Development and validation of a preoperative CT-based body composition nomogram for predicting recurrence-free survival after radical surgery in patients with gastric cancer

Introduction

Gastric cancer (GC) is a prevalent malignant tumor of the digestive system and a leading cause of cancer-related mortality worldwide (1). Surgical resection remains the most effective treatment for curable GC (2). However, the recurrence rate after radical gastrectomy is notably high, ranging from 22% to 45%, which significantly impacts clinical prognosis (3). Consequently, an objective and accurate preoperative prognostic assessment is essential for predicting postoperative outcomes and guiding treatment strategies.

Malnutrition is a common issue among GC patients, driven by reduced food intake and altered metabolic and inflammatory responses (4). The progression of GC is often accompanied by a severe decline in nutritional status and bodily function, leading to diminished treatment efficacy and poorer prognosis (5). Body composition, particularly adipose tissue and skeletal muscle (SM), reflects the nutritional status of patients and has become a focal point in nutritional research for cancer patients. Assessing nutritional status through body composition measurements provides insights into the body’s morphological structure, metabolic characteristics, and physiological reserves. This assessment is linked to perioperative inflammatory responses and nutritional metabolism, making it a significant factor influencing prognosis (6,7).

As an indispensable preoperative imaging tool for GC, computed tomography (CT) provides critical information for accurate diagnosis and informed treatment planning. It serves as the gold standard for evaluating body composition (8). CT-quantified body composition parameters have been recognized as significant factors influencing postoperative clinical outcomes in various types of tumors (9-11). The measurement of body composition using CT can objectively and dynamically monitor the nutritional status of patients, aiding in the prediction of prognosis and the selection of appropriate treatment strategies. Previous studies focused on evaluating the impact of individual CT body composition parameters on the prognosis of patients with GC, but relatively few studies explored the combined prognostic value of CT body composition parameters and other preoperative indicators.

Therefore, this study aimed to construct and validate a nomogram model based on preoperative CT-quantified body composition parameters and preoperative clinical indicators to predict recurrence-free survival (RFS) in patients undergoing radical resection for GC. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-838/rc).

Methods

Patient data

The study was approved by the Ethics Committee of Affiliated Cancer Hospital of Nantong University (No.2023-A 07) and were performed in accordance with Declaration of Helsinki and its subsequent amendments. Due to the retrospective nature of the study, patient informed consent was waived by the Ethics Committee. We retrospectively collected data from patients with pathologically confirmed GC treated at Affiliated Cancer Hospital of Nantong University between October 2018 and May 2023. Inclusion criteria were: (I) patients who received radical gastrectomy combined with D2 lymphadenectomy within a 2-week interval after CT imaging, and whose diagnoses were pathologically confirmed; (II) no previous history of gastrectomy or endoscopic resection; (III) complete preoperative clinical and postoperative pathological data available. The exclusion criteria were: (I) receiving other treatments before gastrectomy (radiotherapy and chemotherapy); (II) more than 2 weeks between CT scanning and gastrectomy; (III) poor-quality CT images; and (IV) incomplete clinical or pathological data (Figure 1).

Figure 1 Patient selection flowchart. CT, computed tomography.

Clinical data

Demographic characteristics and preoperative laboratory measurements were retrieved from the institutional electronic medical records systems. This included age, sex, tumor markers [carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), and carbohydrate antigen 125 (CA125)], nutritional indicators [hemoglobin (HB), platelets (PLT), albumin (ALB), prognostic nutritional index (PNI)], and inflammation markers [platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and white blood cells (WBCs)]. In our institutions, the normal ranges are as follows: HB [110–160] g/L, PLT [100–300] ×10⁹/L, WBC [4–10] ×10⁹/L, and ALB [35–55] g/L. Tumor markers (CEA, CA199 and CA125) are considered abnormal when above 5.0 ng/mL, 37.0 U/mL and 35 U/mL, respectively. The PNI was calculated using the formula: ALB (g/L) + 5 × lymphocyte count (10⁹/L). Based on optimal cutoff values determined by X-tile (version 3.6.1) (12), PLR, LMR, NLR, and PNI were categorized into low and high groups.

