Liang GZ, Li XS, Hu ZH, Xu QJ, Wu F, Wu XL, Lei HK. Development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma. World J Gastrointest Oncol 2025; 17(2): 95423 [DOI: 10.4251/wjgo.v17.i2.95423]
Corresponding Author of This Article
Hai-Ke Lei, Doctor, Associate Professor, The Research Center of Big Data, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing 400030, China. tohaike@163.com
Research Domain of This Article
Oncology
Article-Type of This Article
Prospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Guan-Zhong Liang, Xiang-Lin Wu, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, China
Xiao-Sheng Li, Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China
Zu-Hai Hu, Qian-Jie Xu, Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing 400016, China
Fang Wu, Research Center for Medicine and Social Development, School of Public Health, Chongqing Medical University, Chongqing 400016, China
Hai-Ke Lei, The Research Center of Big Data, Chongqing University Cancer Hospital, Chongqing 400030, China
Co-first authors: Guan-Zhong Liang and Xiao-Sheng Li.
Co-corresponding authors: Xiang-Lin Wu and Hai-Ke Lei.
Author contributions: Liang GZ and Li XS contributed equally to this study as co-first authors. Wu XL and Lei HK contributed equally to this study as co-corresponding authors. Liang GZ and Lei HK were responsible for writing the original draft and designing the model; Li XS designed, experimented, and interpreted the model; Hu ZH and Xu QJ analyzed the data; Wu F and Wu XL reviewed and edited the manuscript; all authors collectively designed the methods and experiments, and read and approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Chongqing University Tumor Hospital (Approval No. CZLS2023343-A).
Clinical trial registration statement: This study did not require clinical registration.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors have no financial relationships to disclose.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hai-Ke Lei, Doctor, Associate Professor, The Research Center of Big Data, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing 400030, China. tohaike@163.com
Received: April 10, 2024 Revised: October 1, 2024 Accepted: November 6, 2024 Published online: February 15, 2025 Processing time: 282 Days and 23.9 Hours
Abstract
BACKGROUND
The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China, with the disease's intricate and varied characteristics further amplifying its health impact. Precise forecasting of overall survival (OS) is of paramount importance for the clinical management of individuals afflicted with this malignancy.
AIM
To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.
METHODS
Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020. Least absolute shrinkage and selection operator, univariate, and multivariate Cox regression analyses were employed to identify independent prognostic factors. A nomogram model was developed to predict gastric cancer patient outcomes. The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves. To evaluate the clinical utility of the model, Kaplan-Meier and decision curve analyses were performed.
RESULTS
A total of ten independent prognostic factors were identified, including body mass index, tumor-node-metastasis (TNM) stage, radiation, chemotherapy, surgery, albumin, globulin, neutrophil count, lactate dehydrogenase, and platelet-to-lymphocyte ratio. The area under the curve (AUC) values for the 1-, 3-, and 5-year survival prediction in the training set were 0.843, 0.850, and 0.821, respectively. The AUC values were 0.864, 0.820, and 0.786 for the 1-, 3-, and 5-year survival prediction in the validation set, respectively. The model exhibited strong discriminative ability, with both the time AUC and time C-index exceeding 0.75. Compared with TNM staging, the model demonstrated superior clinical utility. Ultimately, a nomogram was developed via a web-based interface.
CONCLUSION
This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients, which demonstrated strong predictive ability. Based on these findings, this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.
Core Tip: One of the main contributions of this study is that we developed an accessible web-based tool that allows clinicians to easily input patient data and obtain survival prediction, thereby enhancing the model's clinical applicability. This model provides insights that can guide personalized treatment strategies and potentially improve patient outcomes. More importantly, this model demonstrates good predictive ability and accuracy, thus assisting clinicians in creating personalized interventions for patients with gastric cancer.
Citation: Liang GZ, Li XS, Hu ZH, Xu QJ, Wu F, Wu XL, Lei HK. Development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma. World J Gastrointest Oncol 2025; 17(2): 95423
Gastric cancer is the fifth most common malignancy and fourth leading cause of cancer-related deaths worldwide. The global burden of this malignancy is projected to increase by 62% by 2040[1]. China has the most new gastric cancer cases and deaths worldwide, with an estimated 478000 new cases and 373000 deaths annually[2,3]. Early screening and the aggressive nature of the disease cause most cases to be diagnosed at an advanced stage, resulting in a 5-year survival rate of approximately 20%[4].
