Development and validation of a novel modified cancer cachexia index in patients with locally advanced gastric cancer undergoing neoadjuvant chemotherapy: a multicenter cohort study.

Introduction

According to the most recent global cancer statistics for 2022, gastric cancer (GC), which has the fifth-highest incidence and mortality rates worldwide, poses a substantial global health burden [ 1 , 2 ] . Radical surgical resection remains the only effective curative approach for patients with locally advanced gastric cancer (LAGC). However, even after radical resection, these patients face a considerable risk of postoperative recurrence. With the completion of the European MAGIC [ 3 ] and FLOT4 [ 4 ] trials, it has been confirmed that perioperative chemotherapy combined with surgery improves overall survival and progression-free survival in patients with LAGC. In recent years, the RESOLVE study [ 5 ] and the PRODIGY phase III trial [ 6 ] conducted by Asian researchers have demonstrated that adding NACT before surgery enhances the prognostic benefits for patients with LAGC in Asia. Based on these results, perioperative chemotherapy has become the standard treatment for LAGC. This study follows the TITAN 2025 guidelines for transparent reporting of AI use in medical research [ 7 ]

Recently, Jafri et al [ 8 ] developed a cachexia index (CXI; CXI = SMI (cm 2 /m 2 ) × ALB (g/L)/NLR) based on the clinical features of cachexia to evaluate the prognosis of patients with non-small cell lung cancer. Their research showed that the CXI is a useful predictor of cachexia risk in patients with small-cell lung cancer, aiding the prediction of survival and treatment response. Furthermore, numerous studies [ 9 - 12 ] have demonstrated that the CXI has a significant prognostic value for patients with gastric malignancies who have not undergone NACT. The CXI comprises serum albumin (ALB) levels, neutrophil-to-lymphocyte ratio (NLR), and skeletal muscle index (SMI). Serum albumin is an indicator of disease severity and progression; low levels are associated with increased disease severity, higher progression risk, and reduced survival rates. The NLR is a simple and effective systemic inflammatory marker for assessing cancer prognosis. Thus, CXI integrates multiple assessments of nutrition, inflammation, and body composition, making it a promising predictor of cancer cachexia and prognosis.

Although patients with gastric cancer undergoing surgery alone often present varying degrees of cachexia at diagnosis, those receiving neoadjuvant therapy experience different physiological changes owing to advanced disease progression and the effects of adjuvant chemotherapy (AC), which distinguishes them from patients undergoing surgery without prior treatment. Therefore, traditional CXI may not be suitable for such patients. Gomes da Rocha et al [ 13 ] indicate that chemotherapy can exacerbate cachexia, which in turn increases chemotherapy toxicity and affects patient tolerance to both chemotherapy and surgery. However, after NACT, cachexia may improve with changes in the tumor burden. Currently, there are few reports on the impact of changes in cachexia status following NACT on patient prognosis.

Therefore, this study aimed to explore the application value of the CXI in the prognostic assessment of patients undergoing NACT using multicenter data. Modifying the traditional CXI to better fit specific characteristics of patients with LAGC treated with NACT. Through this study, we hope the modified cachexia index to enhance the effectiveness of perioperative chemotherapy in the treatment of LAGC and ultimately improve overall patient outcomes.

Materials and methods

Study design and participants

Data were collected from 600 consecutive patients diagnosed with locally advanced gastric cancer (cT2-4NanyM0) between 1 January 2010, and 30 June 2022, identified at two large tertiary hospitals. The inclusion criteria for this study were as follows: (1) locally advanced gastric cancer (cT2-4NxM0) with clinical staging before neoadjuvant chemotherapy, (2) no history of other malignant tumors, (3) no evidence of distant metastasis or invasion of adjacent organs, and (4) patients who underwent radical gastrectomy following neoadjuvant chemotherapy. The exclusion criteria were as follows: (1) history of gastric surgery; (2) acute cardiovascular disease (such as cerebrovascular or coronary artery injury) within the past three months; (3) emergency surgery; (4) active infection or severe systemic immunodeficiency; and (5) incomplete serological, computed tomography, or anthropometric data. A total of 348 patients from Fujian Medical University Union Hospital (FJMUUH) were assigned to the internal training cohort, and 49 patients from Zhangzhou Affiliated Hospital of Fujian Medical University (ZZAFJMU) were designated as the external validation cohort. A treatment flowchart is shown in Figure S1 ( http://links.lww.com/JS9/E389 ). This study has been reported in accordance with the STROCSS criteria [ 14 ] . HIGHLIGHTS The cachexia index (CXI), its effectiveness in patients with locally advanced gastric cancer (LAGC) who have undergone neoadjuvant chemotherapy (NACT) remains unclear. Analysis of 397 LAGC patients showed that mCXI outperformed traditional CXI in predicting overall survival (OS) and TRG grade. The mCXI-high group had significantly better 3-year OS (73.0% vs. 58.9%), RFS (67.7% vs. 50.2%), and DSS (74.4% vs. 62.5%). mCXI was identified as an independent prognostic factor. The different mCXI groups can selectively determine how many cycles of adjuvant chemotherapy a patient needs postoperatively. mCXI is a more accurate prognostic tool than traditional CXI and can guide postoperative chemotherapy decisions, improving survival outcomes.

All procedures were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later versions. Informed consent or substitute for it was obtained from all patients for being included in the study. This study was approved by the Institutional Review Board of Fujian Medical University Union Hospital and Zhangzhou Affiliated Hospital of Fujian Medical University. This study is registered at ClinicalTrials.gov (Identifier: NCT06689462 ).

Perioperative treatment and surgical procedures

All participants in this investigation were subjected to a minimum of two cycles of NACT. Given the diversity of perioperative chemotherapy regimens among patients, we categorized the specific chemotherapy protocols into the following groups: 1. Platinum-based regimens (platinum + capecitabine/platinum + S-1/platinum + 5-FU); 2. paclitaxel regimens (paclitaxel + capecitabine/paclitaxel + S-1/paclitaxel + 5-FU); and 3. Other regimens (paclitaxel + platinum/single agent S-1) [ 15 ] . Postoperative adjuvant chemotherapy is recommended for patients with LAGC who have a stable general condition and preserved organ function. The doses of all administered medications were determined based on instructions provided by the drug manufacturers, guidelines, and the patient’s body surface area. Experienced oncologists at each center adjusted the chemotherapy regimen based on the tumor response and treatment toxicity.

Data collection and definition

All patients who underwent hematological tests and CT images before the initiation of neoadjuvant chemotherapy and the last test before surgery were included.

Tumor regression following neoadjuvant treatment was evaluated by professional radiologists at each center using the RECIST criteria 1.1 [ 16 ] . Concurrently, senior pathologists at each center assessed the pathological response of the primary tumor based on the Becker TRG standard, categorized as follows: TRG 0 (complete response, no residual tumor cells), TRG 1 (near-complete response, only single cells or small clusters of cancer cells), TRG 2 (partial response, residual tumor cells remaining), and TRG 3 (poor response, minimal or no tumor cell death). The T and N stages were defined according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system [ 17 ] .

Measurement of the nutrition, body composition and inflammatory indicators

Body composition was measured using axial CT images at the L3 vertebral level obtained from the Picture Archiving and Communication System in Digital Imaging and Communication in Medicine format for all patients. A single CT image of the third lumbar vertebra (L3) was selected to quantify muscle and adipose tissue characteristics because this anatomical location is strongly associated with whole-body volume [ 18 , 19 ] . A single trained investigator analyzed all CT images using Slice-O-Matic software (version 5.0; Fast Vision, Montreal, Canada) [ 20 ] . The software precisely identified and quantified the cross-sectional areas of skeletal muscle (29–150 HU), visceral adipose tissue (−150 to −50 HU), and subcutaneous adipose tissue (−190 to −30 HU) using standardized Hounsfield unit (HU) ranges. Additionally, the skeletal muscle area (SMA, cm 2 ), subcutaneous adipose tissue area (SAT, cm 2 ), and visceral adipose tissue area (VAT, cm 2 ) were measured (Figure S2, http://links.lww.com/JS9/E389 ). SMI = SMA (cm 2 )/height 2 (m 2 ).