CT imaging technology

CT scans were performed using a Siemens SOMATOM Sensation 64-slice CT scanner (Sensation 64, Siemens Healthcare, Germany) with a tube voltage of 120 kV and automatic tube current modulation. The slice thickness and interval were both 5 mm, with a pitch of 1.0, a matrix size of 512×512, and a collimation of 1.0 mm. Contrast-enhanced scans were obtained after intravenous injection of iodixanol (300 mgI/mL) at a dose of 1.5 mL/kg body weight, with a flow rate set at 2.5–3.0 mL/s. Arterial phase images were acquired after a 25-s delay, and venous phase images after a 50-s delay. All images were transferred to a Syngo.via VB20 workstation for coronal, sagittal, and axial reconstruction with a reconstruction layer thickness of 1.0 mm.

CT-based evaluation of body composition

Body composition measurements were performed using the imaging software sliceOmatic (version 5.0, TomoVision Software, Magog, Canada) at the level of the third lumbar vertebra (L3), where both transverse processes are visible. The area and radiological density of different body components, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and SM, were measured (13) (Figure 2). Image analysis utilized axial images from the portal venous phase with a reconstruction layer thickness of 1 mm. According to current literature (14), the following tissue-specific standards were applied: VAT: −150 to −50 Hounsfield unit (HU), SAT: −190 to −30 HU, and SM: −29 to 150 HU. Using X-tile (version 3.6.1), optimal cutoff values were determined, and SM area, SM density, VAT area, VAT density, SAT area, and SAT density were categorized into high and low groups. The SM index was calculated by dividing the SM area (cm²) by the square of the patient’s height (m²). The distribution of adipose tissue was assessed by the ratio of visceral to subcutaneous fat area. Based on previous literature, sarcopenia (15) was defined as an SM index ≤40.8 cm²/m² for men and ≤34.9 cm²/m² for women. Visceral obesity (16) was defined as a visceral-to-subcutaneous fat ratio ≥1.33 for men and ≥0.93 for women. CT-based body composition analysis was performed independently by two experienced abdominal radiologists (with 5 and 10 years of expertise, respectively) who were blinded to clinical and pathological information. To ensure reliability, the final quantitative parameters were calculated as the mean values of both observers’ measurements.

Figure 2 Measurement of body composition on a single axial CT slice at the L3 vertebral level during the portal venous phase. (A) SM area; (B) SAT area; (C) VAT area. CT, computed tomography; SM, skeletal muscle; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue.

Follow-up strategy

Systematic postoperative follow-up was implemented for all enrolled patients, with clinical assessments performed at 3-month intervals during the initial year and extended to 3–6-month intervals thereafter, continuing through December 2023. A minimum follow-up period of 6 months was ensured for all cases. Standardized follow-up protocols incorporated serial laboratory investigations and imaging modalities. Recurrence was strictly defined as radiologically or pathologically confirmed local recurrence, peritoneal metastasis, distant organ metastasis, or GC-specific mortality. RFS was precisely measured from the surgical date to the first objectively documented recurrence event.

Pathological finding

Pathological findings from surgical specimens were documented, including tumor staging according to pathological classification [according to the 8^th edition of the American Joint Committee on Cancer (AJCC) staging system] (17), tumor differentiation grade, perineural invasion, and lymphovascular invasion (LVI).

Statistical analysis

Continuous variables with a normal distribution were expressed as mean ± standard deviation, and comparisons between groups were made using independent sample t-test. Categorical variables were expressed as counts and percentages, with comparison between groups made using the Chi-squared test. Optimal cutoff values for preoperative indicators were determined using X-tile (version 3.6.1). Continuous variables were then converted into categorical variables. Kappa statistics were used to calculate the inter-observer agreement for CT body composition parameters. Predictors demonstrating statistical significance (P<0.05) in univariate Cox proportional hazards analysis were subsequently included in multivariate analysis, employing backward stepwise elimination to determine independent risk factors for tumor recurrence. The discriminatory ability of the preoperative nomogram model was evaluated using Harrell’s C-index. Calibration plots were used to describe the agreement between predicted and observed recurrence risks. Time-dependent receiver operating characteristic (tdROC) curve analysis was performed to quantify predictive accuracy at different time points. Decision curve analysis (DCA) was conducted to quantify the clinical utility and net benefit of the preoperative nomogram model across various threshold probabilities. RFS distributions were visualized using Kaplan-Meier (KM) survival curves, stratified according to risk classification. Statistical significance between risk groups was evaluated using the log-rank test, with P values <0.05 considered statistically significant. All statistical analyses were performed using R software (version 3.5.1; The R Foundation for Statistical Computing, Vienna, Austria) or SPSS software (version 26.0; IBM, Armonk, NY, USA).

