Construction and testing of a nomogram for patients with pancreatic signet-ring cell carcinoma and analysis of treatment-related survival differences: a study based on the Surveillance, Epidemiology, and End Results (SEER) database

Introduction

Cancer has become one of the most prevalent diseases worldwide, and pancreatic cancer (PC) is a highly malignant tumor of the digestive system (1). Most of PC patients are already in advanced stages before the appearance of clinical symptoms, such as abdominal pain or abdominal masses, with a poor prognosis (2). According to the U.S. cancer data, PC is going to become the second most common cancer in Western Europe and North America after lung cancer by 2030, posing a serious threat to human health worldwide (3).

The 5-year survival rate of PC was less than 5%in the 90s (4). The 5-year survival rate of PC patients is only 12%, and its 5-year survival rate is still much lower than that of other malignant tumors although surgery, radiotherapy, chemotherapy, targeted therapy, immunotherapy and other treatment methods have made considerable progress in recent years (5). The pathological manifestations and clinical prognosis of distinct pathological PC types are extremely different (6). Signet ring cell carcinoma (SRCC) is a unique and rare pathological form, with a very low incidence rate, accounting for less than 0.5% (7). Since SRCC was first described in 1951, although several studies have characterized this pathological type, little is known about its pathogenesis, associated risk factors, and the potential impact of treatment (8). SRCC is a poorly mucinous cancer that secretes mucus that is not excreted from the cell. The accumulated mucus pushes the nucleus towards the periphery of the cell, thus giving the cell the appearance of a ring. More than 50% of the tumor cells are rich in cytoplasmic mucins and eccentric crescent-shaped nuclei; the prognosis is poor due to the lack of intercellular adhesion proteins, leading to metastasis and giving SRCC worse histological features than general adenocarcinoma (9). SRCC originates from undifferentiated or poorly differentiated stem cells in the lamina propria of the mucosa, which diffuse into the submucosa where they disseminate, leading to distant metastasis, consequently making SRCC a highly aggressive pathological type (10). Pancreatic signet ring cell carcinoma (PSRCC) is less common than PC limited lesions (3.0% vs. 11%) and more often occurs in conjunction with distant metastases (69.4% vs. 52%), resulting in a significant decrease in the overall survival (OS) (7). SRCC is most common in the gastrointestinal tract, but varies widely depending on the location, with the pancreas resulting in the worst median OS in gastrointestinal SRCC (11). At present, its very poor prognostic characteristics highlight the importance of effective clinical treatment modalities. The treatment of PSRCC in clinical practice mainly relies on the PC treatment guidelines as a reference (12). Surgery and chemotherapy are the main treatments to combat PC (13). However, most patients are in an advanced stage or have distant metastases at the time of diagnosis, and surgery is recommended only in a small proportion of patients (14). The Surveillance, Epidemiology, and End Results (SEER) database provides information on the treatment of PSRCC patients. Therefore, the primary objective of this study is to use the epidemiology of PSRCC patients, related treatment and prognosis information found in the SEER database to construct a prognostic model to predict OS in PSRCC patients and analyze the prognostic differences among PSRCC patients subjected to different treatment modalities, so as to provide a reference for the treatment of PSRCC patients (15). We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0134/rc).

Methods

Data sources

The SEER database covers approximately 30% of the U.S. population and provides comprehensive data, including clinical and pathological characteristics, demographic information, and survival outcomes (16). Using the SEER*Stat software, data of patients diagnosed with PSRCC from 2000 to 2020 were extracted from the SEER database. The inclusion criteria were the following: (I) time: 2000–2020; (II) SRCC of primary pancreas; (III) histopathology consistent with the International Classification of Oncological Diseases, third edition (ICD-O-3; code 8490/3); (IV) complete survival information. The exclusion criteria were the following: (I) non-primary cancer; (II) multiple primary tumors; (III) patients with reports derived from autopsies and death certificates; (IV) no prognostic data. The above criteria led to a selection of a total of 618 patients with PSRCC included in this study. These patients were randomly assigned to the training set (n=432) and the testing set (n=186) (Figure 1).

Figure 1 Flowchart of patient screening and grouping. PSRCC, pancreatic signet ring cell carcinoma; SEER, Surveillance, Epidemiology, and End Results.

