A prognostic nomogram utilized lymph node ratio for signet ring cell gastric cancer patients post-surgery

Introduction

Gastric cancer continues to be a significant global health issue, currently ranked as the fifth most prevalent cancer worldwide (1). While the incidence of gastric cancer has steadily declined over the past few decades, particularly in the United States (https://www.iarc.who.int), where it is no longer among the top five causes (5.8 per 100,000), it remains one of the leading causes of cancer-related deaths worldwide (2). Over time, the clinicopathological characteristics of gastric cancer have shifted. The incidence of intestinal-type gastric cancer has decreased, the diffuse subtype, including signet ring cell (SRC) histology as defined by Lauren’s classification, has been steadily rising (1).

The prognosis of gastric cancer varies significantly depending on its histological type. SRC carcinoma is known for its aggressive nature and is generally considered a subtype with poor prognosis in gastric cancer (3-5). However, the prognostic impact of SRC histology is stage-dependent, showing outcomes comparable to or better than non-SRC gastric cancer in early stages, but worse in advanced stages (6,7). In early-stage gastric cancer, SRC is often confined to the mucosa and involves fewer lymph nodes compared to non-SRC cases (3,4). Conversely, advanced SRC gastric cancer is linked to a higher incidence of peritoneal carcinomatosis, increased lymph node involvement, and a reduced chance of curative resection (8-10). Most studies have shown that advanced gastric cancer, especially with SRC histology, is associated with a more advanced stage and poorer outcomes (8,10). Predicting the prognosis of SRC carcinoma, however, remains challenging. Surgery is the primary treatment for gastric cancer, and identifying a reliable method to predict postoperative outcomes could aid in selecting personalized treatment for patients with gastric SRC carcinoma (GSRC).

An accurate, integrated analysis of a patient’s individual situation is essential for predicting postoperative prognosis. Currently, clinical prognostic assessments primarily rely on the tumor node metastasis (TNM) staging system, which does not account for individual factors such as age, gender, tumor location, and size. These individual factors are also important prognostic factors for tumors, and combining these factors can provide better prediction for individuals (11). In clinical practice, the TNM system may lead to stage migration, often due to an insufficient number of lymph nodes being examined (11). To address this issue, the metastatic lymph node ratio (LNR), defined as the proportion of positive lymph nodes among those examined, has been proposed as an alternative to the N staging system to reduce stage migration (12-16). Numerous studies have consistently shown that LNR provides a more refined prognostic prediction than the N stage for gastric cancer patients post-surgery (13-15). Nomograms are widely accepted as reliable tools for assessing risk by incorporating and visualizing key prognostic factors (17). Although numerous nomograms have been created to forecast the prognosis of gastric cancer and its subtypes, none have been specifically tailored for GSRC patients post-surgery (11,18,19).

Therefore, the goal of this research is to integrate individual patient factors to develop a personalized prediction model for patients with GSRC after surgical treatment. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-745/rc).

Methods

Patient selection and data handling

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. We performed a thorough analysis of patients diagnosed with SRC gastric cancer from 2004 to 2021, employing SEER*Stat software version 8.4.4 to gather and evaluate the data. Cases were identified using International Classification of Diseases (ICD)-O-3 morphology code 8490/3. We established specific exclusion criteria to ensure data quality and relevance: (I) patients with multiple primary cancers; (II) patients lacking critical clinicopathological data, including primary tumor site, grade, T stage, M stage, tumor size, details of surgery for the primary site, survival time, and regional lymph node examination results; (III) cases lacking definitive pathology confirmation; and (IV) individuals under 18 years of age.

The variables extracted from the Surveillance Epidemiology, and End Results (SEER) database included age, sex, race, tumor site, histology grade, T stage, M stage, tumor size, surgery, radiation, chemotherapy, primary tumor site, surgery for the primary site, regional lymph node examination, regional lymph nodes positive, follow-up time, cancer-specific death, and all-cause death. Patients were categorized as either married or unmarried, with the latter group including those who were single, divorced, separated, or widowed. Age was divided into two groups: under 60 years and 60 years or older. Tumor size was classified into three categories: <3, 3–6, and >6 cm. The primary tumor site was grouped into two categories: cardia and non-cardia.