Results

Patient characteristics

A total of 450 eligible patients were enrolled in this study and subsequently divided into derivation (n=268) and validation (n=182) cohorts. The demographic, clinical, and pathological characteristics of both groups are summarized in Table 1. Comparative analysis revealed no statistically significant differences in baseline characteristics, imaging features, pathological findings, or follow-up data between the derivation and validation sets (all P values >0.05). The median follow-up time was 18.3 months. During the follow-up period, 92 out of 268 patients (34.3%) in the derivation set and 65 out of 182 patients (35.7%) in the test set experienced tumor recurrence. The median RFS for the derivation set was 16.0 months [95% confidence interval (CI): 1.5–43.0], and for the test set, it was 18.0 months (95% CI: 2.0–40.0). The optimal cutoff values for SM area, SM density, SAT area, SAT density, VAT area, and VAT density were 132.6, 37.9, 50.8, −85.7, 30.4, and −85.5, respectively. The kappa values for the six CT body composition parameters measured by the two radiologists ranged from 0.845 to 0.913 (Table S1).

Construction of the nomogram model in the derivation set

In the univariate Cox regression analysis, 12 variables were significantly associated with RFS after GC surgery. Multivariate Cox regression analysis identified five independent predictors of postoperative recurrence in GC: VAT density, visceral obesity, sarcopenia, NLR, and PNI (Table 2). These five independent predictors were integrated to construct the nomogram model (Figure 3). The total score projection ranged from 0 to 450, predicting the 1-, 2-, and 3-year RFS rates for GC patients within this range.

Figure 3 Nomogram for 1-, 2-, and 3-year RFS in gastric cancer patients after surgery. HU, Hounsfield unit; NLR, neutrophil-to-lymphocyte ratio; PNI, prognostic nutritional index; RFS, recurrence-free survival; VAT, visceral adipose tissue.

Evaluation of the nomogram model in the derivation set

The C-index of the preoperative nomogram model in the derivation set was 0.758 (95% CI: 0.706–0.811), outperforming the tumor pathological staging model, which had a C-index of 0.684 (95% CI: 0.606–0.761, P=0.10) (Table S2). tdROC curve analysis showed that the area under the curve (AUC) of the preoperative nomogram model for predicting 1-, 2-, and 3-year RFS was 0.751 (95% CI: 0.678–0.824), 0.710 (95% CI: 0.622–0.798), and 0.830 (95% CI: 0.732–0.928), respectively (Table S2). Comparative analysis revealed that the preoperative nomogram model outperformed traditional pathological tumor staging in terms of predictive accuracy at multiple time points (1-, 2-, and 3-year) in the derivation cohort (Figure 4). The calibration plots demonstrated excellent model fit, with close alignment between predicted recurrence probabilities and actual observed outcomes across the entire risk spectrum in the derivation set (Figure 5). DCA showed that the preoperative nomogram model had better clinical utility compared to tumor pathological staging (Figure 6).

Figure 4 Time-dependent areas under the receiver operating characteristic curve from 12 to 36 months. The area under the tdROC curve for predicting postoperative RFS using preoperative nomogram and tumor pathological staging. The dotted lines indicate 95% confidence intervals. AJCC, American Joint Committee on Cancer; AUC, area under the curve; RFS, recurrence-free survival; tdROC, time-dependent receiver operating characteristic.

Figure 5 Calibration plots for predicting 1-, 2-, and 3-year RFS in gastric cancer patients using the preoperative nomogram. RFS, recurrence-free survival.

Figure 6 Decision curve for predicting 1-, 2-, and 3-year RFS in gastric cancer patients using the preoperative nomogram. AJCC, American Joint Committee on Cancer; RFS, recurrence-free survival.

Evaluation of the nomogram model in the test set

In the test set, the C-index of the preoperative nomogram model was 0.761 (95% CI: 0.692–0.829), superior to the tumor pathological staging model, which had a C-index of 0.653 (95% CI: 0.582–0.724, P=0.02) (Table S3). tdROC curve analysis revealed that the AUC for predicting 1-, 2-, and 3-year RFS was 0.728 (95% CI: 0.632–0.825), 0.765 (95% CI: 0.673–0.856), and 0.795 (95% CI: 0.691–0.899), respectively (Table S3). Similarly, the preoperative nomogram model demonstrated higher predictive accuracy at different time points in the test set compared to tumor pathological staging (Figure 4). Calibration plots in the test set showed good overall agreement between the predicted probabilities and the actual outcomes (Figure 5). The preoperative nomogram model provided greater net benefit in clinical utility compared to tumor pathological staging (Figure 6).