Outcome variables

SEER database information was downloaded using SEER*stat 8.4.2 software. Information on variables in the SEER database included the following: (I) baseline demographic characteristics, including age at diagnosis, sex (male and female), race [White, Black and other (including Asian and American Indian/indigenous)], marital status [married, unmarried, and other (divorced and widowed)], and economic status. (II) Tumor characteristics, including maximum tumor diameter, site of origin (head of pancreas, body of pancreas, tail of pancreas, whole pancreas, and unknown), American Joint Committee on Cancer (AJCC) stage (stage I/II, III/IV), histological grade (G1/G2, G3/G4). (III) Survival outcomes, including OS, cancer-specific survival (CSS), and survival status. The primary endpoints of this study are OS and CSS. OS is defined as the time from diagnosis to death or last follow-up. CSS is defined as the time from diagnosis to death due to PSRCC or the last follow-up.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study used the SEER database of the American Institute of Cancer Research, cancer is a reportable disease in the United States, and the SEER database is an open and shared platform; Thus, the included data do not require the informed consent of the patient, and the study does not need to be approved by the medical ethics committee.

Statistical analysis

Statistical analysis was performed using SPSS 26.0 statistical software and R software (version 4.3.2). The optimal cut-off values of age, economic income and score (age <70 years old, ≥70 years old, economic income <$45,000, ≥$45,000) were found by the analysis of the receiver operating characteristic (ROC) curve. Continuous variables are presented as the mean ± standard deviation or the median (interquartile range), while categorical variables are presented as frequencies (percentages). Kaplan-Meier survival curves were used to compare survival differences between the two patient groups. In the training set, independent prognostic factors for PSRCC patients were identified through univariate and multivariate Cox regression analyses. These factors were then incorporated into a nomogram to predict 1-, 3-, and 5-year CSS and OS in PSRCC patients. The consistency between predicted probabilities and observed outcomes was analyzed using time-dependent ROC curves and calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. The model was then validated on the testing set. Finally, Kaplan-Meier survival curves were used to analyze survival differences among PSRCC patients across different treatment modalities. A two-sided P value <0.05 was considered statistically significant.

Results

Basic features

A total of 618 patients with PSRCC were included in this study according to the inclusion and exclusion criteria. These patients were randomly assigned to the training set (n=432) and the testing set (n=186). The baseline characteristics were balanced between the two groups (Table 1). The median age at diagnosis was 68 years; more than 41.59% of patients were over 70 years old. The incidence was slightly higher among male patients than among female patients (55.50% vs. 44.50%). The most common site of disease was the pancreatic head, accounting for 294 cases (47.57%). The histological grade was generally poor, with 237 cases (38.35%) classified as high grade (G3/G4) and 33 cases (5.34%) as low grade (G1/G2). The majority were already at stage III/IV at the time of diagnosis, accounting for 40.94%, while only 15.70% of patients were at stage I/II. A total of 344 (55.66%) patients received tumor-related treatment (surgery, chemotherapy, radiotherapy); 121 patients (19.58%) underwent cancer-related surgical treatment. A total of 281 patients (45.47%) received chemotherapy, and 35 patients (5.66%) received radiotherapy (Table 1).

Kaplan-Meier survival analysis of CSS and OS using training and testing sets

In both the training and testing sets, Kaplan-Meier survival curve analysis showed no significant differences in CSS (Figure 2A) and OS (Figure 2B) between the two groups of patients. In the training set, the 1-, 3-, and 5-year CSS rates were 9.81%, 5.81%, and 5.81%, respectively. The 1-, 3-, and 5-year OS rates were 8.72%, 5.17%, and 5.17%, respectively. In the testing set, the 1-, 3-, and 5-year CSS rates were 9.32%, 4.30%, and 4.30%, respectively, while the 1-, 3-, and 5-year OS rates were 7.49%, 3.46%, and 2.88%, respectively.

Figure 2 Kaplan-Meier survival curves showing CSS and OS for the training and testing sets. (A) Kaplan-Meier survival curves showing CSS for the training and testing sets. (B) Kaplan-Meier survival curves showing OS for the training and testing sets. CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; OS, overall survival.