The NLR(neutrophil to lymphocyte ratio), LMR(lymphocyte to monocyte ratio), PLR(platelet to lymphocyte ratio), SII(systemic immune inflammation index), and SIRI(system inflammation response index), were calculated using the following formulas: NLR = neutrophil count/lymphocyte count; LMR = lymphocyte count/monocyte count; PLR = platelet count/lymphocyte count; SII = (neutrophil count × platelet count/lymphocyte count; and SIRI = (neutrophil count × monocyte count/lymphocyte count [ 21 - 23 ] (Table S6, http://links.lww.com/JS9/E388 ).

The calculation formulas for all corresponding dynamic change indicators (Δ) are as follows: (post-NACT indicator − pre-NACT indicator)/pre-NACT indicator [ 15 ] .

Method for constructing the modified cachexia index formula

The selected indicators in each formula were defined based on their positive or negative predictive impact on the endpoint (survival). For example, higher levels of albumin (ALB), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), the lymphocyte-to-monocyte ratio (LMR), and lymphocytes (LC) are associated with better patient prognosis. To amplify their predictive power, these indicators were placed in the numerator of the formula. Conversely, fibrinogen (FIB) and platelets (PLT), which are associated with poorer prognosis when elevated, were placed in the denominator to enhance the overall predictive efficiency. As the values of the positive predictors (numerator) increase and the negative predictors (denominator) decrease, the resulting mCXI value increases, indicating a better nutritional status and stronger predictive power for the endpoint. Conversely, when the positive predictors decrease and the negative predictors increase, the mCXI value decreases, indicating poorer nutritional status and weaker predictive power. The formulation used in this study aligns with the conceptual framework of traditional CXI indicators.

Modified cachexia index and traditional cachexia index

The random forest model (Supplementary Statistical Material 1, http://links.lww.com/JS9/E387 ) was used to rank the importance of inflammatory, nutritional, and body composition indicators at different time points. Modified cachexia index were developed based on the dynamic changes in inflammation, nutritional status, and body composition before, during and after neoadjuvant chemotherapy (Fig. 1 A–C). The formulae are as follows: Figure 1. Selection of inflammation indicators, nutrition indicators, and body composition using the random forest model. This figure presents the importance ranking of inflammatory indicators, nutritional indicators, and body composition at different time points using random forests. Panel A shows the selection of pre-neoadjuvant indicators, panel B shows the selection of post-neoadjuvant indicators, and panel C show the selection of dynamic changes in indicators during neoadjuvant chemotherapy. The formulas are as follows: pre-NACT mCXI = (pre-SAT × pre-LMR)/pre-FIB; post-NACT mCXI = (post-SAT × post-ALB)/post-PLT; Δ-NACT mCXI = ΔVAT × ΔALB × ΔLC and traditional CXI = (SMI × ALB)/ NLR.

Pre-NACT CXI = pre-SAT (cm 2 ) × pre-LMR/pre-FIB (g/L)

Post-NACT CXI = post-SAT (cm 2 ) × post-ALB (g/L)/post-PLT

Dynamic change during NACT: ΔCXI = ΔVAT × ΔALB × ΔLC

The traditional cachexia index was calculated as follows:

Traditional CXI = SMI (cm 2 /m 2 ) × ALB (g/L)/NLR [ 9 , 24 ]

Primary and secondary outcome

The primary outcome is overall survival (OS), and the secondary outcomes were recurrence-free survival (RFS) and disease-specific survival (DSS). OS was defined as the time from the end of surgery to death. RFS was defined as the time from the end of surgery to the first recorded instance of cancer recurrence, metastasis, or occurrence of a new cancer. DSS was defined as the time from the end of surgery to death, specifically due to tumor recurrence or metastasis.

Local-regional recurrence refers to tumors clinically confirmed to have recurred in the residual stomach, anastomosis, or surgical site. Multisite recurrence involves simultaneous recurrence at two or more metastatic sites such as the peritoneum, liver, lungs, bones, brain, distant lymph nodes, or other hematogenous metastatic sites. Other site recurrences include those occurring in locations other than the liver, such as the lungs, bones, brain, pancreas, ovaries, adrenal glands, and rectum. Patients with lymph node recurrence or recurrence at unspecified locations were also included.

Postoperative follow-up strategy

The outpatient review included physical examination, laboratory evaluation, chest radiography, abdominal computed tomography, and ultrasound. Additionally, an annual endoscopy is advised to evaluate the anastomosis site and residual stomach, with a biopsy performed as needed to exclude local recurrence. Patients were followed up once every 3–6 months within the first 2 years, every 6–12 months within the next 3–5 years, and once every year after 5 years. The time of follow-up evaluation could be advanced, and the frequency could be increased depending on the patients’ specific situation.

Statistical analyses

Continuous variables were presented as mean ± standard deviation (SD) if they followed a normal distribution; otherwise, they were reported as median (interquartile range, IQR). Chi-square tests or Fisher’s exact tests were used to analyze categorical variables. The randomForest package in R (version 4.3.2) was employed to evaluate the predictive importance of variables for overall survival (OS). The model was configured with parameters ntree = 1000 and mtry = √p (where p denotes the total number of variables), ensuring stable estimation of feature importance across multiple iterations. A 5-fold cross-validation approach was applied to assess model stability, and the average error rate was reported. Variable importance was ranked based on two metrics: “ Mean Decrease in Accuracy ” and “ Mean Decrease Gini .” A random forest regression algorithm was employed to rank and select the importance of continuous prognostic variables. Associations between various factors were evaluated using a heatmap with color encoding to illustrate the degree of correlation. The “ ggcoxzph ” function was used to plot residuals, and the “ ggcoxfunctional ” function was employed to test for linearity between continuous variables and the log hazard in the Cox model (Supplementary Statistical Material 2, http://links.lww.com/JS9/E387 ). Cox proportional hazard models with robust standard errors were used for univariate and multivariate analyses to identify independent predictors of overall survival. The area under the curve (AUC) was computed with a 95% confidence interval based on 2000 stratified bootstrap replicates. Restricted cubic splines (RCS) were used to evaluate the relationship between postoperative adjuvant chemotherapy cycles and OS in patients with LAGC (Supplementary Statistical Material 3, http://links.lww.com/JS9/E387 ). Data analysis was performed using SPSS (version 25; IBM, Armonk, NY, USA) and R 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at P <0.05.

Results

Selection of the modified cachexia index

In the training cohort, ROC curves were used to compare the predictive performance of the traditional CXI for OS and TRG2/3. The results indicated that the AUC values for CXI before, after, and during dynamic changes throughout NACT were as follows: OS (0.504 vs. 0.524 vs. 0.543, respectively) and TRG2/3 (0.519 vs. 0.530 vs. 0.501, respectively) (Figure S3, http://links.lww.com/JS9/E389 ).

While, mCXI demonstrated superior predictive power for OS compared to traditional CXI across different time points, as assessed by ROC curves (AUC pre-nact mCXI vs. AUC pre-nact traditional CXI = 0.607 vs. 0.504; AUC post-nact mCXI vs. AUC post-nact traditional CXI = 0.638 vs. 0.524; AUC Δnact mCXI vs. AUC Δnact traditional CXI = 0.544 vs. 0.543) (Fig. 2 A–C). Similarly, for predicting TRG 2/3, the mCXI outperformed the traditional CXI across all time points (AUC pre-nact mCXI vs. AUC pre-nact traditional CXI = 0.561 vs. 0.519; AUC post-nact mCXI vs. AUC post-nact traditional CXI = 0.605 vs. 0.530; AUC Δnact mCXI vs. AUC Δnact traditional CXI = 0.502 vs. 0.501) (Fig. 2 D–F). Figure 2. Comparison of receiver operating characteristic (ROC) curves between modified cancer cachexia index and traditional cancer cachexia index in predicting postoperative TRG grade and overall survival. Panels A-C compare the predictive abilities of pre-neoadjuvant, post-neoadjuvant, and dynamically changing mCXI and traditional CXI at different time points for postoperative overall survival. Panels D-F compare the predictive abilities of pre-neoadjuvant, post-neoadjuvant, and dynamically changing mCXI and traditional CXI at different time points for postoperative TRG grade.