Risk stratification based on the preoperative nomogram model

Using X-tile software, the optimal cutoff value for the nomogram score in the derivation set was determined, categorizing patients into high-risk (nomogram score >100) and low-risk (nomogram score ≤100) groups. The median RFS for the low-risk group was 38.0 months, compared to 23.5 months for the high-risk group (P<0.001). The 1-, 2-, and 3-year RFS rates for the low-risk group were 93.7%, 84.5%, and 84.5%, respectively, whereas for the high-risk group, they were 66.5%, 48.8%, and 42.3%. The nomogram scores similarly stratified patients in the test set into two distinct risk groups, with a median RFS of 33.7 months for the low-risk group and 23.2 months for the high-risk group (P<0.001) (Figure 7, Table S4).

Figure 7 RFS curve for two risk groups defined by preoperative nomogram score. RFS, recurrence-free survival.

Significant differences between the low-risk and high-risk groups were observed across multiple prognostic factors. Compared to the low-risk group, the high-risk group exhibited higher rates of LVI (derivation set: 57.1% vs. 68.8%, P=0.047; test set: 47.6% vs. 65.4%, P=0.02), perineural invasion (derivation set: 49.2% vs. 62.4%, P=0.03; test set: 52.4% vs. 69.4%, P=0.02), and poor tumor differentiation grade (derivation set: 33.3% vs. 48.1%, P=0.02; test set: 42.9% vs. 58.2%, P=0.04). Additionally, a higher proportion of patients in the high-risk group had an advanced AJCC stage (derivation set: 48.3% vs. 68.2%, P<0.001; test set: 53.6% vs. 69.4%, P=0.08) (Tables S5,S6).

Discussion

We developed and validated a nomogram model that incorporated CT body composition parameters, as well as preoperative inflammatory and nutritional indicators, to predict RFS in patients undergoing radical resection for GC. This preoperative nomogram model demonstrated superior predictive value in assessing recurrence risk compared to postoperative tumor pathological staging. It provided a useful tool for preoperative risk stratification and helped in formulating treatment strategies for patients.

Our nomogram model included three key CT body composition parameters: sarcopenia, visceral obesity, and VAT density. Sarcopenia, defined as a low SM index, is associated with prolonged hospital stays, higher postoperative complication rates, and increased mortality (18). In various cancers, such as rectal, pancreatic, and liver cancers, sarcopenia is considered an independent predictor of patient survival (19-21), consistent with our findings. The underlying mechanism may be related to the association between sarcopenia, malnutrition, and weight loss, leading to frail patients lacking the amino acids necessary for tissue repair. This impairment of physiological and repair functions results in poor clinical outcomes (22). Additionally, research suggests that myokines produced by SM may have anti-inflammatory and anti-cancer effects. Highly invasive tumors with high metabolic activity can cause significant nutritional depletion and muscle loss, with reduced secretion of myokines often correlating with poor clinical outcomes (23).

VAT refers to the fat depot located within the abdominal cavity, enveloping internal organs. This metabolically active tissue releases bioactive molecules that profoundly influence systemic immune responses, metabolic pathways, and endocrine signaling networks (24). Pathological VAT expansion creates a proinflammatory state characterized by insulin resistance and metabolic dysfunction (25). Visceral obesity has been shown to be associated with poor clinical outcomes after rectal cancer resection (26), which is consistent with our study. The radiological density of VAT can be measured by CT values. A previous study has indicated that higher VAT density is an independent risk factor for survival after GC surgery (27). Cancer cachexia can lead to the browning of white adipose tissue, where the transition from white adipose tissue (−88 to −190 HU) to brown adipose tissue (−10 to −87 HU) signifies increased VAT density (28). Inflammatory responses and underlying diseases can also lead to increased VAT density (29). Therefore, incorporating CT body composition parameters into preoperative assessments can reflect physiological reserves, metabolic characteristics, and changes in inflammatory responses, thereby providing accurate predictions of clinical outcomes.

