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29 January 2025: Database Analysis  

Development and Validation of a Competitive Risk Model in Elderly Patients with Transitional Cell Bladder Carcinoma

Libin Yang ORCID logo1ABCDE, Chao Chen ORCID logo1AC, Qianghui Wang ORCID logo1AB, Zhiliang Zhuang1EF, Tao Sun ORCID logo1EF*

DOI: 10.12659/MSM.946332

Med Sci Monit 2025; 31:e946332

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Abstract

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BACKGROUND: Transitional cell bladder carcinoma (tcBC) is the predominant form of bladder cancer, making up around 95% of reported cases. Prognostic factors for older individuals with tcBC differ from those affecting younger patients. The main purpose of this study was to establish a prognostic competing risk model for elderly patients with tcBC.

MATERIAL AND METHODS: We conducted a retrospective analysis using data from the SEER database, randomly assigning patients to training and validation groups. We applied proportional subdistribution hazard (SH) to assess risk factors for cancer-related mortality (CSM). A competitive risk model was created to predict cancer-specific survival in elderly patients with tcBC. Model validation involved evaluating the area under the receiver operating curve, the consistency index, and a calibration curve. The Kaplan-Meier (K-M) curve was then used to compare mortality risk between high-risk and low-risk groups identified by the model.

RESULTS: This study randomly assigned 61 293 patients from the SEER database into training (42 905 patients) and validation (18 388 patients) groups in a 7: 3 ratio. Using a proportional subdistribution hazards model, we identified prognostic risk factors such as age, race, sex, marital status, TNM staging, grade, and metastatic status in brain, bone, liver, and lung. We developed a competitive risk model to predict 5-year cancer-specific survival (CSS) in elderly tcBC patients, achieving consistency index (C-index) values of 0.814 and 0.815 for the training and validation groups, respectively. Kaplan-Meier (K-M) analysis revealed 5-year survival probabilities of 35.1% (high-risk) and 42.2% (low-risk) in the training group, with similar rates of 35.7% and 42.0% in the validation group, both showing statistically significant differences (log-rank P<0.01).

CONCLUSIONS: We successfully established a competitive risk model for forecasting cancer-specific survival in elderly tcBC patients, primarily relying on these identified risk factors. The validation outcomes indicate the model’s accuracy and dependability, making it a highly efficient predictive instrument. This tool enables making personalized clinical decisions for both medical professionals and patients.

Keywords: Prognosis, Survival Analysis, Urinary Bladder Neoplasms

Introduction

In 2020, global data revealed that 573 278 individuals were newly diagnosed with bladder cancer (BC), predominantly affecting men and those over 55 years of age [1,2]. According to predictions from the World Health Organization, this number is expected to double by 2040. BC ranks as the tenth most prevalent form of cancer worldwide and ranks as the thirteenth leading cause of cancer-related death [3]. In 2020, expenditures on BC treatment reached US$4.65 billion, placing a significant strain on society and the medical sector [4]. Around one-quarter of patients diagnosed with BC have either muscle-invasive BC (MIBC) or metastatic forms of the disease, with the remaining 75% having non-muscle-invasive bladder cancer (NMIBC) [5]. There are various types of BC, including transitional cell carcinoma, squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and sarcoma [6]. TcBC, also known as urothelial carcinoma, is the most prevalent type of BC, comprising nearly 95% of all cases [7,8]. At present, cisplatin-based chemotherapy is still a first-line treatment for metastatic tcBC patients. As immune checkpoint inhibitors (ICIs) were approved by the Food and Drug Administration (FDA) for clinical use, there are more choices for patients who are not suitable for cisplatin-based chemotherapy. The ICIs currently used for tcBC mainly include atezolizumab, nivolumab, avelumab, and pembrolizumab. PD-L1 expression in tumor cells is upregulated through interactions with lymphocytes in response to inflammatory signals, as well as proliferative oncogenic signals such as epidermal growth factor receptor (EGFR) and signal transducer and activator of transcription 3 (STAT3). This process sustains both innate and adaptive immune resistance in cancer. Targeting the PD-1/PD-L1 pathway can restore anti-tumor immune responses, primarily mediated by CD8+ lymphocytes [9].