Notably, post-NACT mCXI exhibited higher AUC values for predicting OS and TRG grade than both pre-NACT mCXI and ΔmCXI as well as any individual inflammatory, nutritional, or body composition parameters at any time point (Tables S2–S4, http://links.lww.com/JS9/E388 ). Consequently, this study used post-NACT mCXI as mCXI for further analyses. Patients were stratified into mCXI-low (mCXI < 17) and mCXI-high (mCXI ≥ 17) groups using the “Maxtast” function in R software (Figure S4, http://links.lww.com/JS9/E389 ).

Patient characteristics

Comparisons of the patient characteristics are shown in Table 1 . In the training cohort, patients were classified into mCXI-low (245/348) and mCXI-high (103/348) groups. Age distribution was comparable between the groups (median 63 years, IQR [56–69] vs. median 65 years, IQR [58–69]; P = 0.424). The mCXI-low group had a significantly higher proportion of male patients (196 [80%] vs. 64 [62.14%]; P < 0.001). Additionally, patients in the mCXI-low group had a lower BMI (median 21.3, IQR [19.4–23.6] vs. median 24.2, IQR [22.2–25.9]; P < 0.001), more advanced ypT staging (ypT3 low vs. ypT3 high = 130 (53.06) vs 49 (47.57); ypT4 low vs. ypT4 high = 59 (24.08) vs. 13 (12.62); P < 0.001), higher TRG grades (TRG2/3 low vs. TRG2/3 high = 188 (76.73%) vs. 68 (66.02); P = 0.039), and poorer RECIST response (SD/PD low vs. SD/PD high = 149 (60.82) vs. 42 (40.78); P < 0.001). Further comparisons revealed significant differences in certain inflammatory indicators and nutritional indicators between the groups (Figures S5–S7, http://links.lww.com/JS9/E389 ). However, no statistically significant differences were observed in other clinicopathological variables. The baseline characteristics of the external validation cohort were similar to those of the internal training cohort, which also showed significantly lower BMI, higher TRG grading, and poorer RECIST response in mCXI-low group (Table 1 ). Table 1 Clinicopathological characteristics at staging by modified cachexia index Characteristic FJMUUH (training cohort) ZZAFJMU (validation cohort) Overall, N = 348 a mCXI-low N = 245 a mCXI-high N = 103 a P value Overall, N = 49 a mCXI-low N = 28 a mCXI-high N = 21 a P value Age 64 (57, 69) 63 (56, 69) 65 (58, 69) 0.424 b 66 (59, 71) 65 (59, 71) 67 (61, 70) 0.627 b Sex <0.001 c 0.201 c Male 260 (74.71%) 196 (80.00%) 64 (62.14%) 35 (71.43%) 22 (78.57%) 13 (61.90%) Female 88 (25.29%) 49 (20.00%) 39 (37.86%) 14 (28.57%) 6 (21.43%) 8 (38.10%) BMI (kg/m 2 ) 22.2 (20.2, 24.3) 21.3 (19.4, 23.6) 24.2 (22.2, 25.9) <0.001 b 21.5 (19.4, 22.9) 20.6 (18.5, 22.2) 22.5 (21.5, 25.4) 0.004 b ASA 0.776 c 0.559 d 1 27 (7.76%) 20 (8.16%) 7 (6.80%) 13 (26.53%) 9 (32.14%) 4 (19.05%) 2 265 (76.15%) 184 (75.10%) 81 (78.64%) 29 (59.18%) 15 (53.57%) 14 (66.67%) 3 56 (16.09%) 41 (16.74%) 15 (14.56%) 7 (14.29%) 4 (14.29%) 3 (14.28%) cT (AJCC 8th) 0.619 d 0.398 c T2 13 (3.74%) 8 (3.26%) 5 (4.86%) 12 (24.49%) 5 (17.86%) 7 (33.33%) T3 52 (14.94%) 35 (14.29%) 17 (16.50%) 19 (38.78%) 11 (39.28%) 8 (38.10%) T4 283 (81.32%) 202 (82.45%) 81 (78.64%) 18 (36.73%) 12 (42.86%) 6 (28.57%) cN (AJCC 8th) 0.848 c 0.055 c N0 32 (9.20%) 23 (9.39%) 9 (8.74%) 12 (24.49%) 4 (14.29%) 8 (38.10%) N + 316 (90.80%) 222 (90.61%) 94 (91.26%) 37 (75.51%) 24 (85.71%) 13 (61.90%) ypT (AJCC 8th) 0.006 c 0.777 d T0 27 (7.76%) 14 (5.72%) 13 (12.62%) 4 (8.16%) 3 (10.71%) 1 (4.76%) T1 33 (9.48%) 22 (8.98%) 11 (10.68%) 6 (12.24%) 2 (7.14%) 4 (19.05%) T2 37 (10.63%) 20 (8.16%) 17 (16.50%) 2 (4.09%) 1 (3.57%) 1 (4.76%) T3 179 (51.44%) 130 (53.06%) 49 (47.57%) 9 (18.37%) 5 (17.86%) 4 (19.05%) T4 72 (20.69%) 59 (24.08%) 13 (12.63%) 28 (57.14%) 17 (60.72%) 11 (52.38%) ypN (AJCC 8th) 0.178 c 0.507 d N0 141 (40.52%) 92 (37.55%) 49 (47.57%) 18 (36.73%) 8 (28.57%) 10 (47.62%) N1 69 (19.83%) 47 (19.18%) 22 (21.36%) 9 (18.37%) 6 (21.43%) 3 (14.29%) N2 55 (15.80%) 41 (16.74%) 14 (13.59%) 14 (28.57%) 8 (28.57%) 6 (28.57%) N3 83 (23.85%) 65 (26.53%) 18 (17.48%) 8 (16.33%) 6 (21.43%) 2 (9.52%) NACT cycles 4.00 (3.00, 4.00) 4.00 (2.00, 4.00) 4.00 (3.00, 4.00) 0.729 b 3.00 (3.00, 3.00) 3.00 (3.00, 3.00) 3.00 (3.00, 3.00) 0.646 b AC cycles 4.00 (2.00, 6.00) 4.00 (2.00, 6.00) 4.00 (2.00, 4.50) 0.074 b 2.00 (1.00, 4.00) 3.00 (1.75, 4.00) 1.00 (1.00, 4.00) 0.113 b AC 0.756 c 0.301 d No 26 (7.47%) 19 (7.76%) 7 (6.80%) 4 (8.16%) 1 (3.57%) 3 (14.29%) Yes 322 (92.53%) 226 (92.24%) 96 (93.20%) 45 (91.84%) 27 (96.43%) 18 (85.71%) Tumor size (cm) 4.00 (3.00, 6.00) 4.50 (3.00, 6.00) 4.00 (3.00, 5.00) 0.074 b 4.50 (3.50, 5.60) 4.50 (3.50, 5.70) 4.50 (3.50, 5.00) >0.999 b Tumor location 0.250 c >0.999 d Upper 1/3 139 (39.94%) 90 (36.73%) 49 (47.57%) 20 (40.82%) 11 (39.28%) 9 (42.85%) Middle 1/3 63 (18.10%) 46 (18.78%) 17 (16.50%) 10 (20.41%) 6 (21.43%) 4 (19.05%) Lower 1/3 113 (32.47%) 86 (35.10%) 27 (26.21%) 9 (18.37%) 5 (17.86%) 4 (19.05%) Mixed 33 (9.48%) 23 (9.39%) 10 (9.71%) 10 (20.41%) 6 (21.43%) 4 (19.05%) Differentiation 0.190 c 0.600 c Well/moderate 126 (36.21%) 95 (38.78%) 31 (30.10%) 16 (32.65%) 8 (28.57%) 8 (38.10%) Poor/undifferentiated 192 (55.17%) 132 (53.88%) 60 (58.25%) 31 (63.27%) 18 (64.29%) 13 (61.90%) Unknown 30 (8.62%) 18 (7.34%) 12 (11.65%) 2 (4.08%) 2 (7.14%) 0 (0.00%) Lymphovascular invasion 0.245 c 0.100 c No 210 (60.34%) 143 (58.37%) 67 (65.05%) 17 (34.69%) 7 (25.00%) 10 (47.62%) Yes 138 (39.66%) 102 (41.63%) 36 (34.95%) 32 (65.31%) 21 (75.00%) 11 (52.38%) Perineural invasion 0.666 c 0.325 c No 183 (52.59%) 127 (51.84%) 56 (54.37%) 15 (30.61%) 7 (25.00%) 8 (38.10%) Yes 165 (47.41%) 118 (48.16%) 47 (45.63%) 34 (69.39%) 21 (75.00%) 13 (61.90%) TRG grade 0.039 c 0.055 c 0/1 92 (26.44%) 57 (23.27%) 35 (33.98%) 12 (24.49%) 4 (14.29%) 8 (38.10%) 2/3 256 (73.56%) 188 (76.73%) 68 (66.02%) 37 (75.51%) 24 (85.71%) 13 (61.90%) RECIST criteria <0.001 b 0.005 c CR/PR 157 (45.11%) 96 (39.18%) 61 (59.22%) 37 (75.51%) 17 (60.71%) 20 (95.24%) SD/PD 191 (54.89%) 149 (60.82%) 42 (40.78%) 12 (24.49%) 11 (39.29%) 1 (4.76%) LOS (days) 8.0 (7.0, 11.0) 8.0 (7.0, 11.0) 8.0 (7.0, 10.0) 0.113 b 7.00 (6.00, 8.00) 7.00 (6.00, 8.00) 7.00 (7.00, 8.00) 0.834 b Operation duration (min) 180 (160, 221) 180 (157, 220) 180 (169, 225) 0.648 b 225 (202, 250) 220 (200, 251) 229 (206, 245) 0.486 b Intraoperative blood loss (ml) 40 (30, 50) 50 (30, 60) 35 (30, 50) 0.359 b 50 (30, 50) 50 (45, 50) 50 (30, 50) >0.999 b Bold values indicate that the P value is statistically significant. AC, adjuvant chemotherapy; ASA, American Society of Anesthesiologists Physical Status Classification System; BMI, body mass index; CR, complete response; LOS, length of stay; NACT, neoadjuvant chemotherapy; PD, progressive disease; PR, partial response; SD, stable disease; TRG, tumor regression grade. a Median (IQR); b Wilcoxon rank sum test; c Pearson’s chi-squared test; d Fisher’s exact test.