The tumor-node-metastasis (TNM) classification is currently the most widely used criterion for predicting tumour prognoses in clinical practice. However, the mechanisms underlying tumour formation are complex and influenced by multiple factors. Likewise, prognosis is affected by various variables. Despite patients having similar TNM stages and treatment regimens, their prognoses can still differ. Therefore, constructing an accurate and reliable prediction model for overall survival (OS) in patients with gastric cancer is highly important for accurately assessing prognoses.
The nomogram model integrates multiple predictive indicators derived from multivariable regression analysis. This model employs scaled line segments, drawn proportionally on the same plane, to illustrate the interrelationships between variables, and calculates the incidence rate of individual outcome events. This visual risk assessment and prediction method has been examined across various tumour types[5-7]. Nomogram models have also been constructed using age, race, tumour size and location, pathological characteristics, tumour markers, metastatic site number, invasion depth, lymph node stage, and resection extent. In each case, these models have more effectively predicted gastric cancer prognoses than TNM staging[8-10]. Several studies have employed nomogram models to predict gastric cancer patient survival, providing a basis for individualized treatment plans and follow-ups for patients at high risk[11]. No standard or widely recommended model currently exists for predicting gastric cancer prognoses or survival in clinical practice. We analysed and constructed a nomogram model for predicting OS in a diverse sample of Chinese gastric cancer patients. We incorporated the following distinct factors in our model: Age, sex, marital status, body mass index (BMI), Karnofsky performance status (KPS), histopathology, tumour TNM stage, radiotherapy history, chemotherapy, surgery, immunotherapy, albumin (ALB), globulin (GLB), β2-microglobulin, neutrophil count, white blood cell count (WBC), lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR). Thus, our findings will provide references for gastric cancer assessment and individualized treatment in the future.
MATERIALS AND METHODS
Study design and population
Data were collected from gastric cancer patients at Chongqing University Cancer Hospital between 2018 and 2020. Patients were included if they: (1) Were 18 years of age or older; (2) Had histologically confirmed gastric cancer; and (3) Completed primary antitumour treatment at our hospital. Patients were excluded if they: (1) Were discharged or deceased within 24 h of admission; (2) Had concurrent tumours; (3) Had incomplete baseline data or lacking follow-up records; and/or (4) Had poor treatment compliance.
Data collection
Data related to the following variables were collected: Age, sex, marital status, BMI, KPS, histopathology, TNM stage, radiation therapy history, chemotherapy history, surgery history, immunotherapy history, ALB, GLB, β2-microglobulin, neutrophil, WBC, LDH, NLR, PLR, and LMR. Patient survival outcomes were obtained through active and passive follow-up methods. OS was the primary outcome and defined as the time from diagnosis to death or the most recent follow-up. December 31, 2023 was established as the follow-up endpoint. OS was measured at 1, 3, and 5 years.
Variable selection and model development
Patient data were randomly divided into a training or validation set at a 7:3 ratio. A univariate Cox regression analysis was performed on the training set to identify the predictive value of variables potentially associated with OS. Variables with a P value < 0.2 were selected for preliminary screening and the least absolute shrinkage and selection operator (LASSO) regression algorithm was applied for each. All variables identified by both methods were then included in a multivariable Cox regression, and a stepwise regression was employed to determine the final variable feature set and establish a nomogram prediction model.
Model performance and validation
The concordance index (C-index) was calculated via the Cox regression model. The C-index ranges from 0.5 to 1.0, with values closer to 1.0 indicating better nomogram discriminative ability and values approximating 0.5 indicating more randomness. The training and validation set C indices were compared to determine the nomogram’s discriminative capacity. Area under the curve (AUC) and decision curve analyses (DCA) were employed to assess the nomogram model’s performance. The observed 1-year, 3-year, and 5-year OS rates were compared with the predicted rates to further evaluate the nomogram's predictive performance.