Prior research has indicated that BC risk can be influenced by factors such as sex, age, tobacco use, presence of arsenic in water sources, exposure to aromatic amines in the workplace, and ethnic background [10–13]. The incidence of bladder cancer (BC) varies significantly between males and females; it ranks as the 17th most common cancer among women but rises to the sixth among men [14]. Although women have a lower incidence, they exhibit a higher proportion of progressive BC, worse prognoses, and poorer survival rates compared to men. These findings are in close agreement with the 2022 study by Theodorescu et al [15], which highlighted substantial differences in disease progression and risk between males and females with BC. Zhang et al [16] found that most cases of BC occur in individuals over the age of 60 years, with older age being the primary risk factor. The TNM classification system, outlined in the eighth edition of the US Joint Commission on Cancer (AJCC) staging manual, signifies a growing understanding of the pathophysiology of BC and potential treatment options [17]. This classification system is extensively utilized for making prognostic assessments and treatment choices in clinical settings. Nevertheless, research indicates that patients with identical pathological grading or clinical staging of BC may exhibit varying prognoses and overall survival rates, especially for tcBC patients, who account for the vast majority of BC patients. Accurately predicting the prognosis of patients with transitional epithelial cancer is of great benefit to BC patients, especially those over 60 years old. It can provide a basis for formulating personalized precision medicine and follow-up plans, thereby improving patients’ quality of life. To accurately and reliably predict survival, the use of nomograms has been suggested and applied as a multi-layered tool in clinical studies of prostate, breast, gastric, and colorectal cancer [18–21].

The purpose of this research was to establish a predictive model for risk assessment and validate its precision in assessing the CSS rate of elderly patients with tcBC, offering guidance for clinical intervention and post-treatment monitoring. Data for this investigation were acquired from the SEER database of the National Cancer Institute. Survival comparison of elderly tcBC patients in various risk categories, defined by model scores, was conducted using Kaplan-Meier analysis.

Material and Methods

DATA COLLECTION AND PATIENT SELECTION PROCEDURES:

All patient demographics and tumor characteristics in this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) database using SEER*Stat version 8.4.3. Cases with tcBC diagnosed from 2010 to 2015 were included, while patient records with incomplete information on pre-specified variables were excluded. Established in 1973, SEER covers 34.6% of the U.S. population. It compiles comprehensive data on tumor demographics, site, morphology, staging at diagnosis, treatment modalities, and follow-up outcomes, serving as an invaluable resource for oncology researchers globally. In all the exported patient data in this study, the only information used to identify the patient is the patient’s ID number. No other personal privacy information of the patient is collected. Given the public nature of the SEER database, patient confidentiality is preserved, obviating the need for ethics clearance and informed consent for our study. Our research protocol adhered strictly to the guidelines outlined by the SEER database. We collected relevant clinicopathological data from all individuals with tcBC: patient ID, age, sex, ethnicity, marital status, histological tumor grade, TNM staging, type of surgical intervention, radiotherapy, chemotherapy, presence of metastases in the brain, bone, liver, and lung, survival status, duration of survival in months, and cause of death. Criteria for inclusion: (1) Patients must have received a diagnosis between the years 2010 and 2015; (2) The pathological diagnosis must indicate transitional cell bladder carcinoma (ICD.O.3.Hist: 8120/3, 8122/3, 8130/3, 8131/3); (3) Patients must be aged 60 or older. Criteria for exclusion: (1) Patients with unknown TNM staging; (2) Patients diagnosed solely through autopsy or death certificate; (3) Patients with an unknown surgical method; (4) Patients with a survival time of less than 1 month; (5) Patients with unknown status of tumor metastasis to the brain, bones, liver, and lungs. The process of patient screening is outlined in Figure 1. The study categorized participants based on sex (female, male), race (white, black, others), marital status (married, unmarried, unknown), histological grades (I–II, III–IV), surgical procedures (TURBT, P/R, non-surgery), radiotherapy and chemotherapy (Yes, No/unknown), tumor metastasis to brain, bones, liver, and lung (Yes, No), and survival status.