Nomogram construction and validation

A total of 3,481 patients diagnosed with SRC gastric cancer, with complete clinical and survival data, were included in the study. The patients diagnosed between 2004 and 2015 were randomly assigned to a training set (n=2,052) and a validation set (n=879) at a ratio of 7:3. Randomization and data analysis were conducted using R software. Additionally, patients diagnosed between 2016 and 2021 were selected as second validation set. To identify prognostic factors, both univariate and multivariate Cox regression analyses were executed. Survival nomograms for cancer-specific survival (CSS) and overall survival (OS) were then developed based on the significant factors found in the training set. Validation of the nomograms was carried out using the Hosmer-Lemeshow test, and calibration curves were generated through bootstrapping with 1,000 samples to compare actual outcomes with nomogram-predicted survival. The predictive performance of the nomograms was additionally evaluated through Harrell’s concordance index (C-index) and the receiver operating characteristic (ROC) curve.

Statistical analysis

Statistical analyses were performed using SPSS version 21.0 and R software version 4.0.2, along with various R packages, including survival, survminer, rms, and pROC, were used to create visualizations such as nomograms, calibration plots, and ROC curves. Frequencies and percentages were used to represent categorical variables. To assess the differences in clinical parameters between the training and validation sets, the χ² test or Fisher’s exact test was applied for categorical variables, and one-way analysis of variance (ANOVA) was employed for continuous variables. The relationship between LNR and survival rates was evaluated using Pearson correlation analysis. Moreover, ROC curve analysis was carried out to determine the optimal cut-off point for LNR. The study endpoints included OS and CSS. The Kaplan-Meier method was employed to estimate survival probabilities, with a two-sided P value of less than 0.05 considered statistically significant.

Results

Clinicopathologic characteristics of the patients

This study involved 3,481 patients with SRC gastric cancer, all drawn from SEER databases. The training cohort consisted of 2,052 cases, whereas the first validation cohort comprised 879 cases, the second validation cohort comprised 550 cases. LNR exhibited a negative correlation with both OS and CSS, with Pearson correlation coefficients of 0.427 (P<0.001) and 0.420 (P<0.001), respectively. The ideal cut-off point for LNR is determined by both ROC curve analysis and its clinical significance. Patients were classified into three groups: LNR0, LNR1 (ranging from 0.001 to 0.155), and LNR2 (ranging from 0.155 to 1.000). The longest follow-up period for all patients reached 155 months, while the median survival time was 24 months. In the training dataset, 1,314 (64.0%) patients died, while 542 (61.7%) patients died in the first validation dataset, and 292 (53.1%) patients died in the second validation dataset; 1,125, 480 and 239 dead caused by SRC gastric cancer, respectively. Of the patients, 1,260 (36.2%) were classified as LNR2 and 1,061 (30.5%) as LNR1, 1,160 (33.3%) as LNR0. 1,746 (50.2%) were male. The main race was White (65.5%) follow by other race 791 (22.7%). According to the age cutoff value of 60 years, 1,893 patients (54.4%) were older than 60 years. In terms of tumor size, using the optimal cutoff values (size <3, 3–6, >6 cm), 1,200 patients (34.5%) had tumors measuring less than 3 cm, whereas 1,144 patients (32.9%) had tumors exceeding 6 cm. Most of the T stage is T4 (1,376, 39.5%). 3,052 (87.7%) were in M0 stage. Gastrectomy (50.6%) was the most common surgical type. Most patients (60.5%) accepted chemotherapy while a large part of patients (64.7%) was without radiation therapy. Most patients (82.9%) were married. Table 1 displays the clinicopathological features of the patients from the training dataset, the first validation dataset and the second valid dataset.

Independent predictor of survival assessed in the SRC gastric cancer

By applying both simple and multiple Cox proportional hazards regression analyses, eight distinct factors linked to SRC gastric cancer were revealed. Univariate analysis showed that sex (P=0.90), marital status (P=0.33), and radiation therapy (P=0.10) had no significant impact on OS. Similarly, sex (P>0.99), marital status (P=0.16), and radiation therapy (P=0.19) were not significantly associated with CSS. Upon incorporating these variables into the Cox proportional hazards regression model, we discovered that race, chemotherapy, T stage, M stage, age, tumor size, tumor primary site, and LNR were independently associated with CSS and OS in patients following the resection of SRC gastric cancer. Tables 2,3 present the results of variable selection, including hazard ratios and P values.