Risk stratification based on the nomogram
The risk score for each patient was calculated via the nomogram. To establish the optimal cut-off for stratification, the patient population was divided into low- and high-risk groups based on their risk scores. Hypothesis testing was conducted for each group, and the hazard ratio and log-rank test P value were recorded. The optimal cut-off was determined when the log-rank test produced the minimum P value. The Kaplan-Meier method was used to estimate survival curves for both groups.
Statistical analysis
A normality test was conducted for continuous data, which are expressed as the mean ± SD if normally distributed and as the median (p25, p75) if not. Count data are presented as frequencies and percentages. Either an independent samples t test or Mann-Whitney U test was applied for comparisons of continuous data between different groups, depending on the data distribution. A χ2 test was performed to compare count data between groups. LASSO and Cox regression analyses were performed to identify significant predictive variables. LASSO regression analysis was carried out via the "glmnet" package in R. Variable scores were calculated via the "nomogramEx" package, allowing for patient totals to be computed. A DCA of the nomogram was performed via the "rmda" package. All the statistical analyses were conducted via R version 4.1.2 (http://www.r-project.org), with statistical significance defined as a two-sided P value of less than 0.05.
RESULTS
Baseline demographic characteristics
A total of 1431 eligible patients were included in the study. The patients’ average age was 62.0 ± 10.8 years, with 1075 males (75.1%) and 356 females (24.9%). A total of 902 patients (63.0%) had a BMI between 18.5 and 23.9 kg/m², 320 (22.4%) had a BMI of 24 kg/m² or greater, and 209 (14.6%) had a BMI below 18.5 kg/m². Adenocarcinoma was the predominant histological type, affecting 1396 patients (97.6%). Most patients (1332, 93.1%) did not receive radiation therapy. A total of 752 patients (52.6%) received chemotherapy, and 759 (53.0%) underwent surgery. Most (1379, 96.37%) patients did not receive immunotherapy. Table 1 contains demographic, clinical, and pathological characteristics, and treatment-related data for both groups.
Table 1 Baseline characteristics of the training and validation sets.
We selected variables via LASSO regression and generated coefficient profile (Figure 1A) and spectrum plots (Figure 1B). Based on the lambda.min criterion, 16 variables were selected. We also conducted a Cox regression analysis (Table 2) and identified 10 feature variables through stepwise regression.
Figure 1 Clinical predictor selection via least absolute shrinkage and selection operator Cox regression with 10-fold cross-validation.
A: Plot of the least absolute shrinkage and selection operator (LASSO) coefficient distribution for 20 risk factors; B: LASSO regression cross-validation graph.
Table 2 Analysis of survival of patients and risk factors affecting prognosis in the training set.
We then constructed a nomogram model (Figure 2) based on the 10 selected variables. A score was assigned for each risk factor in the model, which was aggregated on the scales to predict 1-, 3-, and 5-OS rates. The points indicated by the tick marks on each variable's axis were summed via a ruler diagram. On the bottom scale, the probabilities of survival at 1, 3, and 5 years were estimated.
Figure 2 The nomogram model illustrates the overall survival of gastric cancer patients in the training set.
BMI: Body mass index; TNM: Tumor-node-metastasis; ALB: Albumin; GLB: Globulin; LDH: Lactate dehydrogenase; PLR: Platelet-lymphocyte ratio.
Model validation
To comprehensively evaluate the model, the time-dependent AUC and C-index were plotted (Figure 3). Both were greater than 0.75 at the 5-year mark, indicating the strong discriminatory ability of the model. Receiver operating characteristic (ROC) curves were then plotted for each timepoint to further assess the model’s performance (Figure 3). The area under the ROC curve values in the validation set at 1-, 3-, and 5 years were 0.843 (95% confidence interval [CI]: 0.817-0.870), 0.850 (95%CI: 0.822-0.877), and 0.821 (95%CI: 0.780-0.862), respectively. In the validation set, the values at 1, 3, and 5 years were 0.864 (95%CI: 0.828-0.900), 0.820 (95%CI: 0.773-0.866), and 0.786 (95%CI: 0.726-0.845), respectively. Calibration curves were also plotted (Figure 4), demonstrating strong accuracy, with the lines connecting the predicted and observed OS at 1, 3, and 5 years near the diagonal line. DCA curves were plotted (Figure 4), revealing favourable net benefits across thresholds ranging from 0.05 to 0.8.