Construction and testing model for PSRCC patient prognosis

To identify independent prognostic factors for CSS and OS, we performed univariate and multivariate Cox regression analyses. In the training set, univariate Cox regression analysis revealed that the site of origin (pancreatic head), AJCC stage, and histological stage were significantly associated with CSS in patients with PSRCC (Table 2). Age, site of origin (pancreatic head), AJCC stage, and histological stage were significantly associated with OS in PSRCC patients (P<0.05) (Table 3). Multivariate Cox regression analysis revealed that age, site of origin (pancreatic head), and AJCC stage were independent prognostic factors for both CSS and OS in PSRCC patients (P<0.05) (Tables 2,3).

In the testing set, univariate Cox regression analysis revealed that AJCC stage and histological grade were significantly associated with CSS in PSRCC patients (Table 4). AJCC staging was significantly associated with OS in PSRCC patients (P<0.05) (Table 5). Multivariate Cox regression analysis revealed that age, site of origin (pancreatic head), and AJCC staging were independent prognostic factors for CSS and OS in PSRCC patients (P<0.05) (Tables 4,5).

To enhance its potential clinical utility, this study selected independent prognostic factors affecting CSS and OS in patients with PSRCC (including age, site of origin, and AJCC stage) as variables to jointly construct nomograms for CSS and OS (Figure 3). Each patient was individually stratified based on the scores of the different variables in the nomogram, and the total score was used to predict 1-, 3-, and 5-year CSS and OS in patients with PSRCC.

Figure 3 Prognostic nomograms. (A) Prognostic nomogram for CSS. (B) Prognostic nomogram for OS. AJCC, American Joint Committee on Cancer; CSS, cancer-specific survival; OS, overall survival.

A time-dependent ROC curve analysis was performed to systematically evaluate the predictive performance of this nomogram in estimating survival rates. The C-statistic and area under the curve (AUC) were used as quantitative measures of the model’s discriminatory ability. The results indicated that the model demonstrated good predictive accuracy in both the training and testing sets. Regarding CSS prediction, the C-statistic for the training set was 0.62 [95% confidence interval (CI): 0.59–0.66], and the AUC values for 1-, 3-, and 5-year survival were 0.72 (95% CI: 0.65–0.79), 0.85 (95% CI: 0.76–0.94), and 0.87 (95% CI: 0.76–0.99), respectively. In the testing set, the C-index was 0.59 (95% CI: 0.53–0.64), and the AUC values at 1, 3, and 5 years were 0.66 (95% CI: 0.56–0.76), 0.70 (95% CI: 0.49–0.91), and 0.73 (95% CI: 0.41–1.05), respectively (Figure 4).

Figure 4 ROC curves and AUC values for 1-, 3-, and 5-year CSS in the training and testing sets. (A) ROC curves for 1-year CSS in the training and testing sets. (B) ROC curves for 3-year CSS in the training and testing sets. (C) ROC curves for 5-year CSS in the training and testing sets. AUC, area under the curve; CSS, cancer-specific survival; ROC, receiver operating characteristic.

Regarding OS prediction, the C-statistic for the training set was 0.63 (95% CI: 0.60–0.67), with 1-, 3-, and 5-year AUC values of 0.74 (95% CI: 0.66–0.83), 0.85 (95% CI: 0.75–0.94), and 0.87 (95% CI: 0.76–0.99), respectively. In the testing set, the C-statistic was 0.59 (95% CI: 0.54–0.64), with corresponding AUC values of 0.67 (95% CI: 0.54–0.79), 0.71 (95% CI: 0.49–0.92), and 0.73 (95% CI: 0.41–1.05) (Figure 5). These results indicate that the predictive model developed in this study exhibits high discriminatory accuracy across different datasets.

Figure 5 ROC curves and AUC values for 1-, 3-, and 5-year OS in the training and testing sets. (A) ROC curves for 1-year OS in the training and testing sets. (B) ROC curves for 3-year OS in the training and testing sets. (C) ROC curves for 5-year OS in the training and testing sets. AUC, area under the curve; OS, overall survival; ROC, receiver operating characteristic.

In addition, the accuracy of the predicted 1-, 3-, and 5-year survival probabilities in the training and testing sets were evaluated through calibration curve analysis. Under ideal calibration conditions, the predicted values should coincide exactly with the 45-degree reference line. For CSS predictions, the calibration curves indicate good agreement between the nomogram’s predicted values and the observed values, confirming its satisfactory calibration capability (Figure 6). Similarly, the calibration curves for OS predictions also demonstrate good agreement (Figure 7), further validating the model’s calibration performance.

