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. 2017 Jan 27;8(12):18968–18978. doi: 10.18632/oncotarget.14859

Tumor volume increases the predictive accuracy of prognosis for gastric cancer: A retrospective cohort study of 3409 patients

Zhen Liu 1,#, Peng Gao 2,#, Shushang Liu 1,#, Gaozan Zheng 1, Jianjun Yang 1, Li Sun 1, Liu Hong 1, Daiming Fan 1, Hongwei Zhang 1, Fan Feng 1
PMCID: PMC5386662  PMID: 28145885

Abstract

Tumor diameter or T stage does not reflect the actual tumor burden and is not able to estimate accurate prognosis of gastric cancer. The current study aimed to evaluate the prognostic value of tumor volume (V) for gastric cancer. A total of 3409 enrolled gastric cancer patients were randomly divided into training set (n = 1705) and validation set (n = 1704). Tumor volume was calculated by the formula V = Tumor diameter × (T stage)2/2. The survival predictive accuracy and prognostic discriminatory ability between different variables and staging systems were analyzed. Four optimal cutoff points for V were obtained in training set (3.5, 8.6, 25.0, 45.0, all P < 0.001). V stage was significantly associated with tumor location, macroscopic type, differentiation degree, tumor diameter, T stage, N stage, vessel invasion, neural invasion and TNM stage (all P < 0.001). V stage was an independent prognostic factor both in training and validation set. V stage showed better predictive accuracy and prognostic discriminatory ability than tumor diameter and T stage. VNM staging system also have advantages in predictive accuracy and prognostic discriminatory ability than TNM staging system. The VNM multivariable model represent good agreement between the predicted survival and actual survival. In conclusion, tumor volume was significantly associated with clinicopathological features and prognosis of gastric cancer. In comparison with TNM staging system, VNM staging system could improve the predictive accuracy and prognostic discriminatory ability for gastric cancer.

Keywords: gastric cancer, tumor volume, prognosis, predictive accuracy

INTRODUCTION

Although the incidence of gastric cancer has significantly decreased worldwide, it is still the second most common malignancy in China [1]. Thus, identification of its risk factors for prognosis remains greatly important to clinicians. A variety of factors have been adequately analyzed in order to evaluate their predictive value of prognosis for gastric cancer, including tumor diameter [2], T stage [3], N stage [4], tumor markers [5, 6] and other novel indexes [7–9].

Till now, the most commonly used classification is TNM staging system including T stage, N stage and distant metastasis, which was recommended by American Joint Committee on Cancer (AJCC) [10] and Japanese Gastric Cancer Association (JGCA) [11]. However, the tumor diameter, as an important prognostic factor which was demonstrated in many other tumors [12–15] as well as gastric cancer [16], has not been included in the TNM staging system yet. Thus, in present study, we defined a new index—tumor volume (V) by the formula V = Tumor diameter × (T stage)2/2, and investigated the prognostic value of tumor volume and VNM for gastric cancer.

RESULTS

General features of gastric cancer patients

There were 2662 males (78.1%) and 747 females (21.9%). The patient age ranged from 20 to 90 years (median, 58; mean, 57). The follow up time ranged from 1 to 75 months (median, 24.9; mean, 28.1). The 1-, 3- and 5-year overall survival rate was 89.0%, 66.6% and 57.9%, respectively. There were 1705 patients in training set and 1704 patients in validation set. The clinicopathological characteristics were comparable between training and validation set (Table 1).

Table 1. Clinicopathological characteristics of patients in training and validation set.