STATISTICAL ANALYSIS:

This study uniquely categorized patient outcomes into 3 types: survival, death due to tcBC, and death due to other causes. This approach enabled the identification of both cancer-specific (CSM) and other causes of mortality (OCM) risk factors in elderly tcBC patients. This model accounts for competing endpoints, making it more reflective of real-life scenarios [22]. The training cohort’s mortality risk was assessed using the classic Fine and Gray’s proportional subdistribution hazards model [23]. Based on these identified cancer-specific risk factors, a competitive risk model was developed to predict the 1-, 3-, and 5-year CSS for this group, thereby enabling accurate CSS predictions for tcBC patients. The model’s performance was thoroughly validated using the C-index, AUC, and calibration curve, setting the stage for further evaluation of its clinical utility via the decision curve analysis (DCA) curve.

We used R software version 4.3.2 and SPSS version 26.0 (SPSS, Inc., Chicago, IL, USA) for all statistical analyses. The data analysis involved several steps: First, all patients meeting the inclusion criteria were randomly divided into training and validation groups in a 7: 3 ratio using R software, with a random seed set to 1 to ensure the reproducibility of the results. SPSS was then used to assess statistical differences in baseline data, describing continuous variables with mean and standard deviation and comparing group differences using a nonparametric U test. Categorical variables are presented as frequencies, with chi-square tests for group differences. The proportional subdistribution hazard model was employed to examine risk factors linked to CSM and OCM. A weighted dataset for competing risks analysis was created using the ‘crprep’ function from the ‘mstate’ package, followed by univariate tests with the ‘cuminc()’ function and multivariate tests using the ‘crr()’ function from the ‘cmprsk’ package. Validation was carried out using R packages, including ‘pec’, ‘survcomp’, ‘survivalROC’, ‘riskRegression’, ‘rms’, and ‘ggDCA’. Statistical significance was set at a P value below 0.05.

CLINICAL UTILITY:

Decision curve analysis is a cutting-edge method for evaluating overall benefit at different levels. DCA was used to assess the possible practicality of the latest model and contrast it with the conventional TNM staging system. First, use the ‘nomogramFormula’ package to calculate the predicted model scores for each patient based on the established model. Then, utilize the ‘survminer’ package to determine the optimal cutoff values for both the training and validation groups, allowing for the classification of patients into high-risk and low-risk groups within each cohort. Finally, based on these classifications, use the ‘ggsurvplot’ package to create Kaplan-Meier curves and perform the log-rank test.

Results

PATIENT CLINICAL BASELINE DATA:

The research cohort was 61 293 elderly individuals diagnosed with transitional cell bladder carcinoma (tcBC). Table 1 provides detailed demographic and clinicopathological information for the 2 patient cohorts. These participants were randomly split into a training group (comprising 42 905 individuals) and a validation group (comprising 18 388 individuals), with the proportions of each category maintained consistently between the 2 groups. The mean age of patients was 74.93±8.52 years, with 54 770 (89.4%) being white, 47 306 (77.2%) male, and 35 647 (58.2%) married. Among them, 1319 (2.2%) had metaplastic carcinoma. Histological grades comprised 18 852 (30.8%) grade I–II and 28 646 (46.7%) grade III–IV. Additionally, 50 161 (81.8%) patients were in Ta/Tis/T1, 59 666 (97.3%) in N0, and 59 974 (97.8%) in M0. Transurethral resection of bladder tumor (TURBT) was conducted on 54 083 (88.2%) patients, while partial or radical cystectomy (P/R) was performed on 3520 (5.7%) patients. Chemotherapy was administered to 14 669 (23.9%) patients, and 2930 (4.8%) received radiotherapy. The average survival time for all patients was 62.50±37.25 months. Based on the analysis findings, there were no substantial differences detected in the initial clinical characteristics among patients in the training and validation groups (P>0.05). The detailed statistical results are shown in Table 1.

DEVELOPMENT OF THE COMPETING RISK MODEL:

An analysis was conducted on the training dataset to identify factors influencing cancer-related mortality and other causes of mortality using proportional subdistribution hazard methodology and cumulative survival rates. The study identified various risk factors for CSM among patients, including demographic variables such as age, sex, race, and marital status, as well as clinical factors like TNM stage, grade, type of surgery, receipt of chemotherapy, and presence of metastases in the brain, bone, liver, and lung. Risk factors for OCM were also assessed and found to include similar variables, except for radiation, which was not associated with either CSM or OCM (Table 2). Utilizing patient-specific risk factors related to cancer outcomes, a competitive risk model was developed to predict patient CSS (Figure 2). To facilitate use by clinicians, we have transformed the predictive model developed in this study into a web tool accessible via a direct link. This enhances the convenience of clinical application and supports broader dissemination. The specific access link is as follows: https://libinyang2024tcbc.shinyapps.io/tcBCDynNomapp/.

