Association between time to treatment and bladder cancer survival: a population-based analysis

Introduction

Bladder cancer is a common type of cancer in the urinary system worldwide. According to the World Health Organization (WHO), there were an estimated 573,278 new cases of bladder cancer and 213,518 deaths from the disease worldwide in 2020 (1). Bladder cancer, also known as urothelial carcinoma, is the most common type of bladder cancer.

Cancer treatment delay is a global health system issue. Previous studies have reported that the median delays between transurethral resection of bladder tumors and radical cystectomy ranged from 4.7 to 13.7 weeks (2,3). For example, in the past three years, due to the coronavirus disease 2019 (COVID-19) pandemic, treatment delays have been observed in multiple types of cancer, including bladder cancer (4). The time from diagnosis to treatment could affect the prognosis of bladder cancer. The Institute of Medicine has identified timely delivery of services as one of the major goals for improving the quality of health care in the United States (5). In muscle invasive bladder cancer, delay of greater than 12 weeks from diagnosis to cystectomy was associated with worse overall survival (6). However, data concerning the impact of treatment delays on survival in bladder cancer are sparse, and the findings that have been published are inconsistent. Conversely, Liedberg et al. did not identify any significant impact of treatment delay on disease-specific survival (7). Therefore, more research is required to explore the impact of initial treatment delay on prognosis of patients, and determine risk factors contributing to treatment delay and factors that affect the survival of bladder cancer patients with treatment delay.

The Surveillance, Epidemiology, and End Results (SEER) database is a publicly available cancer database maintained by the National Cancer Institute (NCI) of the United States (8). The database contains information on cancer incidence, mortality, and survival rates from various regions of the United States. The SEER database includes data from over 34% of the United States population and provides information on more than 1.6 million cases of cancer diagnosed between 1975 and the present. The data are collected from 18 cancer registries covering various geographic areas of the United States. The SEER database is a valuable resource for cancer researchers, clinicians, and policymakers who are interested in analyzing trends and patterns in cancer incidence, mortality, and survival rates. The database can be used to identify risk factors for various types of cancer, evaluate the effectiveness of cancer treatments, and monitor changes in cancer trends over time (9).

In our study, we searched for patients with bladder cancer and collected all available information available in SEER database for the period of 2000–2020. We specifically identified the patients who experienced prolonged time from diagnosis to treatment. Subsequently, we investigated the association between survival and increased time from diagnosis to treatment. Additionally, we identified the risk factors related to treatment delay, and examined the factors associated with the prognosis in bladder cancer patients who experienced treatment delay. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-148/rc).

Methods

Data source and study cohort

The data were retrieved from the SEER registries database, which was released in 2023, using the SEER*Stat software (version 8.4.2). The SEER database includes bladder cancers diagnosed between 2000 and 2020 from 17 registries. It provides clinical information, such as time from diagnosis to treatment, surgery, chemo/radiotherapy, tumor stage/grade (Extent of Disease coding system for 2018 and WHO 2004), as well as demographic information including age, marital status, income, race, and sex. Additionally, follow-up information such as survival duration, vital status, and causes of death is also available.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was exempt from local research ethics committee approval considering that SEER data are de-identified and publicly available for research use.

Inclusion/exclusion criteria

This study included adult patients diagnosed between 2000 and 2020. The study endpoint was defined as the earlier of December 31, 2020, or patient death. The primary outcomes of interest were overall survival, defined as death from any cause, and cancer-specific survival, defined as death due to bladder cancer. The CONSORT diagram is seen in Figure 1. The time point of diagnosis is defined as: the date when the patient is officially diagnosed with bladder cancer. It is usually based on the date of pathological confirmation but can also be determined through imaging or other diagnostic methods; while “initial treatment delay” refers to the time interval between the date of cancer diagnosis and the date when the patient first begins treatment. This delay can include the time taken for diagnostic evaluations, consultations, and decision-making processes before starting the initial course of treatment, which may include surgery, radiation therapy, chemotherapy, or other therapeutic interventions.

Figure 1 CONSORT diagram. ICD-O-3, International Classification of Disease for Oncology, Third Edition.

The inclusion criteria were as follows: (I) adult patients (≥18 years old); (II) bladder as their primary location of disease (coded as C670–679); (III) urothelial carcinoma (coded as 8120/3 for transitional cell carcinoma, NOS, 8122/3 for transitional cell papilloma, spindle cell, 8130/3 for papillary transitional cell carcinoma, by International Classification of Disease for Oncology, Third Edition (ICD-O-3) histology codes); (IV) patients who had an incident bladder cancer detected before death between January 1, 2000, and December 31, 2020; (V) time from diagnosis to treatment provided.