Characteristics Training set Validation set P value
V1 V2 V3 V4 V5 P value V1 V2 V3 V4 V5 P value
Age 0.311 0.461 0.989
≤ 60 213 110 324 226 139 203 107 321 240 140
> 60 127 68 214 174 110 124 72 206 183 108
Gender 0.576 0.068 0.051
Male 268 149 425 312 201 242 152 403 325 185
Female 72 29 113 88 48 85 27 124 98 63
Tumor location < 0.001 < 0.001 0.850
Upper third 53 48 195 153 90 61 48 181 159 85
Middle third 58 22 84 58 50 52 27 98 64 41
Lower third 218 96 230 151 67 202 99 222 156 89
Upper-middle or middle-lower 11 12 29 38 42 12 5 26 44 33
Macroscopic type < 0.001 < 0.001 0.387
Early stage 309 2 0 0 0 291 1 0 0 0
Bormann I 6 22 50 34 29 7 15 39 36 19
Bormann II 11 119 164 76 44 19 124 173 65 35
Bormann III 1 24 265 212 131 2 25 251 255 137
Bormann IV 2 4 40 56 32 0 9 38 41 50
Differentiation degree < 0.001 < 0.001 0.736
Well differentiated 101 19 44 20 8 114 14 44 20 4
Moderately differentiated 90 44 160 86 48 88 68 146 100 37
Poorly differentiated 136 105 304 264 164 114 92 321 271 185
Mucinous or signet ring cell 10 9 26 25 28 11 5 16 30 22
Tumor diameter* < 0.001 < 0.001 0.954
≤ 2.5 cm 232 47 52 0 0 230 52 58 0 0
2.5–4.3 cm 96 129 243 79 2 75 126 252 87 2
4.3–5.5 cm 7 0 206 103 4 15 0 186 107 3
> 5.5 cm 5 2 37 218 243 7 1 31 229 243
T stage < 0.001 < 0.001 0.699
T1 326 2 0 0 0 306 1 0 0 0
T2 14 169 79 0 0 20 174 78 0 0
T3 0 6 389 218 14 1 4 395 229 8
T4a 0 1 69 180 220 0 0 53 193 230
T4b 0 0 1 2 15 0 0 1 1 10
N stage < 0.001 < 0.001 0.587
N0 288 86 158 67 22 274 88 155 69 26
N1 32 40 138 64 24 32 41 146 88 26
N2 14 25 114 91 61 16 30 105 96 43
N3a 5 24 102 125 82 5 16 92 121 103
N3b 1 3 26 53 60 0 4 29 49 50
Vessel invasion < 0.001 < 0.001 0.874
Positive 45 65 209 233 187 51 60 214 235 180
Negative 182 58 166 80 40 175 55 139 102 49
Neural invasion < 0.001 < 0.001 0.347
Positive 62 87 314 278 218 70 75 302 313 215
Negative 128 38 63 37 8 119 39 56 24 16
TNM stage < 0.001 < 0.001 0.239
IA 279 1 0 0 0 260 1 0 0 0
IB 39 81 29 0 0 41 85 35 0 0
IIA 17 45 140 41 3 20 43 132 42 0
IIB 5 24 140 72 20 4 29 143 71 27
IIIA 0 26 105 65 24 2 21 102 110 27
IIIB 0 1 96 139 64 0 0 98 120 43
IIIC 0 0 28 83 138 0 0 17 80 151
VNM stage < 0.001 < 0.001 0.963
IA 288 0 0 0 0 274 0 0 0 0
IB 32 86 0 0 0 32 88 0 0 0
IIA 14 40 161 0 0 16 41 166 0 0
IIB 6 25 143 67 0 5 30 139 69 0
IIIA 0 27 107 64 0 0 20 106 88 0
IIIB 0 0 127 91 46 0 0 116 96 52
IIIC 0 0 0 178 203 0 0 0 170 196
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Tumor diameter*: Tumor diameter was divided into 4 subgroups according to the 3 optimal cutoff points calculated by X-tile software (Supplementary Figure 1).

Definition of V stage and VNM stage

Tumor volume was calculated by the formula V = Tumor diameter × (T stage)2/2 (1 represents T1 stage, 2 represents T2 stage, 3 represents T3 stage, 4 represents T4a stage, and 5 represents T4b stage). The 4 optimal cutoff points of tumor volume (all P < 0.05) in training set were showed in Figure 1. Then, V stage was defined according to the 4 cutoff points: V1 (≤ 3.5), V2 (3.5–8.6), V3 (8.6–25.0), V4 (25.0–45.0) and V5 (> 45.0). VNM system was designed as combination of V stage, N stage and M stage on the basis of 7th edition of AJCC cancer staging manual.

Figure 1. Calculation of cutoff points of tumor volume by X-tile in training set.

Figure 1

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(A) Three subgroups were built according to the 2 optimal cutoff points (9.6, 45.0, P < 0.001); (B) Two subgroups were built according to the optimal cutoff point (3.5, P < 0.001) for patients with tumor volume between 0 and 9.6. (C) Two subgroups were built according to the optimal cutoff point (25.0, P < 0.001) for patients with tumor volume between 9.6 and 45.0. (D) No cutoff point was obtained for patients with tumor volume exceed 45.0.