Figure 3 The model's receiver operating characteristic curves and area under the curve results.
A: Receiver operating characteristic (ROC) curve for the training set; B: ROC curve for the validation set; C: Time area under the curve of the model; D: Time C index of the model. AUC: Area under the curve.
Figure 4 Calibration curve and decision curve analysis curve of the model.
A: Calibration curve for the training set; B: Calibration curve for the validation set; C: Decision curve analysis (DCA) curve for the training set; D: DCA curve for the validation set. OS: Overall survival; TNM: Tumor-node-metastasis.
We divided the participants in the training and validation datasets into low- and high-risk groups to further assess the clinical utility of the model. We then plotted Kaplan-Meier curves for each group (Figure 5), demonstrating the model's ability to effectively distinguish between the two risk categories.
Figure 5 Survival analysis of high- and low-risk populations.
A: Training set; B: Validation set.
To facilitate the prognostic assessment of gastric carcinoma patients, we developed a user-friendly web server (https://cqchprognosis.shinyapps.io/gastric_carcinoma/) based on the results of the nomogram model. The web server offers an intuitive display of the predicted survival probabilities at 1, 3, and 5 years for each patient based on the selection of relevant indicators. For example, a patient who is 65 years old, has a BMI of 22.5, is in TNM stage 3, has undergone radiotherapy, chemotherapy, and surgery, and has an ALB level of 42, a GLB level of 36, a neutrophil count of 10, an LDH level of 375, and a PLR of 192, has predicted 1-, 3-, and 5-year survival rates of 85.0%, 65.0%, and 59.0%, respectively.
DISCUSSION
China accounts for 43.9% of all global cases of gastric cancer[12]. Treatment burdens and the need for prognosis and disease assessment raise significant concerns among clinicians, researchers, and patients. Advancements in medicine and computer science, as well as the availability of large-scale medical data, have led to more refined disease status assessments. Traditional TNM staging methods are used to predict patient survival but require complementary methodologies. Several gastric cancer prognostic models have been developed, including nomogram models. These clinically applicable models primarily incorporate pathology, genomics, and imaging factors and exhibit better predictive capabilities than TNM staging. We constructed a nomogram model based on a diverse sample, incorporating demographic data, histopathology, TNM stage, KPS, clinical interventions, blood biochemistry, and complete blood count data. With a larger sample size and the integration of clinical and laboratory indicators, our model potentially provides an even greater reference value.
Advanced age is a risk factor for noncancer-related mortality and poor OS in gastric cancer patients[13]. A retrospective study of postoperative gastric cancer patients identified age as an important prognostic factor, with patients under 30 and over 80 years of age exhibiting poorer overall prognoses[14]. We also identified age as a significant risk factor for reduced OS in gastric cancer patients. However, we observed that younger gastric cancer patients had worse prognoses, which may be due to increased pathological malignancy, enhanced invasiveness, and elevated metabolic activity. The poor prognosis of elderly patients may result from immune system effects like compromised cell-mediated immunity and/or comorbidities. Previous research has indicated that lower BMI values correlated with worse prognoses in gastric cancer patients[15-17]. Patients with a BMI < 18.5 kg/m² exhibit significantly lower OS rates following resection. Our findings suggest that nutrition therapy throughout the entire course of antitumour therapy may be beneficial for gastric cancer patients with a low BMI. Research and practice have shown that TNM staging plays an important role in assessing gastric cancer prognosis and tumour progression[18,19]. We also demonstrated that TNM stage is an independent risk factor for OS in gastric cancer patients. However, factors like age, histopathological type, immune function, and treatment regimens, may complicate the precise prediction of individual outcomes when solely relying on TNM staging. Radiotherapy can increase the 5-year survival rate of resectable gastric cancer patients[20]. Neoadjuvant chemotherapy significantly improves OS rates in operable gastric adenocarcinoma patients without increasing surgical complications[21]. Compared with best supportive care, chemotherapy prolongs the survival of advanced gastric cancer patients[22]. Both curative and palliative resections offer survival benefits for patients with locally advanced gastric cancer[23]. Our study aligns with previous research by showing that radiotherapy, chemotherapy, and surgery are beneficial for enhancing OS in gastric cancer patients when indicated. ALB is an indicator of nutritional status and research[24-26] has shown that higher levels of ALB are associated