Figure 6 Nomogram calibration curves for predicting 1-, 3-, and 5-year CSS in the training and testing sets. (A) Calibration curve for 1-year CSS in the training set. (B) Calibration curve for 3-year CSS in the training set. (C) Calibration curve for 5-year CSS in the training set. (D) Calibration curve for 1-year CSS in the testing set. (E) Calibration curve for 3-year CSS in the testing set. (F) Calibration curve for 5-year CSS in the testing set. CSS, cancer-specific survival.

Figure 7 Nomogram calibration curves for predicting 1-, 3-, and 5-year OS in the training and testing sets. (A) Calibration curve for 1-year OS in the training set. (B) Calibration curve for 3-year OS in the training set. (C) Calibration curve for 5-year OS in the training set. (D) Calibration curve for 1-year OS in the testing set. (E) Calibration curve for 3-year OS in the testing set. (F) Calibration curve for 5-year OS in the testing set. OS, overall survival.

Based on the nomogram model, we calculated the risk scores for each PSRCC patient in the training set and identified the optimal cutoff value. Using this cutoff value, patients in the training set were divided into two subgroups: high-risk and low-risk. The stratification criteria for CSS were as follows: high-risk group (score >−0.14) and low-risk group (score ≤−0.14). The stratification criteria for OS were: high-risk group (score >−0.18) and low-risk group (score ≤−0.18). Subsequently, Kaplan-Meier survival curves were used to analyze the differences in prognosis between the two groups. The results showed that the low-risk group significantly outperformed the high-risk group in terms of both CSS and OS (P<0.01) (Figure 8).

Figure 8 Kaplan-Meier survival analysis of patients in the training set stratified by risk group. (A) Kaplan-Meier survival curve for CSS. (B) Kaplan-Meier survival curve for OS. CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; OS, overall survival.

Further evaluation of the model’s clinical utility using DCA. The DCA show that, in the training set, when the model threshold was set within the range of 50–97%, the decision curve lay above both the none and all lines (Figure 9A). In the testing set, when the model threshold was set within the range of 50–92%, the decision curve lay above both the none and all lines, and the DCA indicated that the model has clinical utility (Figure 9B).

Figure 9 DCA for predicting the prognosis of PSRCC patients. (A) DCA of the training set. (B) DCA of the testing set. DCA, decision curve analysis; PSRCC, pancreatic signet ring cell carcinoma.

OS analysis of different treatment modalities

Sub-stratified analysis was performed considering the development of drugs, surgical techniques, and advances in radiation therapy techniques in the last decade, which was divided into four different time periods (2000–2005, 2006–2010, 2011–2015, and 2016–2020) according to the time of diagnosis. The Kaplan-Meier survival curves showed no significant difference in the OS of the patients (Figure 10A). The difference in OS of PSRCC patients subjected to different treatment modalities (surgery, radiotherapy, chemotherapy, surgery combined with chemotherapy, surgery combined with radiotherapy, radiotherapy combined with chemotherapy, and surgery combined with chemoradiotherapy) was also assessed, revealing that the combination therapy prolonged the OS of PSRCC patients compared with the monotherapy, as revealed by the Kaplan-Meier survival curve analysis. Among them, surgery combined with chemotherapy resulted in the longest OS, which was significantly superior to that of radiotherapy combined with chemotherapy, surgery combined with radiotherapy, and treatment alone (surgery, chemotherapy, or radiotherapy) (Figure 10B). In recent years, preoperative neoadjuvant therapy has allowed some patients without surgical recommendation to access to surgical treatment through translational therapy, thus prolonging the survival. Therefore, the prognostic difference between patients subjected to preoperative treatments (preoperative chemotherapy or preoperative radiotherapy) and patients who underwent surgery was analyzed, revealing that preoperative treatments did not prolong the OS of patients with PSRCC (Figure 10C,10D), but there was a survival benefit after postoperative radiotherapy (P<0.05) or postoperative chemotherapy (P<0.01) over surgery alone (Figure 10E,10F), as revealed by the Kaplan-Meier survival curve analysis.