The exclusion criteria were as follows: (I) cases identified from autopsy or death certification only; (II) incomplete information, such as age, sex, tumor stage, tumor grade, treatment history, survival and follow up information; (III) age <18 years old.

Variates

The time from diagnosis to treatment was defined as the duration from diagnosis to the initiation of the first cancer-specific treatment. In the SEER bladder cancer database, “cancer-specific treatment” refers to interventions directly targeting bladder cancer, including surgery (such as TURBT or cystectomy), chemotherapy, radiation therapy, immunotherapy [such as Bacillus Calmette-Guerin (BCG) therapy], and targeted therapy. The time from diagnosis to treatment was collected for all cases included in the study. For the purpose of analysis, the subgroups with treatment delay were divided into the following categories: (I) delay within 1 month, (II) delay between 1–2 months, (III) delay between 2–3 months, and (IV) delay of more than 3 months. Cases with a delay of more than 3 months were grouped together due to the small sample size.

The analysis included data on demographics (such as sex, race, socioeconomic status, age at diagnosis, and marriage status), tumor characteristics (including pathology, stages, and histologic grade), treatment modalities (such as surgery, chemotherapy, radiotherapy), and follow-up information (including survival months, vital status, and cause of death). Before conducting the analysis, any subjects with missing data were excluded from the dataset.

Statistical analysis

In the SEER database (between 2000 and 2020 from 17 registries), we identified 331,430 cases of bladder cancers, of which 303,283 cases had the information about time from diagnosis to treatment. Thereafter, we chose three type of bladder cancer (257,763 cases), including 8020/3, 8022/3 and 8130/3 for further analysis, and after excluding unqualified cases, eventually 12,686 cases were involved for further analysis (Figure 1).

To investigate the impact of initial treatment delay on patient survival, we conducted a survival analysis. This included the duration of survival, patients’ vital status at the end of the follow-up period, and the generation of Kaplan-Meier survival curves to examine the effect of treatment delay. Additionally, a log-rank test was employed to compare the survival curves. We examined the relationship between the delay in initial treatment and two outcomes: overall mortality and bladder cancer-specific mortality. Furthermore, we explored potential differences in both overall and bladder cancer-specific mortality among patients categorized by varying lengths of treatment delay (delays of 1, 2, 3, and >3 months).

Logistic regression (binary) can be used to analyze the probability of survival or occurrence of an event at a fixed time point. The model estimates the odds ratios for the predictors. An odds ratio greater than 1 indicates a higher likelihood of the event occurring with the predictor, while an odds ratio less than 1 indicates a lower likelihood. The logistic regression model can provide direct estimates of probabilities, which can be straightforward for clinical decision-making. In present study, binary logistic regression analyses were conducted to investigate the potential factors related to treatment delay. Time from diagnosis to initial treatment was evaluated as binary covariates, categorized into the initial treatment delay group and the no initial treatment delay group. Then we investigated whether demographic data and clinical variables could affect the initial treatment delay.

Multivariate Cox proportional hazards model, is a statistical technique frequently used in the survival analysis of the relationship between the survival time of patients and one or more predictor variables. The multivariate Cox model is used to model the hazard ratio (HR), which is the probability of the event occurring at certain time. In the present study, we explored whether demographic data, clinical variables, and treatments (including surgery, chemotherapy, and radiation therapy) impacted the survival of patients with initial treatment delay. Multivariate Cox proportional hazard analyses were employed to investigate the association of these covariates with overall and cancer-specific survival respectively in bladder cancer patients. HRs and 95% confidence intervals (CIs) were calculated. A positive HR indicates an increased risk of the event, while a negative HR indicates a decreased risk.

The data of clinical variables and demographic characteristics are presented as counts (percentages) for discrete variables. Chi-square test tests were used to explore potential differences among groups.

The statistical analyses were performed using SPSS (Version 26, IBM Corporation, Armonk, NY, USA). A significance level of 0.05 was set for statistical significance, and all tests were two-tailed.