Development and verification of the OS and CSS nomograms

Using the independent factors, nomograms incorporating all predictors were developed to estimate OS and CSS at 1, 3, and 5 years (Figure 1). The nomograms indicated that T stage was the primary predictor, with LNR, M stage, and chemotherapy following closely behind. The internal validation carried out within the training cohort demonstrated C-index values of 0.748 [95% confidence interval (CI): 0.734–0.763] for OS and 0.763 (95% CI: 0.761–0.764) for CSS in the predictions made by the nomogram. In the first validation cohort, the analysis revealed C-index values of 0.746 (95% CI: 0.702–0.791) for OS and 0.751 (95% CI: 0.706–0.796) for CSS. In the second validation cohort, the analysis showed C-index values of 0.784 (95% CI: 0.733–0.836) for OS and 0.818 (95% CI: 0.767–0.870) for CSS. The calibration curves illustrated a strong correlation between the estimated and observed probabilities of OS and CSS post-surgery in the training, the first validation cohorts and the second validation cohorts (Figures 2,3). Subsequently, we assessed the discriminative capacity of the nomogram across different years (Figures 4,5). Within the training group, the area under the curve (AUC) values for CSS were recorded as 0.760 at 1 year, 0.821 at 3 years, and 0.833 at 5 years. The first validation cohort yielded AUC values of 0.738 at 1 year, 0.800 at 3 years, and 0.824 at 5 years for CSS. The second validation cohort yielded AUC values of 0.808 at 1 year, 0.872 at 3 years, and 0.885 at 5 years for CSS. Regarding OS, the training group showed AUC values of 0.737 at 1 year, 0.812 at 3 years, and 0.825 at 5 years. In comparison, the first validation cohort presented AUC values of 0.727 at 1 year, 0.789 at 3 years, and 0.813 at 5 years, while the second validation cohort showed AUC values of 0.792 at 1 year, 0.851 at 3 years, and 0.839 at 5 years. These findings indicate that our nomogram may be effectively applied in clinical practice with enhanced accuracy.

Figure 1 Nomograms for predicting the 1-, 3- and 5-year overall survival (A), and 1-, 3- and 5-year cancer-specific survival (B) of patients with signet ring cell gastric cancer after surgery. For example, a 63-year-old single White patient with a tumor located at the cardia underwent gastrectomy with no metastasis. The pathology report revealed signet ring cell carcinoma, with a tumor size of 3.8 cm and a T stage of T3. A total of 32 lymph nodes were removed during surgery, 12 of which tested positive, and the patient did not receive chemotherapy. The patient’s age contributed 20 points, T stage contributed 82 points, race contributed 10 points, no chemotherapy contributed 34.5 points, no metastasis contributed 0 points, tumor size contributed 9.5 points, tumor site contributed 12.5 points, and LNR contributed 49 points. So, if you want to figure out the patient’s 1-, 3-, and 5-year survival odds based on a score of roughly 217.5, here’s the lowdown. First, find that 217.5 spot on the Total Points line. Then, just drop a straight line down from there, and see where it crosses the survival lines. The intersections will give you the respective survival probabilities. For this patient, the total score of approximately 217.5 corresponds to a 1-year survival probability of around 40%, a 3-year survival probability of approximately 5%, and no survival predicted at 5 years. LNR, lymph node ratio.

Figure 2 The calibration curves for predictions of overall survival in the training cohort (A), the first validation cohort (B) and the second validation cohort (C) at 1, 3 and 5 years after surgery. OS, overall survival.

Figure 3 The calibration curves for predictions of cancer specific survival in the training cohort (A), the validation cohort (B) and the second validation cohort (C) at 1, 3 and 5 years after surgery. CSS, cancer-specific survival.

Figure 4 The ROC curves for predictions of overall survival in the training cohort (A), the validation cohort (B) and the second validation cohort (C) at 1, 3 and 5 years after surgery. Black dashed line: random guessing baseline (AUC =0.5). AUC, area under the curve; FPR, false positive ratio; OS, overall survival; ROC, receiver operating characteristic; TPR, true positive ratio.

Figure 5 The ROC curves for predictions of cancer specific survival in the training cohort (A), the validation cohort (B) and the second validation cohort (C) at 1, 3 and 5 years after surgery. Black dashed line: random guessing baseline (AUC =0.5). AUC, area under the curve; CSS, cancer-specific survival; FPR, false positive ratio; ROC, receiver operating characteristic; TPR, true positive ratio.