Association of modified cachexia index with clinicalpathological characteristic and long-term survival outcomes

In the training cohort, the heatmap revealed no significant interactions between mCXI and other clinicopathological variables (Figure S8A, http://links.lww.com/JS9/E389 ). Moreover, Kaplan-Meier survival analysis was performed to assess survival differences across mCXI subgroups, indicating that: the mCXI-low group showed a significantly lower 3-year OS rate (58.9% vs. 73.0%; P = 0.002), 3-year RFS rate (50.2% vs. 67.7%; P = 0.002), and 3-year DSS rate (62.5% vs. 74.4%; P = 0.012) (Fig. 3 A–C). Additionally, adjusted Cox proportional hazards model analysis showed that mCXI was an independent prognostic factor for OS (HR: 0.62, 95% CI [0.35–0.92]; P = 0.044) (Fig. 4 ), but not for RFS (HR: 0.73, 95% CI [0.48–1.11]) or DSS (HR: 0.82, 95% CI [0.51–1.31]) (Figure S9A and B, http://links.lww.com/JS9/E389 ). Figure 3. Kaplan-Meier survival curves comparing overall survival (OS), recurrence-free survival (RFS), and disease-specific survival (DSS) between mCXI-low and mCXI-high subgroups. Panels A–C represent the internal training cohort, and panels D–F represent the external validation cohort. Survival differences were calculated using the Kaplan-Meier method and compared using the log-rank test. The mCXI-high subgroup demonstrated significantly better 3-year OS, RFS, and DSS in the internal cohort, and consistent trends were observed in the validation cohort. These findings highlight the clinical relevance of mCXI in stratifying patient prognosis after neoadjuvant chemotherapy and guiding individualized postoperative management for locally advanced gastric cancer. Figure 4. Univariate and multivariate Cox regression analyses associated with overall survival. This figure shows the correlation between patients’ clinicopathological characteristics and overall survival. The univariate analysis identifies whether the variables are prognostic factors for postoperative survival, and all variables are then included in the Cox regression model for adjustment to select independent prognostic factors.

To further investigate the prognostic value of mCXI across different chemotherapy regimens, patients within both the mCXI-low and mCXI-high subgroups were stratified into four chemotherapy groups: Paclitaxel-based, platinum-based, Paclitaxel plus platinum based, and other regimens. Survival analyses showed no significant differences in OS or RFS among the different chemotherapy regimens within either subgroup (all P > 0.05, Figure S13A–D, http://links.lww.com/JS9/E389 ).

Recurrence pattern and peak recurrence survival time

By the time of the last follow-up, in the internal training cohort, recurrence was observed in 129 patients (52.6%) and 34 patients (33.0%) in the mCXI-low and mCXI-high groups, respectively (Table 2 ). The difference in the recurrence rates between the two groups was statistically significant ( P = 0.001). Peritoneal recurrence is the most common recurrence. However, no significant statistical differences were observed between the groups in recurrence rates for any recurrence type, including locoregional [14 (10.8%) vs. 4 (11.7%); P = 0.880], liver [17 (13.2%) vs. 3 (9%); P = 0.491], multiple-sites [24 (18.6%) vs. 6 (17.6%); P = 0.898], and other-sites (bone, brain, lung, distant lymph nodes, pancreas, adrenal gland, etc.) [37 (28.7%) vs. 14 (41.2%); P = 0.162]. Table 2 Recurrence pattern mCXI-low (n = 245,%) mCXI-high (n = 103,%) P value a Recurrence b 129(52.6%) 34(33%) 0.001 Recurrence location Locoregional 14(10.8%) 4(11.7%) 0.880 Peritoneal Metastasis 37(28.7%) 7(20.5%) 0.344 Liver 17(13.2%) 3(9%) 0.491 Multiple sites c 24(18.6%) 6(17.6%) 0.898 Others 37(28.7%) 14(41.2%) 0.162 Bone 8 3 - Brain 5 2 - Distant LN 6 4 - Lung 2 2 - Other sites d 16 3 - Bold values indicate that the P value is statistically significant. a P for Pearson’s chi-squared test. b Refers only to first-time recurrence, even though patients can have recurrences at multiple times. c Includes patients who have recurrence simultaneously in 2 or more metastatic sites, including peritoneum, liver, lung, bone, brain, distant lymph node, or other hematogenous metastatic sites. d Including pancreas, ovaries, adrenal glands, rectum, etc.

Further analysis demonstrated a significant later in the time to the first recurrence peak in the mCXI-high group compared to the mCXI-low group (33.51 months vs. 7.11 months) (Fig. 5 B). Additionally, comparing the time to the first recurrence peak across different sites revealed that locoregional recurrences had the earliest peak at 5.87 months. In contrast, multiple-site recurrences exhibited a later peak at 11.11 months. The peak times for peritoneal, liver, distant lymph node, and lung recurrence ranged from 6.28 to 11.08 months (Fig. 5 A). Figure 5. Peak time of recurrence. Panel A shows the peak time of first recurrence at different recurrence sites in the training cohort. Panel B shows the peak time of first recurrence in the mCXI-low and mCXI-high groups in the training cohort. Panel C shows the peak time of first recurrence in the mCXI-low and mCXI-high groups in the validation cohort.