Results

Patient characteristics

Demographic data and clinical characteristics

The 331,430 referenced cases were bladder cancer cases, which included 10 subtypes of bladder cancer such as transitional cell carcinoma, clear cell adenocarcinoma, plasma cell myeloma, small cell carcinoma, and squamous cell carcinoma. We then selected three specific types of bladder cancer (transitional cell carcinoma, transitional cell papilloma/spindle cell, and papillary transitional cell carcinoma), totaling 257,763 cases. After excluding unqualified cases (as shown in Figure 1), a total of 12,686 eligible patients from the US SEER database were included in this study. The baseline clinical variables and demographic characteristics of the patients are presented in Table 1 and Table S1. The cohort consisted of 6,275 patients diagnosed with stage Tis–Ta, 3,311 patients diagnosed with stage T1, 1,797 patients diagnosed with stage T2, and 1,163 patients diagnosed with stage T3–T4. Additionally, the cohort included 5,965 patients diagnosed with low-grade tumors and 6,721 patients diagnosed with high-grade tumors. Among the cohort, 73.3% of the patients were male, and 83.2% were white. The median follow-up time was 60 months, with a total of 6,790 deaths (53.5%).

Among the patients included in the study, 18.8% (n=2,379) experienced a delay from diagnosis to treatment. In the patients with treatment delay, the median time from diagnosis to treatment was 1 month.

The survival analysis by Kaplan-Meier curve revealed that patients with treatment delay had significantly worse overall survival and cancer-specific survival compared to patients without treatment delay. Furthermore, there was a significant difference in both overall cumulative survival and cancer-specific cumulative survival among patients with varying durations of initial treatment delay (as depicted in the Kaplan-Meier survival curve shown in Figure 2).

Figure 2 Kaplan-Meier survival plot based on subgroup of treatment delay. (A) Comparisons were made of the overall cumulative survival between bladder cancer patients who experienced an initial treatment delay and those who did not. (B) Comparisons were made of the cancer-specific cumulative survival between bladder cancer patients who experienced an initial treatment delay and those who did not. (A,B) The data have shown that an initial treatment delay can decrease both overall and cancer-specific survival in bladder cancer patients. (C) Comparisons were made to assess the differences in overall cumulative survival among bladder cancer patients who experienced varying durations of initial treatment delay. These durations included delays of 0–1 month, 1–2 months, 2–3 months, and over 3 months. (D) Comparisons were conducted to evaluate the differences in cancer-specific cumulative survival among bladder cancer patients with varying durations of initial treatment delay. These durations included delays of 0–1 month, 1–2 months, 2–3 months, and over 3 months. There was a significant difference in both overall cumulative survival and cancer-specific cumulative survival among patients with varying durations of initial treatment delay. P-values were calculated using the log-rank test.

Logistical regression analysis for risk factors related to treatment delay

In the entire cohort, after adjusting for covariates, logistical regression analysis was used to identify the risk factors related to delay from diagnosis to initial treatment included age at diagnosis, sex, surgery, clinical stage, and pathological grade, as shown in Table 2. However, race, histology, home income, radiotherapy, and urban-rural continuum did not influence the occurrence of treatment delay. Additionally, in patients with a stage above T2, surgery was identified as the sole risk factor associated with initial treatment delay, as determined by logistic regression analysis [odds ratio (OR) =1.700, 95% CI: 1.539–1.878, P<0.001].

Multivariate analysis for survival in bladder cancer patients with treatment delay

In Cox regression analysis (Tables 3,4), the results of survival outcomes were stratified by demographic and clinical variables in the patients with treatment delay. In the entire cohort (Table 3), the demographic predictor of worse survival was age at diagnosis, while the marital status at diagnosis was related to better survival outcome. Regarding disease-specific features, factors include advanced stage, lymph node involvement, metastasis, and high pathological grades. Compared to transitional cell carcinoma, the histology types of transitional cell papilloma/spindle cell, and papillary transitional cell carcinoma were associated with poorer outcomes in both overall and cancer-specific survival.

Furthermore, using the Cox regression model, we found that surgery, chemotherapy, and radiotherapy were found to improve both overall survival and cancer-specific survival. Overall death risk and cancer-specific death risk in patients receiving surgery were reduced to 83.9% and 70.8%, respectively, compared to patients who did not undergo surgery. Similarly, patients who received radiotherapy had a significant survival advantage with a 31.9% lower risk of cancer-specific death and 46.4% lower risk of overall death risk compared to those who did not receive radiotherapy. Patients who received chemotherapy had a significant survival advantage with a 47.01% lower risk of cancer-specific death and 37.66 % lower risk of overall death risk compared to those who did not receive chemotherapy.