with improved prognoses. Univariate and multivariate analyses in our study indicated that high ALB levels are favourable for OS in gastric cancer patients, whereas low levels serve as adverse prognostic indicators. This may result from insufficient intake, tumour consumption, and gastrointestinal tumour-induced impairment of ALB absorption in advanced-stage gastric cancer patients. GLB plays a crucial role in immune functions. Research[27] has revealed a correlation between the ALB-to-GLB ratio and prognosis in gastric and other tumour types. Our study suggests that high GLB levels improve OS rates in gastric cancer patients. Neutrophils play essential roles in chemotaxis, phagocytosis, and bactericidal activity and contribute to host antitumour immunity. Tumour-associated neutrophils can be divided into "N1" or "N2" phenotype, both of which inhibit or promote tumour development. The N2 phenotype often prevails[28,29]. Zhang et al[30] has also confirmed that tumour-infiltrating neutrophils in gastric cancer tissues can independently predict OS following chemotherapy. Univariate and multivariate analyses in our study also demonstrated that neutrophils are an independent risk factor influencing gastric cancer survival. Patients with high levels of infiltrating neutrophils tend to have shorter OS. LDH is a glycolytic enzyme, and studies indicate that elevated LDH levels are correlated with poor cancer prognoses. The mechanism underlying this association may involve the activation of several oncogenic signaling pathways by LDH, as well as its involvement in metabolic activity, invasiveness, and immunogenicity in many tumours[31,32]. We also found that LDH significantly predicts OS in gastric cancer patients. PLR is a marker of inflammation and is recognized as a potential indicator of disease progression in gastric cancer patients[33]. An increased PLR has been identified as a prognostic factor for poor OS in gastric cancer patients[34]. In patients with resectable gastric cancer, preoperative PLR is significantly greater than that in healthy individuals, and higher preoperative PLR is correlated with reduced OS[35]. This study suggested that PLR is a risk factor for OS in gastric cancer patients, which is consistent with the findings of previous studies.
We investigated the associations between 12 selected feature variables derived from LASSO regression and Cox regression analyses and the prognosis of patients with gastric cancer. The findings align with prior clinical and foundational research. The constructed nomogram model has been determined to have strong accuracy and discriminative ability. This method can overcome the limitations of TNM staging in predicting the OS of gastric cancer patients, offering insights for clinical assessments and more personalized treatment approaches. In addition, we developed a convenient online calculator through a webpage to help doctors and patients quickly assess survival risk. However, owing to the complexity of malignant tumour formation mechanisms, certain mechanisms remain poorly understood within the medical field. Individual differences exist in the nature and severity of each condition, and multiple factors affect OS of patients with gastric cancer. Our cases were derived from a single centre, and further research with data from diverse regions and expanded study populations is needed for further validation. In future research, we will conduct a multicentre cohort study and use data from other centres to externally validate the model, test its extrapolation ability, and assess its robustness. We previously conducted research and obtained results concerning the application of nomogram models to predict OS in lymphoma[36], lung cancer[37], and various other malignancies[38], laying a foundation for clinical practice and related research. We look forward to our ongoing and future work, which will support and guide the precise prediction of OS in patients with gastric cancer.
CONCLUSION
We created a nomogram model that integrated demographic characteristics, TNM stage, clinical treatment protocols, and laboratory haematological indicators associated with OS in patients with gastric cancer. Through a stepwise regression approach, we identified 12 features, including age, BMI, TNM stage, radiation, chemotherapy, surgery, ALB, GLB, neutrophil count, LDH, and PLR. These features were used to construct a nomogram model for predicting OS in gastric cancer patients. Our findings validated the model's predictive ability and accuracy, potentially equipping clinicians with a reliable tool for estimating OS in gastric cancer patients.
ACKNOWLEDGEMENTS
We thank all participants, as no meaningful research could have been conducted without them.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B, Grade C
Novelty: Grade B, Grade B, Grade C
Creativity or Innovation: Grade B, Grade B, Grade C
Scientific Significance: Grade B, Grade B, Grade C
P-Reviewer: Han T; Ikram D; Mohamed SY S-Editor: Lin C L-Editor: Wang TQ P-Editor: Zhao S
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