Figure 10 OS analysis of different treatment modalities. (A) OS analysis at different time of diagnosis. (B) OS analysis under different treatment modalities. (C) OS analysis after preoperative chemotherapy combined with surgery vs. surgery. (D) OS analysis after preoperative radiotherapy combined with surgery vs. surgery. (E) Surgery combined with postoperative chemotherapy vs. surgery. (F) Surgery combined with postoperative radiotherapy vs. surgery. CI, confidence interval; HR, hazard ratio; OS, overall survival.

Discussion

PC has an insidious onset, atypical symptoms, a high degree of malignancy, poor treatment effect and only a handful of early detections, this representing a huge family burden to the individuals and their families (17). An important factor in the poor prognosis of PC is that it is mostly locally advanced or having distant metastases at the time of diagnosis (18). Having SRCC is a poor prognostic factor for gastric, colorectal, and esophageal cancer (19). The prognosis of PSRCC is significantly worse than that of other pathological types (20). PSRCC had the worst median OS of all gastrointestinal SRCCs, more common in poorly differentiated or undifferentiated pathologic grades and mostly in advanced clinical stages (21). The ErbB2/ErbB3 signaling pathway is involved in the degree of malignancy of SRCC (22). ErbB 2 phosphorylates ErbB 3 to promote the signaling, and Muc4 binds to ErbB2 to help activate ErbB2/ErbB 3 pathways, with subsequent activation of PI3K consequently activating downstream p38MAP. This disrupts the activation of the initiating adhesion junction Rac1, leading to disruption of cell-to-cell adhesin and tight junctions, the loss of cell-cell interactions, infiltration of the surrounding matrix, and metastasis (23). The loss of this critical cell-to-cell interaction in SRCC promotes the formation of a microenvironment favourable to tumor growth, which may explain why most PSRCCs are advanced or metastatic at the time of diagnosis (24). Although PC treatments are evolving, surgical and pharmacologic developments are improving the outcomes. Study showed that age, AJCC stage and treatment modality are independent risk factors for predicting OS and DSS in patients with PSRCC (25). Our study found that age, site of origin, and AJCC stage are independent prognostic factors for CSS and OS in patients with PSRCC, consistent with previous studies (26). Another study showed that early diagnosis, surgery, and chemotherapy are effective in improving the prognosis of PSRCC patients (27). However, another study found that chemotherapy improved short-term survival rates in patients with PSRCC but had limited long-term benefits (28). A study by Nie et al. found that early surgery and chemotherapy can improve survival rates in patients with PSRCC (25). PSRCC is an independent prognostic factor for PC, characterized by poorer histological grading, a higher rate of lymph node involvement, and a higher incidence of distant metastasis, and is associated with a poorer prognosis (29). Case reports revealed that two patients with unresectable PSRCC survived only a few weeks, and four patients with PSRCC survived less than 3 months on average after palliative surgery. Our study showed that the median OS for patients with PSRCC was only 3 months, and the 5-year OS rate was only about 5%, which was consistent with the findings of Wu et al. (10). Moreover, our study showed a median age of 68 years at the onset of PSRCC, with most patients being of high age, poorly tolerating treatment, with metastases and losing the opportunity of being subjected to surgery. Chemotherapeutic drugs commonly used for PC, such as gemcitabine, tegafur, have many side effects and require a certain degree of patient tolerate (30). Just over half of the patients in our study underwent oncology-related treatment and just 19.58% had surgery. Our study demonstrated that surgery and chemotherapy were important factors affecting OS in patients with PSRCC. Despite the rapid development of drugs along with surgical and radiotherapy techniques in the last decade, there is no significant difference in the prognosis of PSRCC patients at four different diagnostic time periods, which might be related to their poor pathological type, late diagnosis, and loss of therapeutic opportunities. Reports by Terada et al. confirmed that patients who underwent radical surgery have a better prognosis than those who underwent palliative surgery or did not undergo surgery (31). Our study showed a better prognosis after surgery than after radiotherapy and chemotherapy. Combination therapy improves OS in patients with PSRCC, with surgery combined with chemotherapy having the best prognosis, whereas patients with surgery alone had a better prognosis than those treated with radiotherapy, thus, our hypothesis was that surgery remains the most effective treatment for patients with PSRCC. Preoperative neoadjuvant radiotherapy for rectal cancer has been effective in recent years, converting locally advanced low rectal cancer from a condition with no recommendation for surgery to one that can be operated on or that allows organ function to be preserved (32). Radojkovic et al. reported a case of PSRCC in the head of the pancreas in which the patient was treated with gemcitabine chemotherapy followed by surgical resection, which improved the patient’s prognosis (7). Another study showed that chemotherapy enhanced CSS in bladder impression cell carcinoma (33). Therefore, preoperative neoadjuvant therapy for PSRCC was analyzed, revealing that it did not improve the OS of patients, probably due to the more advanced staging of patients who took preoperative neoadjuvant therapy compared to those who underwent surgery. It might also be due to the small number of cases involved in preoperative neoadjuvant therapy, thus needing to be further verified in future clinical studies. Some studies showed that adjuvant chemotherapy improves OS in patients with colorectal imprinted cell carcinoma (34). Our study showed that postoperative radiotherapy or postoperative chemotherapy improved the prognosis of patients compared with surgery alone, and patients with PSRCC should be treated with a combination of modalities to improve their survival when their physical condition permits. However, no chemotherapeutic agents have been studied for the specific pathological type of SRCC, and the chemotherapeutic regimen used falls within the recommended chemotherapy regimen for PC, with possible differences in prognosis.