Additionally, in patients with a stage above T2 (Table 4), the demographic predictor of worse survival was age at diagnosis. Compared to transitional cell carcinoma, the histology types of transitional cell papilloma/spindle cell, and papillary transitional cell carcinoma were associated with poorer outcomes in both overall and cancer-specific survival. Besides, we found that lymph node involvement could lead to worse cancer-specific survival, and metastasis could affect both overall and cancer-specific survival. Furthermore, using the Cox regression model, we found that surgery and chemotherapy were associated with improved outcomes in both overall and cancer-specific survival. Specifically, the risk of overall death and cancer-specific death in patients undergoing surgery was reduced to 72.7% and 60.9%, respectively, compared to those who did not undergo surgery, while the risk of overall death and cancer-specific death in patients undergoing chemotherapy was reduced to 72.6% and 71.9%, respectively, compared to those who did not undergo chemotherapy. In patients with a stage Ta combined with grade G3, Tis or T1 (Table 4), the demographic predictor of worse survival was age at diagnosis. Besides, we found that metastasis could lead to worse cancer-specific survival, whereas chemotherapy were associated with improved outcomes in both overall and cancer-specific survival.

Discussion

In the present study, we utilized SEER-Medicare data to investigate the prognosis of bladder cancer patients who experienced initial treatment delays. We also analyzed risk factors for initial treatment delay and clinical variables linked to patient survival. Our data showed that age at diagnosis, sex, surgery, clinical stage (T), and pathological grade, were all the risk factors related to longer intervals from diagnosis to initiation of any or definitive treatment. Cox regression analysis showed that age at diagnosis, marital status at diagnosis, advanced stage, lymph node involvement, high pathological grades, surgery, chemotherapy, and radiotherapy were all associated with survival of bladder cancer patients with initial treatment delays.

Numerous studies indicate that postponing treatment can worsen bladder cancer outcomes. In a systematic review, Fahmy et al. posited that bladder cancer patients who experienced a delay of more than 12 weeks following transurethral resection of bladder tumors faced a significantly heightened risk of needing salvage radical cystectomy (10). Meanwhile, Lee et al. indicated that in cases of muscle-invasive bladder cancer, a delay exceeding 12 weeks from diagnosis to cystectomy correlated with poorer overall survival (Cox regression analysis, with an HR for death of 1.96) (6). And Li et al. suggested that patients with T2–T3 muscle-invasive bladder cancer, the time of surgical delay >90 days can have a negative impact on survival (Kaplan-Meier survival curve analysis) (11). And a delay from diagnosis of bladder cancer to cystectomy was associated with worse survival results (12). Boeri et al. suggested that postponing radical cystectomy for muscle-invasive bladder cancer leads to unfavorable survival outcomes (13). In the present study, using the SEER database, we examined a large cohort to investigate the relationship between initial treatment delay and survival outcomes. Our findings clearly indicate that delays in initial treatment negatively impact both overall survival and cancer-specific survival. As the time from diagnosis to treatment initiation increases, both overall and cancer-specific survival rates decline significantly. It is logical to infer that prolonged waiting times for treatment can provide more opportunities for the cancer to invade, grow, and potentially metastasize, all of which can jeopardize patient survival. Thus, treatment delays in bladder cancer patients should be a matter of concern for both healthcare professionals and patients. Our findings underscore the importance of timely treatment for patients diagnosed with bladder cancer. Thus, shortening the time from diagnosis to initial therapy is crucial for improving treatment outcomes and increasing patient survival. Some clinics and researchers have been exploring methods to decrease the treatment window for bladder cancer patients. In the UK, Blick et al. introduced the 2-week wait rule to reduce the time from diagnosis to treatment for bladder cancer patients. They found that the introduction of this rule significantly reduced the time from referral to the first specialist consultation by 47.6% (14). In Sweden, a standardized care pathway for patients with suspected bladder cancer, primarily due to macroscopic hematuria, was implemented in 2016 (15). This pathway includes immediate referral (within 24 hours) from any care unit to cystoscopy and computed tomography (CT) scan for all eligible patients, TURBT for all patients with suspected bladder cancer, and multidisciplinary team conferences for all patients with ≥cT1 tumors. This standardized approach has significantly decreased the median time to TURBT and increased the proportion of patients discussed at multidisciplinary team conferences, thereby reducing the time from the first symptom to definitive treatment and ensuring uniform management according to international guidelines.