Discussion

In this research, we formulated nomograms to estimate the survival of patients with SRC gastric cancer following surgical procedures. SRC gastric cancer is a histological subtype known for poor survival outcomes in gastric cancer cases (5). It presents distinct clinicopathological characteristics compared to non-SRC types (5). The SRC histology in gastric cancer has been extensively studied, though its prognostic value remains somewhat controversial (5,8,9). The stage at diagnosis seems to play a role in determining the prognosis of SRC gastric cancer (20). In early gastric cancer, SRC is often confined to the mucosa and is associated with fewer lymph node invasions compared to non-SRC cases. It is also more frequently observed in younger patients and is more prevalent in females at presentation (4,5,21). Several studies have reported similar or better OS for early-stage SRC (7,12,22). However, in advanced gastric cancer, SRC histology is linked to more aggressive tumor behavior and worse outcomes (9,10,23). SRC gastric cancer is typically diagnosed at a more advanced stage, reflecting its aggressive nature (10). Incomplete resections are more common in these cases, and a significant number of patients experience early recurrence or complications following curative surgery (22). The contrasting prognosis between early and advanced SRC gastric cancer suggests a complex tumor biology, making survival prediction more challenging.

Nomograms provide a powerful visual tool for cancer prognosis. By incorporating multiple prognostic factors, they allow clinicians to make more accurate predictions about individual patient outcomes and tailor treatment strategies accordingly (17). While TNM staging remains the most crucial prognostic system, studies have shown that nomograms offer superior discriminatory ability compared to TNM staging in predicting outcomes for gastric cancer patients (24,25). Several prognostic nomograms exist for predicting gastric cancer outcomes; however, histological type has not been consistently considered as an independent factor (11,18,26). This omission may lead to inaccuracies when predicting survival for operable SRC gastric cancer patients, given its complex tumor biology. Thus, creating a model that effectively assesses the prognosis of SRC gastric cancer is important. To the best of my understanding, this study marks the first initiative to analyze and establish nomograms specifically designed to forecast the outcomes for patients with SRC gastric cancer after they undergo surgical intervention.

Our analysis revealed that several factors—including age at diagnosis, race, tumor size, chemotherapy, T and M stages and LNR—are independent prognostic indicators for OS and CSS in patients undergoing surgery for SRC gastric cancer. The independent prognostic factors identified in our study are consistent with those found in several previous studies. Kwon et al. found that age and TNM stage was an independent prognostic factor (27). Another study reports that younger age is linked to improved survival, despite the fact that younger patients typically present with more advanced-stage disease (28). Papers also report that adjuvant chemotherapy improved OS (29). Zhou et al. found that SRC gastric cancer displays unique biological characteristics, typically manifests as a large tumor (≥49 mm), and is linked to worse outcomes (30). While these factors have not all been previously assembled into a usable clinical tool.

Although there have been major advancements in the treatment of gastric cancer, including targeted therapy, chemotherapy, and immunotherapy, radical surgical resection continues to be the primary intervention for patients diagnosed with this disease (31). The conventional TNM staging system is frequently used to assess the prognosis of gastric cancer after surgery. While TNM stage is effected by the dissected lymph node numbers (32). According to the American Joint Committee on Cancer (AJCC) 8^th edition guideline, a minimum of 15 lymph node dissections is recommended. In this study, the median lymph node examined was 16, which means that lymph node dissections of half of patients was less than 16. This will inevitably cause N stage migration and inaccurate prediction. As a result, researchers have suggested that lymph node metastasis evaluations be performed using the LNR, which represents the proportion of pathologically metastatic lymph nodes to the total number of nodes examined. Recently, the LNR has become an essential prognostic indicator for patients with gastric cancer after surgery, frequently considered to provide greater prognostic value than N staging (14,16,33). Unlike the traditional N stage, LNR specifically focuses on the ratio of lymph node metastasis. Therefore, patients with fewer than 15 lymph nodes can be staged more accurately. Through univariate and multivariate analyses, our study identified LNR as a key prognostic indicator for both OS and CSS, consistent with previous research highlighting LNR as a reliable prognostic factor post-curative gastrectomy, alongside N stage. In this study, the optimal cutoff point for LNR, determined by ROC curve analysis from the training cohort, is 0.155. According to the nomograms, the importance of LNR ranks just below T staging. An increasing LNR is associated with a poorer prognosis, as demonstrated by our results, which are consistent with previous studies (13,15).