MODEL VALIDATION:

To evaluate the performance of the constructed nomogram model, we assessed its discrimination and calibration using training and validation datasets. Discrimination refers to the model’s ability to correctly distinguish between individuals with high and low future risk of the outcome event by setting an appropriate risk threshold. Calibration reflects the model’s ability to accurately predict the probability of an individual experiencing the outcome event in the future. We evaluated discrimination by plotting ROC curves and calculating AUC values. An AUC closer to 1 indicates better discrimination, and an AUC >0.7 is generally considered indicative of good discrimination. As shown in Figure 3A and 3B, the AUC values for predicting the 1-, 3-, and 5-year CSS rates for elderly tcBC patients were 0.901, 0.860, and 0.826 in the training set, and 0.898, 0.861, and 0.828 in the validation set, respectively, indicating that the nomogram model demonstrates good discrimination. Calibration was assessed using calibration plots, where the y-axis represents the observed probability and the x-axis represents the predicted probability. The diagonal line serves as the reference line, while the curve shows the fitted relationship between predicted and observed probabilities. When predicted probabilities closely match observed probabilities, the curve will approach or overlap the reference line. As shown in Figure 4A and 4B, the predicted 1-, 3-, and 5-year CSS rates closely match the observed rates, indicating that the nomogram model has good calibration.

THE COMPETITIVE RISK MODEL IN CLINICAL PRACTICE:

If the DCA curve is above the baseline benefit line, it indicates that the model provides additional value. In other words, using the model results in a higher net benefit. The DCA curve can also be used to compare the performance of multiple models. From the DCA curves in this study (Figure 5A, 5B), it is evident that, in both the training and validation groups, the model’s DCA curve is consistently above that of the TNM staging system. This suggests that the model developed in this study offers better clinical decision support across the entire risk spectrum. Subsequently, the developed prediction model was used to calculate the outcome risk score for each patient, and the patients were divided into high-risk and low-risk groups using the optimal cutoff value in the ROC curve. The optimal cutoff scores for the training group and validation group were 116.36 and 96.09, respectively. As shown in Figure 6, Kaplan-Meier curve analysis revealed significantly higher survival probabilities among patients in the low-risk group compared to their high-risk counterparts. In the training group, the high-risk group recorded survival rates of 84.2%, 58.8%, and 35.1% at 1, 3, and 5 years, respectively, while the low-risk group had rates of 89.7%, 66.8%, and 42.2%, respectively.

Discussion

Intrinsic urothelial carcinoma subtypes were initially identified through transcriptomic analyses using clustering analysis, which yielded luminal papillary, luminal nonspecified, luminal unstable, stroma-rich, basal/squamous, and neuroendocrine subtypes. The clusters displayed distinct oncogenic mechanisms, immune stromal phenotypes, and prognoses[24]. Despite displaying distinct clinical outcomes, both NMIBC and MIBC shared similar genomic features related to tcBC. These included DNA damage repair (DDR), the MAPK/ERK signaling pathway, and ERBB family genes [25]. At the chromosomal level in tcBC, the deletion of chromosome 9q appears to occur early in the development of tumors, whereas the loss of chromosomes 3p, 10q, 13q, 17p, and 18q is more prevalent in high-grade tumors. Amplifications and increases are frequently observed in patients with advanced tumors. Common mutations in NMIBCs affect genes STAG2, PIK3CA, FGFR3, and those in the RAF/RAS/RTK pathway, while MIBCs are often characterized by mutations in MDM2, ERBB2, ARID1A, CDKN2A, P53, and RB1[26].

This study focused on the prognostic outcomes of the elderly tcBC patient population, taking cancer-specific survival as the research prediction target, and successfully established a prognostic evaluation model using the K-M analysis method and AUC curve, which was verified with the help of the DCA curve, proving that the model has good clinical application significance.