Our study also verified NLR and PNI as independent risk factors for RFS in GC. NLR, an indicator of the systemic inflammatory response, is calculated from the ratio of neutrophils to lymphocytes. During the early stages of tumor development, the immune system is activated, causing changes in the levels of inflammatory cells such as neutrophils and lymphocytes, leading to elevated NLR levels (30). A high NLR indicates a significant decline in the cell-mediated anti-tumor immune response, shifting the tumor microenvironment toward promoting tumor growth (31). PNI, calculated from serum ALB and peripheral blood lymphocytes, is a novel systemic immuno-nutritional index that reflects the host’s immune and nutritional status (32). PNI has been used to assess the incidence of postoperative complications in patients undergoing gastrointestinal surgery (33). Higher PNI values suggest better nutritional and immune status, lower risk of tumor recurrence and deterioration, better postoperative recovery, and overall better clinical outcomes (34). Including NLR and PNI in the nomogram model highlights the significant impact of inflammatory responses and nutritional status on the prognosis of GC patients post-surgery.

The tumor node metastasis (TNM) staging system is the recognized method for risk stratification in GC and is widely used to predict the prognosis of patients with GC (35). However, tumor staging is challenging to determine preoperatively, and GC patients at the same stage may have different clinical outcomes even after receiving the same treatment due to tumor heterogeneity (36). Relying solely on tumor staging for accurate risk stratification is insufficient, necessitating new preoperative predictive models to evaluate patient’s prognosis. Previous studies have focused on assessing the impact of individual CT body composition parameters on the prognosis of GC patients (15,25,27) but have not explored the value of combining CT body composition parameters with other preoperative indicators in prognostic assessment. This study constructed a nomogram model combining CT body composition parameters with preoperative inflammatory and nutritional indicators to accurately predict postoperative RFS in GC, with a C-index of 0.758 for the derivation set and 0.761 for the test set. Compared to tumor pathological staging, our nomogram model demonstrated better predictive value.

Feng et al. (37) have constructed a preoperative risk model composed of CT morphological features to predict disease-free survival in GC patients undergoing radical surgery, achieving satisfactory predictive accuracy. However, CT morphological indicators require high image quality and are subject to significant subjective bias, making it difficult to achieve good consistency. Huang et al. (38) have developed a nomogram model combining CT radiomics scores with clinicopathological factors, showing good discrimination and calibration abilities in predicting postoperative recurrence types in GC patients. However, radiomics requires experienced radiologists to perform target delineation and feature extraction, which is time-consuming and limits its clinical applicability, especially in primary healthcare settings where doctors may lack the skills to accurately analyze tumor characteristics. In contrast to the above models, our study emphasized the crucial role of nutrition in the prognosis of GC patients. We obtained quantifiable CT body composition parameters from routine preoperative CT scans and combined these with preoperative inflammatory and nutritional indicators to construct a more comprehensive and objective nomogram model. This model offered stronger clinical applicability and practicality.

By calculating the scores from the nomogram, we stratified patients into high-risk and low-risk groups. The KM curves for both the derivation and test sets illustrated the prognostic status of patients in different risk groups, showing that high-risk patients had significantly shorter RFS times compared to low-risk patients. Our nomogram model might aid in preoperative risk stratification and the selection of appropriate treatment strategies. For high-risk patients, a combination of drugs, nutritional interventions, and exercise can contribute to improving their survival after radical resection (39). Additionally, our study found that high-risk GC patients had significantly higher incidences of LVI, perineural invasion, poor differentiation grade, and high AJCC stage, highlighting the potential histopathological mechanisms underlying our nomogram model.

Limitations

There are several limitations in this study. First, the retrospective design may introduce inherent selection bias. Second, the retrospective nature of this study necessitates confirmation of our findings through prospective, multicenter validation studies with larger cohorts. Third, whether the assessment of muscle and fat areas at the L3 vertebral level can fully represent an individual’s overall body composition requires further investigation. Fourth, to minimize the impact of preoperative confounding factors on the study results, we exclusively included patients who had not undergone chemotherapy prior to surgery. Lastly, the determination of cutoff values for body composition parameters lacks a standardized approach. The cutoff values in this study were determined based on previous literature and X-tile (version 3.6.1), without accounting for the influence of age, gender, and ethnicity, which warrants further research.

Conclusions

In summary, we have developed and validated a preoperative nomogram model incorporating sarcopenia, visceral obesity, VAT density, NLR, and PNI to predict RFS after radical resection in GC patients. This model helps predict postoperative prognosis and aid in selecting appropriate treatment strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-838/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-838/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-838/prf

Funding: This work was supported by the Nantong Health Commission Scientific Research Project (QN2022033).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-838/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was approved by the Ethics Committee of Affiliated Cancer Hospital of Nantong University (No.2023-A 07) and were performed in accordance with Declaration of Helsinki and its subsequent amendments. Due to the retrospective nature of the study, patient informed consent was waived by the Ethics Committee.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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