary laparoscopic surgery or chemotherapy. Meanwhile, the sensitivity of the VAT mean attenuation was 34%, leading to some OPM cases being missed and missing the opportunity for early intervention. Therefore, combining the VAT/SAT area ratio with the VAT mean attenuation can balance their respective shortcomings: first, the VAT/SAT area ratio can be used to screen high-risk patients, and then the VAT mean attenuation can be used to predict OPM. The adipose tissue is a highly metabolically active endocrine organ that secretes various cytokines and hormones that regulate and contribute to tumorigenesis and tumor progression through multiple pathways²⁸ (Figure 4B). Therefore, when patients with GC undergo abdominal CT, an analysis of abdominal body fat composition can be performed, which helps predict the likelihood of OPM. This method does not increase the patient’s radiation exposure or economic burden and can also guide surgical decision-making.

Previous studies on the use of CT-based adipose tissue quantification parameters for the preoperative prediction of PM in GC typically constructed models that focused solely on VAT mean attenuation without incorporating both adipose area and attenuation into the analysis. Such models fail to comprehensively and accurately assess the adipose tissue status, thereby limiting their clinical applicability. The combined model of adipose area and attenuation constructed in this study is a noninvasive and efficient tool that can effectively assess the likelihood of preoperative OPM in patients with GC. Because body fat parameters are readily available preoperatively, this model is well-suited for clinical practice.

This study also has some limitations. First, the sample size for OPM in GC was relatively small, and external validation was not performed. Larger multicenter studies are required to validate these findings. Second, this study only assessed two-dimensional SAT and VAT at the L3 vertebral level. Future research could benefit from the application of three-dimensional artificial intelligence delineation techniques to comprehensively evaluate the entire adipose tissue.

Conclusion

In conclusion, this study provides a preliminary exploration of the potential value of body fat composition and clinical factors for predicting OPM in patients with GC. This demonstrates that the VAT/SAT area ratio and mean VAT attenuation are independent risk factors for OPM in GC. Early detection of OPM can aid the development of personalized treatment strategies and improve patient prognosis.

Ethical Consideration

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by The Medical Ethics Committee of Changzhi People’s Hospital (Approval No. 2025K020). This was a retrospective study.

Footnotes

Acknowledgments

We are thankful to all the participants in this study. The authors also thank hospital managements for permission, cooperation, contributions and logistic support during data Collection. We would like to thank Editage () for English language editing.

ORCID iD

Yongai Li

Consent to Participate

Each patient signed a general informed consent form of our hospital upon admission, which was approved by the ethics committee. Therefore, there was no need for additional informed consent.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported partly by Fundamental Research Program of Shanxi Province (202303021212367 and 202203021212011) and Shanxi Provincial Health Commission, Grass-roots Appropriate Technology Promotion (2025J054).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data are not publicly available due to privacy and ethical restrictions.*

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