The correlation between V stage and other factors were analyzed in Table 1. Both in training and validation set, V stage was found to be significantly associated with tumor location (P < 0.001), macroscopic type (P < 0.001), differentiation degree (P < 0.001), tumor diameter (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), vessel invasion (P < 0.001), neural invasion (P < 0.001) and TNM stage (P < 0.001). Compared with the small tumor volume-patients, patients with larger tumor volume were found more frequently in Borrmann type III or IV, having a higher proportion in poor differentiation, in advanced T stage and N stage, in positive vessel and neural invasion and in advanced TNM stage.

Prognostic value of V stage in gastric cancer

Prognostic predictors were identified by univariate and multivariate analysis in training set (Table 2). Age (P = 0.025), tumor location (P = 0.004), macroscopic type (P < 0.001), differentiation degree (P < 0.001), tumor diameter (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), V stage (P < 0.001), vessel invasion (P < 0.001) and neural invasion (P < 0.001) were risk factors for prognosis of gastric cancer. Multivariate analysis (Table 2) showed that age (P = 0.016), macroscopic type (P = 0.001), N stage (P < 0.001) and V stage (P < 0.001) were independent prognostic factors for gastric cancer.

Table 2. Univariate and multivariate analysis of overall survival in training set.

Characteristics Univariate analysis Multivariate analysis C-index AIC
β HR (95% CI) P value β HR (95% CI) P value
Age 0.203 1.225 (1.026–1.464) 0.025 0.283 1.327 (1.053–1.671) 0.016 0.528 3936.8
Gender 0.017 1.017 (0.818–1.265) 0.879 0.499 3935.5
Tumor location 0.003 1.003 (1.001–1.006) 0.004 0.516 3937.0
Macroscopic type 0.540 1.716 (1.566–1.879) < 0.001 0.257 1.292 (1.109–1.507) 0.001 0.653 3832.8
Differentiation degree 0.422 1.525 (1.352–1.720) < 0.001 0.593 3894.7
Tumor diameter 0.632 1.882 (1.721–2.058) < 0.001 0.686 3835.3
T stage 0.736 2.087 (1.889–2.306) <0.001 0.681 3780.3
N stage 0.657 1.930 (1.798–2.072) < 0.001 0.561 1.753 (1.576–1.949) < 0.001 0.736 3698.2
V stage 0.681 1.975 (1.820–2.144) < 0.001 0.340 1.405 (1.235–1.599) < 0.001 0.715 3768.2
Vessel invasion 1.087 2.966 (2.282–3.855) < 0.001 0.614 3871.8
Neural invasion 1.237 3.445 (2.395–4.955) < 0.001 0.579 3880.2
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HR: Hazard ratio; CI: Confidence interval.

The prognostic value of V stage was also analyzed in validation set using the cutoff points from training set (Table 3). V stage was still the independent prognostic factor for gastric cancer in validation set (P = 0.045).

Table 3. Univariate and multivariate analysis of overall survival in validation set.

Characteristics Univariate analysis Multivariate analysis C-index AIC
β HR (95% CI) P value β HR (95% CI) P value
Age 0.355 1.426 (1.193–1.705) < 0.001 0.312 1.366 (1.093–1.707) 0.006 0.512 4137.4
Gender 0.128 1.136 (0.922–1.399) 0.230 0.546 4146.5
Tumor location 0.005 1.005 (1.003–1.008) < 0.001 0.495 4146.4
Macroscopic type 0.587 1.798 (1.629–1.984) < 0.001 0.174 1.190 (1.018–1.391) 0.029 0.657 4032.1
Differentiation degree 0.473 1.606 (1.417–1.819) < 0.001 0.591 4112.3
Tumor diameter 0.519 1.681 (1.541–1.833) < 0.001 0.656 4039.4
T stage 0.752 2.121 (1.906–2.359) < 0.001 0.332 1.394 (1.071–1.815) 0.014 0.686 3979.3
N stage 0.637 1.891 (1.762–2.029) < 0.001 0.485 1.625 (1.471–1.795) <0.001 0.728 3919.9
V stage 0.646 1.907 (1.752–2.076) < 0.001 0.200 1.221 (1.004–1.486) 0.045 0.701 3962.4
Vessel invasion 1.173 3.230 (2.490–4.190) < 0.001 0.627 4062.3
Neural invasion 1.214 3.366 (2.318–4.887) < 0.001 0.574 4095.7
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HR: Hazard ratio; CI: Confidence interval.