Identifying the risk factors associated with treatment delay in bladder cancer is crucial, as it enables clinicians and patients to act promptly upon diagnosis, potentially enhancing survival rates and improving the quality of life for patients. Using logistic regression, further analysis from our study revealed several determinants linked to treatment delay. These include age at diagnosis, sex, surgery, clinical stage (T), and pathological grade. Inconsistent with our data, Buac et al. used counterfactual framework to perform causal inference mediation analysis, and found that racial disparities play a role in these delays (16). Their research found that black and Hispanic patients diagnosed with muscle-invasive bladder cancer faced more extended initial treatment delays when compared to their white counterparts. Additionally, the referral process itself can introduce delays. McCombie et al., in their study on treatment delays of bladder cancer in western Australia, posited that the referral system might be one of the primary contributors to these delays through epidemiological analysis (17). Another factor to consider is financial distress, often termed “financial toxicity” in the context of medical treatments. This refers to the financial strain and hardship patients experience due to the high costs associated with medical care, particularly in oncology where treatments can be expensive and necessitate long-term monitoring and follow-up. This financial burden can further exacerbate treatment delays, especially for those without adequate insurance or financial resources. Socioeconomic status has been highlighted as a significant determinant in treatment delays across various studies. Casilla-Lennon et al. specifically pointed out that financial distress stands out as a primary factor causing treatment delays in bladder cancer. This is particularly pronounced among younger patients, who might not have the financial stability or adequate insurance coverage to afford timely medical interventions. Such financial hardships can lead to postponement of treatments, potentially compromising the efficacy of the treatment when it is eventually administered (18). Gary et al. used multivariable, generalized estimating equation models, and found that individuals with a lower socioeconomic status often receive less aggressive treatments when diagnosed with muscle-invasive bladder cancer (19). However, our analysis did not identify socioeconomic status as a significant factor influencing treatment delay. We postulate that this discrepancy might arise from the specific dataset used for our study. As healthcare systems evolve and insurance coverage becomes more widespread, the influence of socioeconomic status and race on treatment decisions might diminish. This is a positive trend, suggesting that access to care is becoming more equitable. Through analysis of bladder cancer database using Cox multivariable proportional hazard models and multivariable logistic regression models, Chu et al. identified male gender, socioeconomic status, and residence in rural areas as additional risk factors for treatment delay. The confluence of these factors underscores the multifaceted challenges faced by patients in accessing timely care (20). Our findings consistently showed that treatment delays were more prevalent among male patients compared to their female counterparts. However, it is important to consider the inherent imbalance in the distribution of bladder cancer between the sexes when interpreting these results. Bladder cancer is more frequently diagnosed in males, which could contribute to the observed disparity in treatment delays between the two genders.

Our analysis revealed that pathological features, specifically tumor stage and grade, are significant predictors of treatment delay. Patients with advanced-stage and high-grade tumors were more likely to experience delays in treatment initiation. High-stage and high-grade bladder cancers are often more complex and may require a multidisciplinary approach for treatment planning. This complexity can lead to delays as various specialists, such as urologists, oncologists, and radiologists, need to collaborate to develop an optimal treatment plan. These patients often require extensive pre-treatment evaluation, including advanced imaging and sometimes additional biopsies or surgeries to accurately stage the disease. This thorough evaluation is crucial for appropriate treatment planning but can contribute to delays. High-stage and high-grade cancers may present several treatment options, including surgery, chemotherapy, radiation, or a combination of these. The decision-making process can be time-consuming, as it involves detailed discussions with the patient about the risks, benefits, and potential outcomes of each treatment option. Patients with more advanced disease may have significant comorbidities or poor performance status, which can necessitate further medical optimization before initiating aggressive treatments like chemotherapy or major surgery. This optimization process can delay the start of cancer-specific treatment. There can be logistical delays related to scheduling surgeries, treatment sessions, or coordinating care among different departments. Abuhasanein et al. introduced a standardized care pathway for patients with suspected bladder cancer, which included multidisciplinary team conferences for all patients with ≥cT1 tumors. This procedure clearly increased the proportion of patients discussed at multidisciplinary team conferences, thereby reducing the time from the first symptom to definitive (15). Additionally, systemic issues such as healthcare access, insurance approvals, and availability of specialized care can also contribute to treatment delays. The diagnosis of a high-stage or high-grade cancer can be overwhelming for patients, leading to a period of psychological adjustment before they are ready to commence treatment. This period can contribute to the overall delay in treatment initiation (21,22). In addition, Chu et al. highlighted that the apprehension towards undergoing radical cystectomy, a fear of surgery, was a common reason for treatment postponement (20). This sentiment was also echoed in our analysis, emphasizing the psychological factors that can influence treatment decisions and timelines.