This study made use of data from the SEER database, which provides an extensive sample size and a wealth of information. Nonetheless, it is important to note certain limitations. First, the omission of patients with missing data on essential variables could have resulted in selection bias. Second, the SEER database lacks critical prognostic details, such as surgical margin status, vascular invasion, genetic profiles, and comprehensive treatment data. Third, the use of commonly defined categorical variables like age and tumor location may limit the generalizability of our results. Lastly, although internal validation of our nomogram was performed, external validation with independent datasets is required to further substantiate its predictive accuracy.

Conclusions

We have successfully developed effective nomograms to evaluate the prognosis of patients with SRC gastric cancer, focusing on both OS and cumulative survival CSS. These nomograms integrate key clinical factors and provide valuable tools for personalized patient prognosis, enhancing clinical decision-making and potentially improving treatment outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-745/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-745/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-745/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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

References

1. Yang WJ, Zhao HP, Yu Y, et al. Updates on global epidemiology, risk and prognostic factors of gastric cancer. World J Gastroenterol 2023;29:2452-68. [Crossref] [PubMed]
2. Thrift AP, El-Serag HB. Burden of Gastric Cancer. Clin Gastroenterol Hepatol 2020;18:534-42. [Crossref] [PubMed]
3. Zhao B, Lv W, Zhang J, et al. Different prognostic significance of signet ring cell histology for early and advanced gastric cancer patients: a systematic review and meta-analysis. Expert Rev Gastroenterol Hepatol 2020;14:499-509. [Crossref] [PubMed]
4. Kao YC, Fang WL, Wang RF, et al. Clinicopathological differences in signet ring cell adenocarcinoma between early and advanced gastric cancer. Gastric Cancer 2019;22:255-63. [Crossref] [PubMed]
5. Wei Q, Gao Y, Qi C, et al. Clinicopathological Characteristics and Prognosis of Signet Ring Gastric Cancer: A Population-Based Study. Front Oncol 2021;11:580545. [Crossref] [PubMed]
6. Li Y, Zhu Z, Ma F, et al. Gastric Signet Ring Cell Carcinoma: Current Management and Future Challenges. Cancer Manag Res 2020;12:7973-81. [Crossref] [PubMed]
7. Chon HJ, Hyung WJ, Kim C, et al. Differential Prognostic Implications of Gastric Signet Ring Cell Carcinoma: Stage Adjusted Analysis From a Single High-volume Center in Asia. Ann Surg 2017;265:946-53. [Crossref] [PubMed]
8. Efared B, Kadi M, Tahiri L, et al. Gastric Signet Ring Cell Carcinoma: A Comparative Analysis of Clinicopathologic Features. Cancer Control 2020;27:1073274820976596. [Crossref] [PubMed]
9. Machlowska J, Pucułek M, Sitarz M, et al. State of the art for gastric signet ring cell carcinoma: from classification, prognosis, and genomic characteristics to specified treatments. Cancer Manag Res 2019;11:2151-61. [Crossref] [PubMed]
10. Yen HH, Chen PY, Huang RY, et al. Clinicopathological features and cancer transcriptomic profiling of poorly cohesive gastric carcinoma subtypes. J Pathol Clin Res 2024;10:e12387. [Crossref] [PubMed]
11. Zhao E, Zhou C, Chen S. Prognostic nomogram based on log odds of positive lymph nodes for gastric carcinoma patients after surgical resection. Future Oncol 2019;15:4207-22. [Crossref] [PubMed]
12. Park J, Jeon CH, Kim SJ, et al. A Novel Approach for Gastric Cancer Staging in Elderly Patients Based on the Lymph Node Ratio. J Gastric Cancer 2021;21:84-92. [Crossref] [PubMed]
13. Beirat AF, Amarin JZ, Suradi HH, et al. Lymph node ratio is a more robust predictor of overall survival than N stage in stage III colorectal adenocarcinoma. Diagn Pathol 2024;19:44. [Crossref] [PubMed]