Comparison of predictive value of V and VNM stage

C-index and AIC were calculated in order to assess the predictive accuracy and prognostic discriminatory ability of each factor for prognosis of gastric cancer in training set (Table 2). A larger C-index and smaller AIC value of V stage were found when compared with tumor diameter (C-index: 0.715 vs 0.686; AIC: 3768.2 vs 3835.3, P < 0.001) and T stage (C-index: 0.715 vs 0.681; AIC: 3768.2 vs 3780.3, P < 0.001) (Figure 2A). VNM stage also revealed significant superiority to TNM stage in predictive accuracy and prognostic discriminatory ability (C-index: 0.756 vs 0.743; AIC: 3667.2 vs 3668.8, P < 0.001) (Figure 2C).

Figure 2. Comparison of predictive value.

Figure 2

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(A) Comparison among tumor diameter, T stage and V stage in training set; (B) Comparison among tumor diameter, T stage and V stage in validation set; (C) Comparison between TNM and VNM stage in training set; (D) Comparison between TNM and VNM stage in validation set.

In validation set, the predictive accuracy and prognostic discriminatory ability of V stage and VNM stage were still better than that of tumor diameter, T stage (Table 3, Figure 2B) and TNM stage (Figure 2D) respectively.

Multivariable models and nomograms

Two multivariable prediction models were built in training set. TNM model was based on the selection of age, gender, tumor location, macroscopic type, differentiation degree, T stage, N stage, vessel invasion and neural invasion. VNM model was based on the selection of age, gender, tumor location, macroscopic type, differentiation degree, N stage, V stage, vessel invasion and neural invasion. Finally, results of the two multivariable regression models were showed in Table 4. Consistent with the results of multivariate analysis above, V stage was still selected as an independent prognostic factor in VNM model.

Table 4. Multivariable models for predicting overall survival in training set.

Characteristics TNM model VNM model
β HR (95% CI) P value β HR (95% CI) P value
Age 0.307 1.359 (1.080–1.711) 0.009 0.288 1.334 (1.059–1.680) 0.015
Macroscopic type 0.269 1.309 (1.121–1.529) 0.001 0.253 1.288 (1.103–1.503) 0.001
Differentiation degree 0.166 1.181 (0.966–1.443) 0.105 0.198 1.219 (1.000–1.487) 0.005
T stage 0.412 1.510 (1.269–1.798) < 0.001 — — —
N stage 0.562 1.754 (1.575–1.954) < 0.001 0.541 1.719 (1.543–1.913) < 0.001
V stage — — — 0.331 1.392 (1.223–1.585) < 0.001
C-index 0.767 0.775
AIC 3648.7 3635.6
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C-index: Harrell's concordance index; AIC: Akaike Information Criterion;

HR: Hazard ratio; CI: Confidence interval.

Two nomograms were developed for predicting overall survival in training set (Figure 3A and 3C). The VNM model showed significant advantages than TNM model in predictive accuracy and prognostic discriminatory ability (C-index: 0.775 vs 0.767; AIC: 3635.6 vs 3648.7, P < 0.001) (Table 4). The calibration curves of the two models both showed good agreement between predicted and actual outcomes (Figure 3B and 3D).

Figure 3. Nomograms in training set.

Figure 3

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(A) and (B) Nomogram plots and calibration curves of TNM stage; (C) and (D) Nomogram plots and calibration curves of VNM stage.

The results in validation set were consistent with those in training set. The predictive accuracy and prognostic discriminatory ability of VNM model were significant better than those of TNM model (Table 5). The predicted survival of the two models showed good agreement with observed survival (Figure 4).

Table 5. Multivariable models for predicting overall survival in validation set.

Characteristics TNM model VNM model
β HR (95% CI) P value β HR (95% CI) P value
Age 0.358 1.430 (1.144–1.787) 0.002 0.322 1.380 (1.104–1.726) 0.005
Macroscopic type 0.201 1.223 (1.048–1.427) 0.011 −0.193 1.213 (1.040–1.415) 0.014
Vessel invasion 0.244 1.227 (0.951–1.714) 0.105 0.320 1.378 (1.029–1.844) 0.031
T stage 0.505 1.657 (1.380–1.990) < 0.001 — — —
N stage 0.475 1.607 (1.447–1.785) < 0.001 0.442 1.556 (1.400–1.730) < 0.001
V stage — — — 0.379 1.461 (1.276–1.672) < 0.001
C-index 0.767 0.769
AIC 3848.6 3848.4
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C-index: Harrell's concordance index; AIC: Akaike Information Criterion;

HR: Hazard ratio; CI: Confidence interval.