Nomograms serve as a prognostic assessment tool that integrates tumor-related factors for more accurate evaluations of patient outcomes, aiding clinicians in creating personalized treatment plans. Recently, their role in predicting the prognosis of bladder cancer (BC) patients has gained attention, with studies applying them to assess overall survival (OS) and disease-free survival (DFS). Zhan et al [27] developed a nomogram for muscle-invasive bladder cancer (MIBC) patients that demonstrated high accuracy. The nomogram developed by Yang et al [28] focuses solely on cancer-specific survival (CSS) for patients following radical cystectomy and does not stratify by age, making it insufficient for accurately predicting prognostic outcomes for the target age group. Although Guo et al [29] limited their study population to those under 40 years old, the incidence of bladder cancer in this group is relatively low, and their prognosis is generally favorable, which limits its clinical relevance. Meanwhile, the predictive model established by Morgan et al [30] targets the elderly population but primarily predicts mortality within 90 days after radical cystectomy. While the results indicate that older age is an independent risk factor for death within this 90-day period (HR 2.30, 95% CI 1.22–4.32), the short prediction window limits its accuracy for long-term assessments, thus failing to provide constructive insights for long-term follow-up strategies and clinical management. Although these established nomogram models can be used for prognostic assessment of BC patients [27–31], they are not accurate enough and no one has noticed the application value of nomogram in elderly tcBC patients. Traditional survival analysis methods often overlook the impact of competing risks, particularly in older cancer patients, who account for nearly 70% of cancer deaths [32]. Unlike their younger counterparts, elderly cancer patients have added risk elements, including diminished organ functionality due to aging, concealed disease onset, and multiple coexisting illnesses. Consequently, there is a significant gap in research addressing the long-term prognosis for elderly tcBC patients.

Age is a crucial factor in the occurrence of cancer, as the chances of cancer-related genetic changes increase as individuals get older. Cancer cells are characterized by a massive overall loss of DNA methylation (20–60% overall decrease in 5-methylcytosine) and by the simultaneous acquisition of specific patterns of hypermethylation at CpG islands of certain promoters, which can reversibly or irreversibly alter gene function, thereby contributing to cancer progression [33]. Somatic mutations are frequently seen as the initial stage in cancer progression and are associated with aging and notable modifications in DNA methylation. This clarifies why older people are at a higher risk of developing cancerous growths [34]. Although consensus has yet to be reached on the precise age threshold for elderly patients, it is established that over 60% of initial cancer diagnoses and more than 70% of cancer-related deaths occurred within the people aged 60 years and above [35]. It can also be seen from the competing risk model established in this study that age has the same adverse prognostic contribution to survival outcomes, with a maximum calculated contribution value of nearly 50 points. These findings are consistent with data on elderly patients with colorectal cancer. While older patients with pT4 disease are more susceptible to severe postoperative complications, there is no consensus that age significantly impacts survival outcomes [36]. The prognosis for older patients may be influenced by factors such as stage at presentation, tumor location, preexisting comorbidities, and the type of treatment received. The results of this study suggest that women have a worse prognosis than men with tcBC among elderly patients. These findings align with those from a research project by Tracey et al [37], indicating that women diagnosed with bladder urothelial carcinoma exhibited increased cancer-related death rates. The higher prevalence of hypertension and diabetes in female patients over the age of 65 in their medical histories possibly contributed to reduced tolerance to radiotherapy, chemotherapy, and surgical interventions [38].

For patients with high-grade BC, the risk of recurrence, progression, and metastasis is significantly higher compared to low-grade patients, leading to a poorer prognosis [39,40], which is consistent with the result of elderly tcBC patients in this study. Multiple studies in the scientific literature have demonstrated that the forecast of bladder cancer is heavily impacted by the extent of penetration into the bladder. The severity and advancement of the condition become significantly intensified with the progression of the T stage [41–44]. However, in the present study, analysis results of the DCA curve (Figure 5) show that the competing risk model established by including radiotherapy, chemotherapy, surgery, and metastasis has a much higher prediction ability for survival outcomes of tcBC patients than the TNM staging system alone, whether in the training set or the validation set. In addition, the nomogram chart demonstrates that various distant metastasis locations had varying impacts on death rates. Patients with multiple distant metastasis locations had notably higher mortality rates compared to those with just 1 distant metastasis location. This result is similar to the research conclusions on metastatic BC by various scholars such as Shou and Dong [45,46].