14. Acar O, Balcik OY, Urun M, et al. Positive Lymph Node Ratio as a new prognostic score in Geriatric patients with operated gastric cancer. Heliyon 2024;10:e38809. [Crossref] [PubMed]
15. Ergenç M, Uprak TK, Akın Mİ, et al. Prognostic significance of metastatic lymph node ratio in gastric cancer: a Western-center analysis. BMC Surg 2023;23:220. [Crossref] [PubMed]
16. Chu Y, Mao T, Li X, et al. Predictors of Lymph Node Metastasis and Differences Between Pure and Mixed Histologic Types of Early Gastric Signet-ring Cell Carcinomas. Am J Surg Pathol 2020;44:934-42. [Crossref] [PubMed]
17. Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol 2015;16:e173-80. [Crossref] [PubMed]
18. Lin F, Chen X, Liang C, et al. Development and validation of a programmed cell death index to predict the prognosis and drug sensitivity of gastric cancer. Front Pharmacol 2024;15:1477363. [Crossref] [PubMed]
19. Huang L, Feng B, Yang Z, et al. A Transfer Learning Radiomics Nomogram to Predict the Postoperative Recurrence of Advanced Gastric Cancer. J Gastroenterol Hepatol 2025;40:844-54. [Crossref] [PubMed]
20. Lin CL, Zhu GW, Huang YJ, et al. Operable gastric adenocarcinoma with different histological subtypes: Cancer-specific survival in the United States. Saudi J Gastroenterol 2020;26:46-52. [Crossref] [PubMed]
21. Dal Cero M, Bencivenga M, Liu DHW, et al. Clinical Features of Gastric Signet Ring Cell Cancer: Results from a Systematic Review and Meta-Analysis. Cancers (Basel) 2023;15:5191. [Crossref] [PubMed]
22. Ma Y, Wang Y, Hu C, et al. The percentages of signet-ring cells (SRCs) affects the prognosis after radical gastrectomy for advanced gastric cancer. Langenbecks Arch Surg 2023;408:376. [Crossref] [PubMed]
23. Yu C, Yang J, Li H, et al. Prognostic prediction and treatment options for gastric signet ring cell carcinoma: a SEER database analysis. Front Oncol 2024;14:1473798. [Crossref] [PubMed]
24. Zhang H, Yang W, Tan X, et al. Long-term relative survival of patients with gastric cancer from a large-scale cohort: a period-analysis. BMC Cancer 2024;24:1420. [Crossref] [PubMed]
25. Xiao H, Li H, Jian L, et al. Prognostic factors and adjuvant chemotherapy efficacy in stage I gastric cancer patients: a retrospective analysis. BMC Gastroenterol 2024;24:474. [Crossref] [PubMed]
26. Yan J, Liu Y, Liu T, et al. A predictive and prognostic model for metastasis risk and prognostic factors in gastrointestinal signet ring cell carcinoma. Eur J Med Res 2024;29:545. [Crossref] [PubMed]
27. Kwon KJ, Shim KN, Song EM, et al. Clinicopathological characteristics and prognosis of signet ring cell carcinoma of the stomach. Gastric Cancer 2014;17:43-53. [Crossref] [PubMed]
28. Ren J, Niu G, Wang X, et al. Effect of Age on Prognosis of Gastric Signet-Ring Cell Carcinoma: A SEER Database Analysis. Med Sci Monit 2018;24:8524-32. [Crossref] [PubMed]
29. Zhao Z, Yan N, Pan S, et al. The value of adjuvant chemotherapy in stage II/III colorectal signet ring cell carcinoma. Sci Rep 2020;10:14126. [Crossref] [PubMed]
30. Zhou L, Li W, Cai S, et al. Large tumor size is a poor prognostic factor of gastric cancer with signet ring cell: Results from the surveillance, epidemiology, and end results database. Medicine (Baltimore) 2019;98:e17367. [Crossref] [PubMed]
31. Guan WL, He Y, Xu RH. Gastric cancer treatment: recent progress and future perspectives. J Hematol Oncol 2023;16:57. [Crossref] [PubMed]
32. Sexton RE, Al Hallak MN, Diab M, et al. Gastric cancer: a comprehensive review of current and future treatment strategies. Cancer Metastasis Rev 2020;39:1179-203. [Crossref] [PubMed]
33. Hou Y, Wang X, Chen J. Prognostic significance of metastatic lymph node ratio: the lymph node ratio could be a prognostic indicator for patients with gastric cancer. World J Surg Oncol 2018;16:198. [Crossref] [PubMed]