Figure 4. Nomograms in validation set.

Figure 4

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(A) and (B) Nomogram plots and calibration curves of TNM stage; (C) and (D) Nomogram plots and calibration curves of VNM stage.

Comparison of formulas

In order to evaluate the superiority of the current volume calculating formula, we further validated the formula reported in the previous study using our center's data (Table 6). The results showed that the V stage, VNM stage and the multivariable model calculated by current formula had a larger C-index and a smaller AIC value than those calculated by the previous formula (all P < 0.001).

Table 6. Comparison and validation between the two formulas.

Current formula Previous formula P value
C-index AIC C-index AIC
Training group
V stage 0.715 3768.2 0.693 3845.4 < 0.001
VNM stage 0.756 3667.2 0.732 3753.3 < 0.001
Multivariable model 0.775 3635.6 0.764 3712.6 < 0.001
Validation group
V stage 0.701 3962.4 0.684 3993.3 < 0.001
VNM stage 0.746 3862.9 0.723 3917.5 < 0.001
Multivariable model 0.769 3848.4 0.756 3908.2 < 0.001
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Current formula: V = Tumor diameter × (T stage)2/2;

Previous formula [33]: V = pT × (tumor size/2)2.

DISCUSSION

The current study investigated the prognostic value of tumor volume for gastric cancer. The results showed that the predictive value of V stage for gastric cancer was superior to tumor diameter and T stage. VNM staging system could significantly improve the predictive accuracy and prognostic discriminatory ability for gastric cancer.

The actual malignancy of gastric cancer is complex due to the variety of appearances and patterns of invasion [17]. Up to now, T stage and N stage were demonstrated to be the most significant prognostic factors for gastric cancer in several previous studies [18–20]. Tumor diameter, which has been considered as a rough indicator of tumor size for gastric cancer [21, 22], was closely related with histologic type, lymph node metastasis, tumor invasion, vessel invasion, neural invasion and peritoneal metastasis [23–25]. Further investigations demonstrated that tumor diameter was an independent prognostic factor for gastric cancer [26–28]. Saito et al. [28] found that tumor diameter could also be used to predict the recurrence site of gastric cancer. Moreover, Deng et al. [29] demonstrated that tumor diameter represented better prognostic stratification ability compared with T stage, while Zhao et al. [16] reported that the prognostic prediction value was comparable between the two variables. In both studies above, they replaced T stage with tumor diameter in the TNM staging system and found that the new classification was more competent in predicting the prognosis of gastric cancer than the current TNM staging system.

However, tumor diameter or T stage alone could not accurately reflect the actual tumor burden of gastric cancer due to this cancer's complicated morphology and inconsistent pattern of invasion [2, 17, 27, 28]. Thus, a new index which could better reflect the actual size of this tumor is needed.

Tumor volume, which could accurately reflect the tumor burden, may possess significant prognostic value for gastric cancer. Moreover, tumor volume was reported as an independent prognostic factor in several cancers, such as non-small-cell lung carcinoma [30], nasopharyngeal carcinoma [31] and malignant melanoma [32]. However, study assessing the predictive value of tumor volume for gastric cancer is lacking. Up to date, there is only one study reported by Jiang et al [33] that calculated tumor volume via the formula V = pT × (tumor size/2)2 demonstrated tumor volume maybe more reliable than T stage in predicting prognosis of gastric cancer in a cohort of 497 patients. Further, they conducted a VNM staging system by replacing the T stage with tumor volume and found that it was more appropriate than the current TNM staging system in predicting prognosis of gastric cancer patients.

In current study, we calculated the tumor volume based on the formula V = Tumor diameter × T stage2/2. The mathematic model of tumor volume referred to the formula V = length × width2/2 in the tumor bearing mouse model [34]. We used tumor diameter instead of the length and replaced the width with T stage. We first used the C-index and AIC value to evaluate the predictive accuracy and prognostic discriminatory ability for tumor volume, respectively. The predictive value of V stage was higher than tumor diameter and T stage. However, accurate prediction of prognosis is more determined by the staging system than a variable alone [12]. We then conducted the VNM stage on the basis of the two most powerful prognostic predictors—V stage and N stage. The predictive accuracy and prognostic discriminatory ability of VNM stage was better than those of TNM stage.