Treatment strategies for tcBC patients are strongly linked to their prognosis, alongside age, sex, and tumor characteristics. Surgery plays a crucial role in tcBC treatment, with the surgical approach determined by the extent of tumor invasion. Current guidelines indicate that TURBT plus intravesical instillation is the best treatment option for most NMIBC patients, and a second TURBT should be performed for high-risk groups [47]. For patients with MIBC, the mainstream treatment approach involves neoadjuvant chemotherapy followed by radical cystectomy with pelvic lymph node dissection and urinary diversion. In addition to tcB, the prognosis of upper-tract urothelial carcinoma (UTUC), which is also a type of urothelial carcinoma, is significantly influenced by age. A Cox regression model developed by Ferro et al [48] across 21 medical centers identified that age over 70 years is an independent prognostic risk factor for UTUC patients undergoing radical nephroureterectomy, suggesting that chemotherapy can significantly reduce the risk of CSS in patients with tcBC. However, for some elderly patients, their physical condition may not meet the criteria for perioperative chemotherapy. Savin et al [49] reviewed 119 patients who underwent radical cystectomy at their hospital, examining postoperative complications and the use of perioperative chemotherapy. They found that the rate of perioperative chemotherapy use was significantly lower in patients aged 80 and older compared to patients under 80 years old. This discrepancy may affect overall survival rates and reduce the predictive accuracy of this study’s model for elderly patients. Therefore, elderly tcBC patients who are not suitable for chemotherapy, immunotherapy, or radiation therapy may be considered. A recent small-sample study conducted by Vaishampayan et al [50] included 20 patients, with a median age of 78.5 years (range, 58–95 years); 80% of the patients were over 70 years old, and 8 (40%) were over 80 years old. The results indicated that concurrent nivolumab and radiation therapy are tolerable for elderly patients who cannot tolerate chemotherapy. However, in older individuals with multiple comorbidities, the efficacy is limited, and patients with baseline programmed cell death ligand 1 combined prognostic score ≥5% had longer progression-free survival. Nevertheless, the predictive value of PD-L1 expression is quite limited, which may be partially due to the dynamic expression and heterogeneity within the tumor microenvironment. For instance, there seems to be a high degree of discordance in PD-L1 expression between primary and metastatic bladder cancer lesions [51]. The discussion regarding chemoradiation aligns closely with the statistical data in this study, where the proportions of all patients receiving chemotherapy and radiation therapy were 23.9% and 4.8%, respectively. This may be closely related to the challenges elderly patients face in tolerating treatment and the limited efficacy observed.

Certainly, in addition to these factors, BC poses a significant financial burden on the healthcare system, and patients’ insurance status also affects their treatment outcomes and prognosis to some extent. A study by Fletcher et al [52] on the impact of different insurance statuses on treatment outcomes for bladder cancer patients over 65 years old showed that, compared with those with private insurance, uninsured individuals experience worse prognoses and poorer care quality and are more likely to die from bladder cancer (adjusted hazard ratio [AHR] 1.49; 95% CI, 1.31–1.71). Expanding high-quality insurance coverage to marginalized populations may help alleviate the burden of this disease. For the elderly tcBC population in this study, we believe that insurance status will also impact prognostic outcomes. Although this factor was not included in the current research, we are confident that future studies will address this gap and improve our understanding of its significance.

This study has limitations. It relied on retrospective data from the SEER database, which may affect credibility. Additionally, key factors influencing patient prognosis, like tumor pathology, nutritional status, and lymph node positivity, were not included or were unavailable. After using immune checkpoint inhibitors like pembrolizumab and atezolizumab for metastatic tcBC, PD-1/PD-L1 expression in tissues has become vital for immunotherapy selection [53–55]. In future studies, efforts will be made to increase external validation set data to verify the model, and the PD-1/PD-L1 expression of patients will be recorded to analyze the impact of this factor on the prognosis of elderly tcBC patients.

Conclusions

In summary, after excluding deaths attributed to other causes, this study successfully identified pertinent risk factors influencing CSS in elderly patients with tcBC through analyzing cancer-specific mortality outcomes. These factors encompassed age, sex, race, marital status, TNM staging, grade, surgery, chemotherapy, and metastatic status of the brain, bone, liver, and lung. By establishing a competing risk model and translating it into a user-friendly web tool, clinicians in urological oncology can efficiently tailor personalized treatment and follow-up plans for elderly tcBC patients.

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750