External validation

In the external validation cohort, the correlation between the mCXI and other clinical variables was consistent with findings from the internal training cohort, demonstrating relative independence (Figure S8B, http://links.lww.com/JS9/E389 ). mCXI was significantly associated with postoperative OS and RFS, with mCXI-high patients showing better OS and RFS than mCXI-low patients (P for OS = 0.041; P for RFS = 0.013) (Fig. 3 D and E). However, the association with DSS was only marginally significant ( P = 0.074) (Fig. 3 F). Additionally, in the validation cohort, the peak time of recurrence was consistent with that in the internal cohort, with mCXI-high patients experiencing delayed peak recurrence compared with mCXI-low patients (19.69 months vs. 16 months) (Fig. 5 C).

Prediction of postoperative adjuvant chemotherapy effects by modified cachexia index

In the entire cohort, RCS analysis revealed a nonlinear relationship between the number of postoperative AC cycles and OS ( P = 0.0245) (Figure S10, http://links.lww.com/JS9/E389 ). Based on this, AC cycles were categorized into three groups: fewer than four cycles, four to six cycles, and more than six cycles (Table S5, http://links.lww.com/JS9/E388 ).

Subgroups analysis according to the different AC cycles in the mCXI-low group revealed that there was no significant survival difference in OS of different postoperative chemotherapy cycles (3-year OS: AC cycle < 4 vs. 4 ≤ AC cycle ≤6 vs. AC>6 = 50.7% vs. 65.7% vs. 51.8%; P = 0.165) (Figure S11A, http://links.lww.com/JS9/E389 ). While, for the ypStage III in mCXI-low patients who received four or more cycles of postoperative chemotherapy had significantly better 3-year OS than those who received fewer (3-year OS: AC cycle ≥ 4 vs. AC cycle <4 = 43.7% vs. 24.2%; P = 0.005) (Figure S12C and D, http://links.lww.com/JS9/E389 ). Similar results were not observed in the ypCR/ypI and ypII groups (all P > 0.05) (Figure S12A and B, http://links.lww.com/JS9/E389 ).

However, in the mCXI-high group of the entire cohort, patients who received 4-6 cycles of AC had a significantly better 3-year OS compared to those who received fewer than 4 cycles or more than 6 cycles (3-year OS: < 4 cycles vs. 4-6 cycles vs. > 6 cycles = 65.6% vs. 87.4% vs. 50%; P = 0.005) (Figure S11B, http://links.lww.com/JS9/E389 ).

Discussion

This study proposes a novel mCXI as a potential marker of nutritional status. It evaluates its prognostic value and utility in guiding postoperative adjuvant chemotherapy decisions in patients undergoing NACT for LAGC. The mCXI, a composite index, incorporates post-NACT inflammation, nutrition, and body composition indicators. We observed that patients in the mCXI-low group had significantly worse comprehensive survival outcomes than those in the mCXI-high group. This negative effect remained evident even after adjusting for known confounders. Despite comparable recurrence rates at various sites, patients with a low mCXI experienced an earlier peak in the time to first recurrence following NACT. Further analysis revealed that in the mCXI-low group, patients with ypIII stage disease who received more than four cycles of adjuvant chemotherapy had improved survival outcomes compared to those receiving fewer cycles. However, a significant difference in overall prognosis between patients receiving four to six cycles and those receiving fewer or more cycles was observed in the mCXI-high group. These findings suggest that the mCXI could serve as a simple, objective, and effective approach for assessing prognosis and guiding individualized postoperative AC recommendations in patients with LAGC.

Fat loss is a hallmark of cancer cachexia [ 27 ] , and studies have shown that reductions in adipose tissue exacerbate energy metabolism disorders, impair quality of life, and shorten patient survival [ 28 , 29 ] . Recently, numerous studies have confirmed the prognostic relevance of subcutaneous adipose tissue (SAT) across multiple cancer types, including breast cancer [ 30 ] , esophageal cancer [ 31 ] , pancreatic cancer [ 32 ] , and multiple myeloma [ 33 ] . A recent study focusing on gastric cancer patients also found significantly lower survival rates among those with low SAT compared to those with higher SAT, suggesting the potential value of SAT in cancer cachexia risk assessment [ 34 ] . Moreover, ALB is the sole biochemical parameter consistently included in various nutritional scoring systems such as the Nutritional Risk Index (NRI), Prognostic Nutritional Index (PNI), and CONUT score, underscoring its stability and applicability. In our study, ALB demonstrated superior prognostic value compared to other nutritional markers. Recent studies have highlighted that platelets play critical roles beyond hemostasis, including tumor progression, angiogenesis, immune evasion, and metastasis [ 26 , 35 ] . Under cancer cachexia conditions, tumor cells can stimulate the release of pro-inflammatory cytokines such as interleukin-6 (IL-6), which in turn activate platelets and immune cells, triggering a cascade of inflammatory responses that lead to increased lipolysis and muscle wasting [ 36 , 37 ] . Furthermore, platelets can actively secrete inflammatory mediators, exacerbating cachexia progression; clinically, thrombocytosis is often associated with advanced tumors, high tumor burden, and poor prognosis [ 38 , 39 ] . Based on the above mechanisms and clinical evidence, our study incorporated these key indicators – subcutaneous adipose tissue (SAT), albumin (ALB), and platelet count (PLT) – into the mCXI model to more comprehensively and accurately assess the prognostic risk of cancer cachexia patients following neoadjuvant chemotherapy.

However, in the training cohort, the overall AUC values of mCXI for predicting OS and TRG2/3 were relatively low, which may raise concerns about its discriminative performance. This could be closely related to its intended function. mCXI was designed as a prognostic stratification tool to aid in multifactorial risk assessment, rather than as an independent diagnostic marker. In patients with locally advanced gastric cancer receiving neoadjuvant chemotherapy, prognosis is influenced by multiple factors, making it difficult for a single indicator to achieve high discriminative accuracy. Therefore, moderately low AUC values are common in similar studies. For instance, Wang et al [ 40 ] recently proposed the Systemic Nutrition-Inflammation Index (SNII), which, despite outperforming several traditional indices, had an AUC range of only 0.534–0.612. This reflects the inherent limitations of using nutritional and inflammatory indicators to predict outcomes in complex disease settings. Moreover, mCXI consistently showed predictive advantages over traditional CXI across multiple time points and endpoints, and this trend was validated in an external cohort, indicating its strong stability and generalizability.

Jafri et al [ 8 ] introduced the Cancer Cachexia Index (CXI) as a composite measure of various clinical characteristics of cachexia. Their research highlighted that patients with metastatic non-small cell lung cancer exhibiting a low CXI had reduced overall and progression-free survival rates. Similar negative survival outcomes have been documented for other cancer types. Gong et al [ 10 ] and Sakurai et al [ 12 ] assessed the CXI in patients undergoing surgical resection for gastric cancer and found that a low CXI was associated with poorer survival. However, unlike these studies, our mCXI incorporates neoadjuvant chemotherapy-specific factors, providing a more tailored approach for LAGC patients.

Historically, the primary focus in cancer cachexia theory has been muscle loss, which is considered a key complication of this condition. To date, research on cachexia has predominantly focused on the muscle tissue [ 24 , 41 , 42 ] . However, Argilés et al [ 43 ] highlighted the significant role of non-muscle tissue and intertissue communication in cancer cachexia, leading to the exploration of new therapeutic strategies. Furthermore, Kays et al [ 28 ] demonstrated that in patients with advanced pancreatic ductal adenocarcinoma (PDAC) undergoing neoadjuvant therapy, fat loss alone had a prognostic value equivalent to that of muscle loss. Their research underscores the critical role of adipose tissue in cachexia and PDAC mortality. In our study, random forest analysis revealed that SAT had greater prognostic relevance than SMI in patients with neoadjuvant gastric cancer. This pattern may reflect the rapid metabolic demands placed on adipose reserves during chemotherapy, which precedes muscle catabolism, especially in patients with advanced disease. Consequently, the SAT may be a more significant prognostic indicator than the SMI. This pattern may reflect the rapid metabolic demands placed on adipose reserves during chemotherapy, which precedes muscle catabolism, especially in patients with advanced disease.