Further, two nomograms were developed for predicting the overall survival. The VNM model had significant advantages in the predictive accuracy and prognostic discriminatory ability than TNM model. The predicted survival of VNM model showed well agreement with the actual survival.

A good staging system, which could not only be able to predict survival, but also guide the adjuvant therapy, is of great importance for patients with gastric cancer [35]. The predictive superiority of tumor volume demonstrated in current study was consistent with Jiang's findings [33]. To show the improvement we got in this study, we then validated their formula using our data and found that the tumor volume calculated by our formula V = Tumor diameter × T stage2/2 revealed better predictive accuracy and prognostic discriminatory ability.

There are also some limitations in our present study. First, it was a retrospective study of a single center's experiences. Multi-center studies are needed to verify the predictive value of tumor volume. Second, the calculation of tumor volume is not simple and immediate. Thus, a more convenient and accurate index which could reflect the tumor burden is needed.

MATERIALS AND METHODS

From September 2008 to March 2015, a total of 3409 gastric cancer patients who received radical gastrectomy in our department were retrospectively analyzed. The inclusion criteria were listed as follows: 1) without neoadjuvant chemotherapy; 2) without multiple stomach tumors or distant metastasis; 3) with complete follow-up records. This study was approved by the Ethics Committee of Xijing Hospital, and written informed consent was obtained from all patients before surgery.

All of the patients received radical gastrectomy according to the recommendation of Japanese Gastric Cancer Treatment Guidelines [11]. The patients were followed up till November 2015 by enhanced chest and abdominal CT and gastroscopy every 3 months.

Clinicopathological data including age, gender, tumor location, macroscopic type, tumor diameter, differentiation degree, T stage, N stage, vessel invasion, neural invasion and TNM stage were recorded. Tumor diameter was measured and defined as the maximum diameter of the tumor according to the Japanese classification of gastric carcinoma: 3rd English edition [36]. The TNM stage were defined on the basis of 7th edition of AJCC cancer staging manual [10].

Data were processed using SPSS 22.0 for Windows (SPSS Inc., Chicago, IL, USA). With the X-tile software (Yale University) [37], the 3409 patients were randomly divided into training set and validation set according to sample size ratio of 1:1. The optimal cut-off values of tumor volume were calculated using X-tile software (Supplementary). Discrete variables were analyzed using the Chi-square test or Fisher's exact test. Risk factors for survival were identified by univariate analysis and Cox's proportional hazards regression model was employed for multivariate analysis. Overall survival was analyzed by the Kaplan-Meier method and differences between curves were compared using log-rank test. A backward procedure based on the Akaike information criterion (AIC) was used for multivariable selection. Nomogram and calibration curve were displayed using the package of Regression Modeling Strategies (http://CRAN.R-project.org/package=rms) in R (version3.1.2, http://www.R-project.org/). AIC and concordance index (C-index) values within a cox proportional hazard regression model were calculated in order to compare the prognostic discriminatory ability and predictive accuracy of variables using the package of Harrell Miscellanceous (http://CRAN.R-project.org/package=Hmisc.). A smaller AIC value indicated a better discriminatory ability [38], whereas a larger C-index represented a more predictive accuracy [39]. The likelihood ratio χ2 test was used to compare the different C-indexes between different models. The two-tail P value was considered to be statistically significant at the 5% level.

CONCLUSIONS

Tumor volume was significantly associated with clinicopathological features and prognosis of gastric cancer. The predictive value of tumor volume was higher than tumor diameter and T stage. In comparison with TNM staging system, VNM staging system could improve the predictive accuracy and prognostic discriminatory ability for gastric cancer.

SUPPLEMENTARY FIGURES

ACKNOWLEDGMENTS AND FUNDING

This study was supported in part by grants from the National Natural Scientific Foundation of China [NO. 31100643, 31570907, 81300301, 81572306, 81502403, XJZT12Z03].

Abbreviations

V

Tumor volume

TNM

Tumor-nodes-metastasis classification

AJCC

American Joint Committee on Cancer

JGCA

Japanese Gastric Cancer Association

AIC

Akaike information criterion

C-index

Concordance index

Footnotes

CONFLICTS OF INTEREST

There are no financial or other relations that could lead to a conflicts of interest.

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