Targeted therapies (epidermal growth factor receptor inhibitors, vascular endothelial growth factor receptor inhibitors) are effective in colorectal SRCC (35). Advances in molecular tumor biology have led to the recommendation of immunotherapy for patients with dMMR and MSI-H PC (36). Targeted therapy and immunotherapy, which are the hotspots of current research, and overall patients have few treatment side effects; their efficacy in PSRCC patients has not yet been clinically demonstrated, and a more rational combination of therapeutic strategies could bring hope to patients with PSRCC. This study demonstrated that age, site of origin, and advanced clinical stage are factors associated with poor prognosis in PSRCC. Compared to the pancreatic body and tail, surgery on the pancreatic head carries higher risks. Furthermore, in advanced clinical stages (such as involvement of the mesenteric and abdominal major vessels), tumors may invade adjacent tissues, leading to surgical difficulties and significantly impacting patient prognosis. This is consistent with observations in clinical practice. Our study incorporated the identified independent prognostic factors for PSRCC patients into the model. The construction of a survival plot, along with the ROC curve, AUC value, and calibration curve, demonstrated that the model has acceptable predictive performance. DCA indicated that this predictive model has good clinical utility.

The SEER database, although it includes a large amount of clinical and treatment information for this rare tumor, still has certain limitations. First, this is a retrospective study, which might lead to potential selection bias. Second, the study involved 20 years, and the staging and treatment guidelines for tumors were adjusted, resulting in a large proportion of patients not having complete clinical staging and treatment-related details, potentially leading to differences in the results of the analyses. Moreover, the specific surgical methods and chemotherapeutic regimens in the SEER database are not detailed, which might also affect the final analysis results. Targeted therapy and immunotherapy are the hotspots of current research, and patients are tolerating the treatments well. This special pathological type of PSRCC is diagnosed in advanced stages and is often combined with distant metastases. Therefore, a large percentage of patients lose the opportunity to receive treatment, and it is hoped that a more reasonable integrated treatment strategy could bring hope to PSRCC patients. Despite some limitations, the results of this study are still offering some treatment selection and prognosis of PSRCC patients, thus helping clinicians in predicting the survival and prognosis of PSRCC patients, providing ideas for the design of future prospective studies.

Conclusions

Age, site of origin, and AJCC stage are independent prognostic factors influencing CSS and OS in patients with PSRCC. We developed a nomogram to predict the prognosis of PSRCC patients; ROC curves and calibration curves indicate that the model has acceptable predictive performance, and DCA demonstrates that the model has good clinical utility. Furthermore, our study demonstrated that combination therapy significantly improved patient survival rates compared to monotherapy, whereas neoadjuvant therapy (preoperative chemotherapy or preoperative radiotherapy) did not prolong the OS. This might be related to the fact that patients receiving neoadjuvant therapy were at a more advanced stage of disease. However, adjuvant therapy (postoperative chemotherapy or postoperative radiotherapy) did prolong OS.

Acknowledgments

The authors acknowledge the efforts of the National Cancer Institute and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER database.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0134/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0134/prf

Funding: This work was supported by the Longyan Clinical Research Center for Hematology and Oncology (No. 2025LYF1003).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0134/coif). P.L. reports receiving funding from the Longyan Clinical Research Center for Hematology and Oncology. The other 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/.

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