Tumor-associated coagulopathies are commonly observed in patients with malignancies, and both fibrinogen (FIB) and platelets (PLT) are critical components of the coagulation system [ 44 ] . Fibrinogen is an acute-phase protein synthesized by the liver, and its levels increase during systemic inflammation. Sustained chronic inflammation may accelerate multi-organ dysfunction and is closely associated with tumor progression [ 45 ] . Previous studies have demonstrated that elevated fibrinogen levels are not only associated with tumor stage, angiogenesis, and metastasis, but also promote tumor cell adhesion, immune escape, and treatment resistance in gastrointestinal and lung cancers [ 25 , 46 , 47 ] . Cancer cachexia is often accompanied by systemic inflammation and coagulation abnormalities. Platelets, as key mediators of both inflammation and coagulation, may be influenced by nutritional status, which in turn affects tumor behavior and progression [ 26 ] . In gastrointestinal tumors, platelet activation is frequently observed, driven by the prothrombotic tumor microenvironment. Moreover, platelets can facilitate tumor angiogenesis via the release of vascular endothelial growth factor (VEGF), and promote metastasis through direct interaction with tumor cells [ 35 ] .

Moreover, the tumor immune-inflammatory microenvironment contributes significantly to cancer progression [ 48 - 50 ] . Hanahan et al [ 51 ] demonstrated that immune and inflammatory cells affect tumor growth through cytokines and chemokines produced via autocrine and paracrine signaling. The high metabolic rate and abnormal proliferation of cancer cells make patients more susceptible to malnutrition, leading to loss of muscle mass, fat, and overall body weight. Shi et al [ 11 ] revealed that malnutrition can adversely affect the immune-inflammatory microenvironment, leading to an imbalance between immune suppression and tumor proliferation. This imbalance ultimately leads to a vicious cycle of “malnutrition-immune suppression-tumor progression” in patients. Previous research on cachexia indicators for non-small cell lung cancer [ 8 ] and diffuse large B-cell lymphoma [ 24 ] employed the NLR as a key inflammatory marker in traditional CXI models to predict patient outcomes, demonstrating significant predictive accuracy. In our study of patients with LAGC undergoing NACT, we found that the PLT count after neoadjuvant treatment offered superior prognostic value compared to the NLR and had greater clinical applicability. This observation is consistent with the findings of Shi et al , who showed that platelet count (PLT) had a more independent association with overall prognosis in patients with gastrointestinal cancer than the NLR and other markers [ 52 ] .

Prospective studies on perioperative chemotherapy have consistently demonstrated significant improvements in the 3- and 5-year survival rates of patients with locally advanced gastric cancer compared with those undergoing surgery alone [ 53 - 57 ] . However, in patients with advanced gastric cancer and poor nutritional status, rapid tumor progression coupled with disease-related wasting and chemotherapy toxicity exacerbates the decline in physical function and nutritional status. Without timely nutritional interventions and targeted adjuvant therapies, these patients are at a heightened risk of poor clinical outcomes. Although the therapeutic and toxic effects of perioperative chemotherapy are pivotal, conflicting factors influence patient prognostic outcomes. Therefore, tailoring the postoperative adjuvant treatment for individuals with neoadjuvant-treated gastric cancer has emerged as a critical challenge in current clinical practice. In our study, we found that for mCXI-low patients with postoperative pathological stage ypIII, a minimum of four cycles of adjuvant chemotherapy was required to achieve optimal overall survival outcomes. For patients in the mCXI-high group with better prognoses, receiving 4-6 cycles of adjuvant chemotherapy significantly improved overall survival. However, since the number of patients in this group receiving more than 6 cycles of chemotherapy was relatively small, further investigation is needed to determine whether extended chemotherapy cycles provide additional benefits. Consequently, clinicians should consider the nutritional status of these patients when planning postoperative adjuvant chemotherapy to avoid overtreatment. The application of the mCXI in identifying individual differences among patients with neoadjuvant-treated gastric cancer and guiding targeted therapeutic strategies may be crucial for optimizing comprehensive care. Notably, this finding has not been reported previously.

Several limitations should be acknowledged. First, as a retrospective study, it was inherently susceptible to recall and selection biases. However, the prospectively collected data and an external validation cohort may have reduced this type of bias. Additionally, owing to data limitations, the toxicities experienced during chemotherapy and dose adjustments were not collected, resulting in a lack of analysis of adverse reactions during these periods. Finally, although this study conducted a comprehensive assessment of body composition using a substantial and complete dataset of pre- and post-NACT CT images, the relatively small sample size remains a limitation. Future studies should focus on validating these findings in larger, multi-center cohorts and incorporate real-time toxicity data to refine mCXI’s utility in guiding chemotherapy regimens.

Conclusion

This multicenter cohort study demonstrated that the modified Cancer Cachexia Index (mCXI), which integrates post-NACT nutritional, inflammatory, and body composition parameters, is a strong prognostic tool for patients with LAGC. The mCXI showed better performance than the traditional CXI in predicting long-term outcomes and provided useful insights for guiding adjuvant chemotherapy decisions. Its clinical application may improve individualized treatment and optimize prognosis in this population.

What this study adds?

Proposes a modified Cancer Cachexia Index (mCXI) adapted to the peri-neoadjuvant chemotherapy (NACT) setting in locally advanced gastric cancer (LAGC). Demonstrates that nutritional-inflammatory predictors differ before and after NACT, supporting the need for stage-specific modeling. Shows that mCXI consistently outperforms traditional CXI in predicting both overall survival and treatment response across internal and external cohorts. Highlights the biological relevance of SAT, ALB, and PLT in cancer cachexia and prognosis.

Acknowledgements

We thank who have devoted a lot to this study, including nurses, pathologists, further-study doctors, statisticians, reviewers and editors. Thanks for Dr. Zhi-Hong Huang, Public Technology Service Center, Fujian Medical University. Feng-Qiong Liu, Experimental Center of School of Public Health, Fujian Medical University. The authors acknowledge the use of ChatGPT (OpenAI, San Francisco, CA, USA) solely for English language editing during the manuscript preparation. No AI tools were used for data analysis, clinical interpretation, or any part of the scientific content. All authors take full responsibility for the integrity and accuracy of the work.

Ling-Kang Zhang and Hua-Long Zheng contributed equally to this work and should be considered co-first authors.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.lww.com/international-journal-of-surgery .

Published online 20 June 2025

Ethical approval

The study was approved by the Institutional Review Board at the Fujian Medical University Union Hospital and Zhangzhou Affiliated Hospital of Fujian Medical University (2024KY039).

Consent

Participants were informed of the study's purpose, procedures, potential risks and benefits, and their right to withdraw from the study at any time without penalty. Data collected were kept confidential and used solely for research purposes. Patients signed informed consent regarding publishing their data.

Sources of funding

This study was supported by the Fujian Province Medical “Creating high-level hospitals, high-level medical centers and key specialty projects” (MWYZ [2021] No. 76), Fujian provincial health technology project (2022QNA026) and Fujian Provincial Natural Science Foundation of China (2024J01586). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author contributions

L.K.Z., H.L.Z., C.M.H., and J.W.X.: concept and design; L.K.Z., H.L.Z., B.B.X., G.T.L., H.H.Z., Y.H.T., and P.L.: acquisition, analysis, or interpretation of data; L.K.Z., H.L.Z., Q.Y.C., and J.W.X.: drafting of the manuscript; L.K.Z., H.L.Z., X.Y.Z., and J.W.X.: statistical analysis; X.Y.Z., Z.W.Z., C.M.H., P.L., J.W.X., and C.H.Z.: administrative, technical, or material support; H.L.Z., C.M.H., and J.W.X.: supervision.

Conflicts of interest disclosure

There are no conflicts of interest or financial ties to disclose from any of author.