Cox regression, a vital tool in cancer research, is specifically designed for time-to-event data and plays a crucial role in enhancing our understanding of how various factors influence cancer survival. In our research, using Cox regression model, we identified an early stage or having a low grade positively impacts survival. Consistent with our expectations, we discovered that treatments, encompassing surgery, chemotherapy and radiotherapy both conferred a significant survival benefit. This aligns with prior research indicating that tumor removal and radiotherapy are independently linked to enhanced survival outcomes in bladder cancer patients. A study on bladder cancer showed that elderly patients (a median age of 85 years) can tolerate well with external beam radiotherapy, without significant toxicity observed (23). Van Poppel et al. retrospectively compared to patients with stage T1 and T2 receiving radical radiotherapy or preoperative radiotherapy and cystectomy, and they found that bladder cancer-specific survival was improved to 76% in the brachytherapy group compared to 50% and 49% in the two other groups (24). Moreover, radical cystectomy is associated with improved survival rates in muscle-invasive bladder cancer patients, especially when performed before the cancer has spread to distant organs (25).

There are some limitations in this study. Firstly, being a retrospective observational study, it inherently carries the challenges associated with such designs, including potential biases that can affect the cause-and-effect relationship between treatment delay and survival. The minor absolute differences observed might not have clinical significance. Secondly, the SEER database lacks comprehensive details on cancer biology, which could undermine the reliability of our findings. For instance, there could be histological variants that interfere with urothelial carcinoma. Based on previous studies, mixed histology can occur in bladder cancer, such as urothelial carcinoma mixed with adenocarcinoma or squamous cell carcinoma. These variants are crucial for patient management (26,27). Other variants, such as squamous cell carcinoma, adenocarcinoma, and small cell carcinoma, exhibit different biological behaviors and responses to treatment compared to urothelial carcinoma. These differences can influence patient outcomes, including survival rates and recurrence patterns. Ignoring these differences can lead to misleading conclusions and suboptimal treatment strategies. Unfortunately, our SEER database data do not include information on histological variants. To overcome this limitation, in future studies, we plan to supplement SEER data with information from our institutional databases that include histological details. Stratifying data by histological subtype will allow for more accurate comparisons and a better understanding of each variant’s unique characteristics. In addition, we analyzed the initial delay in treatment using data from a database that provides the delay in weekly increments. While this method offers a broad overview, it introduces limitations in precision. For instance, measuring treatment delay in days would allow for a more granular and accurate analysis. This finer resolution could better capture the nuances of treatment initiation timing and its potential impact on patient outcomes. Future research could improve upon our methodology by utilizing databases that record treatment delays in daily increments, thereby enhancing the precision of the analysis. These methodological improvements could significantly contribute to the field by offering more actionable insights into the optimization of treatment schedules for bladder cancer patients. Consequently, the survival benefits reported in this study might not be entirely accurate. Further research with more precise data is essential to validate these findings and delve deeper into the impact of treatment on survival.

Conclusions

This study demonstrated that a delay in bladder cancer treatment was negatively correlated with survival outcomes in this US SEER Medicare database population. Risk factors related to delay from diagnosis to treatment included age at diagnosis, sex, surgery, clinical stage (T), and pathological grade. Notably, surgical resection, chemotherapy and radiotherapy substantially improved survival for patients who experienced treatment delays.

Acknowledgments

Funding: This study was supported by Hebei Province Medical Science Research Project Plan, China (No. 20210339), Hebei Province Higher Education Science and Technology Research Project - Youth Fund (No. QN2019189), and Open Research Topic of Hebei Key Laboratory of Integrative Medicine on Liver-Kidney Patterns (No. B201904).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-24-148/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-148/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-148/coif). All authors report the funding from the Hebei Province Medical Science Research Project Plan, China (No. 20210339), Hebei Province Higher Education Science and Technology Research Project - Youth Fund (No. QN2019189), and Open Research Topic of Hebei Key Laboratory of Integrative Medicine on Liver-Kidney Patterns(No. B201904). The authors have no other 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 (as revised in 2013).

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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