Research registration unique identifying number (UIN)

Name of the registry: Clinical Trials.gov. Unique Identifying number or registration ID: NCT06689462 . Hyperlink to your specific registration (must be publicly accessible and will be checked): https://clinicaltrials.gov/ct2/results?cond=NCT06689462&term=&cntry=&state=&city=&dist= .

Guarantor

Jian-Wei Xie.

Provenance and peer review

Not commissioned, externally peer-reviewed.

Data availability statement

The dataset analyzed for this study is available from the corresponding author on reasonable request.

References

[1] Sung H Ferlay J Siegel RL . Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin 2021 ; 71 : 209 – 49 . 33538338 10.3322/caac.21660

[2] Bray F Ferlay J Soerjomataram I . Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [published correction appears in CA Cancer J Clin. 2020 Jul;70(4):313] . CA Cancer J Clin 2018 ; 68 : 394 – 424 . 30207593 10.3322/caac.21492

[3] Cunningham D Allum WH Stenning SP . Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer . N Engl J Med 2006 ; 355 : 11 – 20 . 16822992 10.1056/NEJMoa055531

[4] Al-Batran SE Homann N Pauligk C . Perioperative chemotherapy with fluorouracil plus leucovorin, oxaliplatin, and docetaxel versus fluorouracil or capecitabine plus cisplatin and epirubicin for locally advanced, resectable gastric or gastro-oesophageal junction adenocarcinoma (FLOT4): a randomised, phase 2/3 trial . Lancet 2019 ; 393 : 1948 – 57 . 30982686 10.1016/S0140-6736(18)32557-1

[5] Zhang X Liang H Li Z . Perioperative or postoperative adjuvant oxaliplatin with S-1 versus adjuvant oxaliplatin with capecitabine in patients with locally advanced gastric or gastro-oesophageal junction adenocarcinoma undergoing D2 gastrectomy (RESOLVE): an open-label, superiority and non-inferiority, phase 3 randomised controlled trial [published correction appears in Lancet Oncol. 2021;22(8):e347] . Lancet Oncol 2021 ; 22 : 1081 – 92 . 34252374 10.1016/S1470-2045(21)00297-7

[6] Kang YK Yook JH Park YK . PRODIGY: a phase III study of neoadjuvant docetaxel, oxaliplatin, and S-1 plus surgery and adjuvant S-1 versus surgery and adjuvant S-1 for resectable advanced gastric cancer . J Clin Oncol 2021 ; 39 : 2903 – 13 . 34133211 10.1200/JCO.20.02914 PMC8425847

[7] Agha RA Mathew G Rashid R . Transparency in the reporting of artificial intelligence – the TITAN guideline . Prem J Sci 2025 ; 10 : 100082 .

[8] Jafri SHR Previgliano C Khandelwal K . Cachexia index in advanced non-small-cell lung cancer patients . Clin Med Insights Oncol 2015 ; 9 : 87 – 93 . 26604850 10.4137/CMO.S30891 PMC4640427

[9] Brown LR Thomson GG Gardner E . Cachexia index for prognostication in surgical patients with locally advanced oesophageal or gastric cancer: multicentre cohort study . Br J Surg 2024 ; 111 : znae098 . 38593042 10.1093/bjs/znae098 PMC11003541

[10] Gong C Wan Q Zhao R . Cachexia index as a prognostic indicator in patients with gastric cancer: a retrospective study . Cancers (Basel) 2022 ; 14 : 4400 . 36139560 10.3390/cancers14184400 PMC9497229

[11] Xie HL Ruan GT Wei L . The prognostic value of the combination of body composition and systemic inflammation in patients with cancer cachexia . J Cachexia, Sarcopenia Muscle 2023 ; 14 : 879 – 90 . 36872512 10.1002/jcsm.13205 PMC10067477

[12] Sakurai K Kubo N Hasegawa T . The cancer cachexia index can be used to prognostically predict patients with gastric cancer undergoing gastrectomy . Ann Nutr Metab 2023 ; 79 : 511 – 21 . 37751717 10.1159/000534244

[13] da Rocha IMG Marcadenti A de Medeiros GOC . Is cachexia associated with chemotherapy toxicities in gastrointestinal cancer patients? A prospective study . J Cachexia, Sarcopenia Muscle 2019 ; 10 : 445 – 54 . 30924270 10.1002/jcsm.12391 PMC6463470

[14] Rashid R Sohrabi C Kerwan A . The STROCSS 2024 guideline: strengthening the reporting of cohort, cross-sectional and case-control studies in surgery . Int J Surg 2024 ; 110 : 3151 – 65 . 38445501 10.1097/JS9.0000000000001268 PMC11175759

[15] Zhang LK Zheng HL Zheng HH . Effects of tumor marker regression load score on long-term prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy . Eur J Surg Oncol 2024 ; 50 : 108367 . 38718701 10.1016/j.ejso.2024.108367

[16] Eisenhauer EA Therasse P Bogaerts J . New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) . Eur J Cancer 2009 ; 45 : 228e47 . 19097774 10.1016/j.ejca.2008.10.026

[17] Ajani JA In H Sano T . Stomach . In Amin MB , ed. AJCC Cancer Staging Manual . 8 th ed. New York: Springer-Verlag ; 2016 : 203 – 20 .

[18] Mourtzakis M Prado CM Lieffers JR . A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care . Appl Physiol Nutr Metabol 2008 ; 33 : 997e1006 . 10.1139/H08-075 18923576

[19] Lin JX Tang YH Zhou WX . Body composition parameters predict pathological response and outcomes in locally advanced gastric cancer after neoadjuvant treatment: a multicenter, international study . Clin Nutr 2021 ; 40 : 4980 – 87 . 34364237 10.1016/j.clnu.2021.06.021

[20] Dennis RA Long DE Landes RD . Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older adults . PLoS ONE 2018 ; 13 : e0204529 . 30278056 10.1371/journal.pone.0204529 PMC6168133

[21] Yasui K Shida D Nakamura Y . Postoperative, but not preoperative, inflammation-based prognostic markers are prognostic factors in stage III colorectal cancer patients . Br J Cancer 2021 ; 124 : 933 – 41 . 33257844 10.1038/s41416-020-01189-6 PMC7921100

[22] Ma F Li L Xu L . The relationship between systemic inflammation index, systemic immune-inflammatory index, and inflammatory prognostic index and 90-day outcomes in acute ischemic stroke patients treated with intravenous thrombolysis . J Neuroinflammation 2023 ; 20 : 220 . 37777768 10.1186/s12974-023-02890-y PMC10543872

[23] Hu B Yang XR Xu Y . Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma . Clin Cancer Res 2014 ; 20 : 6212 – 22 . 25271081 10.1158/1078-0432.CCR-14-0442

[24] Go SI Park MJ Park S . Cachexia index as a potential biomarker for cancer cachexia and a prognostic indicator in diffuse large B-cell lymphoma . J Cachexia, Sarcopenia Muscle 2021 ; 12 : 2211 – 19 . 34676685 10.1002/jcsm.12837 PMC8718032

[25] Liu X Shi B . Progress in research on the role of fibrinogen in lung cancer . Open Life Sci 2020 ; 15 : 326 – 30 . 33817221 10.1515/biol-2020-0035 PMC7874584

[26] Suzuki-Inoue K . Platelets and cancer-associated thrombosis: focusing on the platelet activation receptor CLEC-2 and podoplanin . Blood 2019 ; 134 : 1912 – 18 . 31778548 10.1182/blood.2019001388

[27] Liang XW Wen J Liu B Wang SZ Wu JC Pan T . Prognostic impact of visceral and subcutaneous fat area in stage I-III colon cancer patients with cachexia: a population-based multicenter study . Front Nutr 2025 ; 12 : 1538285 . 40098734 10.3389/fnut.2025.1538285 PMC11911208

[28] Kays JK Shahda S Stanley M . Three cachexia phenotypes and the impact of fat-only loss on survival in FOLFIRINOX therapy for pancreatic cancer . J Cachexia, Sarcopenia Muscle 2018 ; 9 : 673 – 84 . 29978562 10.1002/jcsm.12307 PMC6104116

[29] Silvério R Lira FS Oyama LM . Lipases and lipid droplet-associated protein expression in subcutaneous white adipose tissue of cachectic patients with cancer . Lipids Health Dis 2017 ; 16 : 159 . 28830524 10.1186/s12944-017-0547-x PMC5568087

[30] Cheng E Caan BJ Chen WY Irwin ML Prado CM Cespedes Feliciano EM . Adipose tissue radiodensity and mortality among patients with nonmetastatic breast cancer . Clin Nutr 2022 ; 41 : 2607 – 13 . 36306565 10.1016/j.clnu.2022.09.016 PMC9722634

[31] Anciaux M Van Gossum A Wenglinski C . Fat density is a novel prognostic marker in patients with esophageal cancer . Clin Nutr ESPEN 2020 ; 39 : 124 – 30 . 32859305 10.1016/j.clnesp.2020.07.007

[32] Lee JW Lee SM Chung YA . Prognostic value of CT attenuation and FDG uptake of adipose tissue in patients with pancreatic adenocarcinoma . Clin Radiol 2018 ; 73 : 1056.e1–1056.e10 . 10.1016/j.crad.2018.07.094 30077337

[33] Veld J Vossen JA De Amorim Bernstein K Halpern EF Torriani M Bredella MA . Adipose tissue and muscle attenuation as novel biomarkers predicting mortality in patients with extremity sarcomas . Eur Radiol 2016 ; 26 : 4649 – 55 . 26960539 10.1007/s00330-016-4306-6

[34] Han J Tang M Lu C Shen L She J Wu G . Subcutaneous, but not visceral, adipose tissue as a marker for prognosis in gastric cancer patients with cachexia . Clin Nutr 2021 ; 40 : 5156 – 61 . 34461589 10.1016/j.clnu.2021.08.003

[35] Qian H Appiah-Kubi K Wang Y . The clinical significance of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs) in gastric cancer: a systematic review and meta-analysis . Crit Rev Oncol Hematol 2018 ; 127 : 15 – 28 . 29891108 10.1016/j.critrevonc.2018.05.004

[36] Machado SA Pasquarelli-do-nascimento G da Silva DS . Browning of the white adipose tissue regulation: new insights into nutritional and metabolic relevance in health and diseases . Nutr Metab (Lond) 2022 ; 19 : 61 . 36068578 10.1186/s12986-022-00694-0 PMC9446768

[37] Cunningham P Unger CA Patton EA . Platelet status in cancer cachexia progression in ApcMin/+ mice . Front Immunol 2023 ; 14 : 1253587 . 37701438 10.3389/fimmu.2023.1253587 PMC10493779

[38] Hao P Feng S Suo M . Platelet to albumin ratio: a risk factor related to prognosis in patients with non-ST-segment elevation acute coronary syndrome undergoing percutaneous coronary intervention . Int J Cardiol 2024 ; 395 : 131588 . 37989451 10.1016/j.ijcard.2023.131588

[39] Zhang T Liu W Xu C . Correlation analysis of hemoglobin, albumin, lymphocyte, platelet score and platelet to albumin ratio and prognosis in patients with lung adenosquamous carcinoma . Front Oncol 2023 ; 13 : 1166802 . 37746281 10.3389/fonc.2023.1166802 PMC10514357

[40] Wang P Wang S Huang Q . Development and validation of the systemic nutrition/inflammation index for improving perioperative management of non-small cell lung cancer . BMC Med 2025 ; 23 : 113 . Published 2025 Feb 24 . 39988705 10.1186/s12916-025-03925-2 PMC11849302

[41] Martin L Birdsell L Macdonald N . Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index . J Clin Oncol 2013 ; 31 : 1539 – 47 . 23530101 10.1200/JCO.2012.45.2722

[42] Blauwhoff-Buskermolen S Langius JAE Becker A . The influence of different muscle mass measurements on the diagnosis of cancer cachexia . J Cachexia, Sarcopenia Muscle 2017 ; 8 : 615 – 22 . 28447434 10.1002/jcsm.12200 PMC5566652

[43] Argilés JM Stemmler B López-Soriano FJ . Inter-tissue communication in cancer cachexia . Nat Rev Endocrinol 2019 ; 15 : 9 – 20 . 10.1038/s41574-018-0123-0 30464312

[44] Repetto O De Re V . Coagulation and fibrinolysis in gastric cancer . Ann N Y Acad Sci 2017 ; 1404 : 27 – 48 . 28833193 10.1111/nyas.13454

[45] Zhang Y Liu N Liu C . High fibrinogen and platelets correlate with poor survival in gastric cancer patients . Ann Clin Lab Sci 2020 ; 50 : 457 – 62 . 32826241

[46] Zhao LY Zhao YL Wang JJ . Is preoperative fibrinogen associated with the survival prognosis of gastric cancer patients? A multi-centered, propensity score-matched retrospective study . World J Surg 2020 ; 44 : 213 – 22 . 31637507 10.1007/s00268-019-05191-9

[47] Jones JM McGonigle NC McAnespie M . Plasma fibrinogen and serum C-reactive protein are associated with non-small cell lung cancer . Lung Cancer 2006 ; 53 : 97 – 101 . 16698114 10.1016/j.lungcan.2006.03.012

[48] Mantovani A Allavena P Sica A . Cancer-related inflammation . Nature 2008 ; 454 : 436 – 44 . 18650914 10.1038/nature07205

[49] Diakos CI Charles KA McMillan DC . Cancer-related inflammation and treatment effectiveness . Lancet Oncol 2014 ; 15 : e493 – e503 . 25281468 10.1016/S1470-2045(14)70263-3

[50] Virchow R . An address on the value of pathological experiments . Br Med J 1881 ; 2 : 198 – 203 . 10.1136/bmj.2.1075.198 PMC2264055 20749954

[51] Hanahan D Weinberg RA . Hallmarks of cancer: the next generation . Cell 2011 ; 144 : 646 – 74 . 21376230 10.1016/j.cell.2011.02.013

[52] Jia P Wu X Shen F . The combination of handgrip strength and CONUT predicts overall survival in patients with gastrointestinal cancer: a multicenter cohort study . Clin Nutr 2024 ; 43 : 2057 – 68 . Published online . 39088962 10.1016/j.clnu.2024.07.026

[53] Park MH Wahedally H Cromwell D . National Oesophago-Gastric Cancer Audit 2022 . Healthcare Quality Improvement Partnership , London ; 2023 .

[54] Ott K Sendler A Becker K . Neoadjuvant chemotherapy with cisplatin, 5-FU, and leucovorin (PLF) in locally advanced gastric cancer: a prospective phase II study . Gastric Cancer 2003 ; 6 : 159 – 67 . 14520529 10.1007/s10120-003-0245-4

[55] Yoshikawa T Morita S Tanabe K . Survival results of a randomised two-by-two factorial phase II trial comparing neoadjuvant chemotherapy with two and four courses of S-1 plus cisplatin (SC) and paclitaxel plus cisplatin (PC) followed by D2 gastrectomy for resectable advanced gastric cancer . Eur J Cancer 2016 ; 62 : 103 – 11 . 27244537 10.1016/j.ejca.2016.04.012

[56] Fujitani K Nakamura K Mizusawa J . Posttherapy topographical nodal status, ypN-site, predicts survival of patients who received neoadjuvant chemotherapy followed by curative surgical resection for non-type 4 locally advanced gastric cancer: supplementary analysis of JCOG1004-A . Gastric Cancer 2021 ; 24 : 197 – 204 . 32572792 10.1007/s10120-020-01098-w

[57] Ychou M Boige V Pignon JP . Perioperative chemotherapy compared with surgery alone for resectable gastroesophageal adenocarcinoma: an FNCLCC and FFCD multicenter phase III trial . J Clin Oncol 2011 ; 29 : 1715 – 21 . 21444866 10.1200/JCO.2010.33.0597