Development and external validation of a nomogram to predict the prognosis of patients with metastatic prostate cancer who underwent radiotherapy

Introduction

Prostate cancer (PCa), one of the most frequently diagnosed cancers in males, is the fifth leading cause of cancer-related mortality, with 375,304 estimated deaths in the year 2020, and is the first cause of death due to cancer in 48/185 (26%) countries (1). The belief that PCa can be detected at an early, localized stage has driven prostate-specific antigen (PSA) screening since it was first proposed by Albertsen et al. in 1991 (2). Although the prevalence of PSA testing has decreased the fatality rate from PCa (3), PSA testing remains uncommon worldwide, so many patients are diagnosed at advanced stages (4). About 20% of patients still have local lymph nodes or distant metastases at diagnosis, and about 4% have distant metastases (5). The leading site of metastasis for PCa is bone, and 90% of deaths from PCa are associated with bone metastases, which are the leading cause of morbidity and mortality in PCa patients (6).

Primary tumors can metastasize via circulating tumor cells, and treatment of primary tumors may be beneficial for metastatic PCa (mPCa) patients because tumor self-seeding leads to local progression (7). There are various treatment options for mPCa, including drug therapies like rucaparib and radiation therapy (8). Several studies have shown that radiation therapy in patients with mPCa benefits their survival (9-12). Hence, in clinical practice, radiotherapy is sometimes employed as a treatment option for patients with mPCa. Studies on patient mortality after receiving mPCa radiation have been published. Stolzenbach et al. validated the overall mortality (OM) reduction associated with external beam radiotherapy in M1a and M1b patients with PSA ≤10.0 ng/mL but not in M1b patients with PSA >10.0 ng/mL (13).

Nomogram is a graphic analytical instrument for prognostication that finds prognostic variables linked to clinical illness and has been extensively used in various cancers (14). Several studies have developed nomograms for constructing miRNA-mRNA networks in PCa and performing prognostic analyses (15), as well as predicting OS rates in PCa patients (16). To date, a predictive nomogram for mPCa patients receiving radiotherapy has not been established. In this study, we aimed to address this gap by developing and validating a nomogram-based method for predicting survival in this patient population, leveraging the Surveillance, Epidemiology, and End Results (SEER) database and external validation. Our findings will enable the creation of personalized therapy regimens and inform medical decision-making for mPCa patients receiving radiotherapy. We present this article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-24-313/rc).

Methods

Population selection

We obtained most data for this study from the SEER database (https://seer.cancer.gov/seerstat/), which compiles information from 18 cancer medical centers, which account for 30% of the American population (17). The most data used in this study were obtained from SEER Stat 8.4.0.1. Since SEER is a publicly accessible, de-identified database, no ethical approval or informed consent was required. The SEER database tracks patients longitudinally, with regular follow-ups for outcomes such as survival and recurrence. Follow-up procedures in SEER involve regular updates on patient vital status and cause of death through linkages with state and national mortality databases. The main outcome measure for this study was overall survival (OS), defined as the time from diagnosis to death from any cause. Additionally, external validation data were obtained from The First Affiliated Hospital of Nanchang University. This study obtained ethical approval from the Ethics Committee of The First Affiliated Hospital of Nanchang University [approval ID: (2022) CDYFYYLK (11-031)] and followed all guidelines, with written informed consent obtained from all patients. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The inclusion criteria included: (I) diagnosed between 2004 and 2015; (II) primary site codes C61.9; (III) received radiotherapy; (IV) patients with distant metastases; (V) first malignant primary cancer. Exclusion criteria consisted of the following: (I) multiple primary cancer; (II) tumor-node-metastasis (TNM) stage was unknown; (III) PSA was unknown; (IV) the Gleason score (GS) was unknown; (V) race or marital status was unknown. Ultimately, 1,171 people from the SEER database and 138 patients from the tertiary center were added.

Variables defined

Patient demographics and clinical characteristics were included: age, marital status, race, PSA, GS, T-stage, N-stage, M-stage, chemotherapy and survival time (months). The main outcome measure was OS. Optimal cut-off values for age and PSA were determined using X-tile software: age was divided into <68, 68–78, and >78 years, and PSA into <18, 18–67, and >67 ng/mL (Figure S1). Marital status was categorized as “married”, “single”, and “D/S/W” (divorced/separated/widowed). Race was categorized as White, Black and Other (American Indian/AK Native, Asian/Pacific Islander). T-stages were divided into T1–T4, N-stages were N0, N1, and M-stages were M1a, M1b, M1c. Chemotherapy was classified as “Yes” or “No/unknown”.

Statistical analysis

Firstly, the research population was randomly split into training (70%) and validation (30%) cohorts using a computerized randomization method. The external validation cohort consisted of every qualified patient from 2000 to 2022. Categorical variables were expressed as frequencies (percentages) and compared using Chi-squared tests. For continuous variables, the median of the interquartile range was reported and compared using Mann-Whitney’s U tests. All P values were two-sided, with P<0.05 considered statistically significant. Secondly, univariate Cox regression analysis was used to determine statistical differences between registration variables. Then, variables with a P value of less than 0.05 were included in the multivariate Cox regression analysis to control for potential confounding effects and identify prognostic factors with significant independence. Finally, we evaluated the performance of the nomogram by measuring the area under the curve (AUC) for the 1-, 3-, and 5-year time points of the receiver operating characteristic (ROC) curves. Comparing the expected and actual survival rates using calibration curves for 1-, 3-, and 5-year follow-up intervals allowed researchers to confirm the nomogram’s calibration. Moreover, decision curve analysis (DCA) was conducted to assess the clinical utility of the nomogram. Patients were stratified into high- and low-risk groups based on their total risk score, calculated from the independent prognostic factors. The optimal cut-off for risk stratification was determined using X-tile software. Kaplan-Meier curves were used to compare OS between the high- and low-risk groups. All statistical analyses were performed using R version 4.2.2 and SPSS version 26.0.

Results

Demographic and clinical characteristics

A total of 1,171 patients, including 819 in the primary set and 352 in the validation set, were identified with mPCa and received radiotherapy. Meanwhile, the number of patients in the external validation group was 138. Of SEER set patients, 370 (31.6%) patients were between 68–78 years old, 291 (24.8%) patients had a PSA between 18–67 ng/mL, and 272 (23.2%) patients had a GS of 7. The patients’ predominant race and marital status were White (n=870, 74.3%) and married (n=793, 67.7%). The majority of patients were stage T2 (n=454, 38.8%) or N0 (n=862, 73.6%), and a small proportion were stage M1a (n=77, 6.6%). For treatment, 89.3% of patients did not receive chemotherapy. Mean survival times were 52.8, 50.9, 57.1 and 45.8 months in the SEER, training, validation and external validation cohorts, respectively (Table S1).

Determination of the independent prognostic factors

Univariate Cox regression analysis identified age, marital status, PSA, GS, T-stage, N-stage, M-stage, and chemotherapy as risk-related variables for OS. In multivariate Cox regression analysis, seven variables (age, marital status, PSA, GS, T-stage, M-stage, and chemotherapy) were identified as independent predictors; race and N-stage were insignificant factors (Table 1).

Nomogram construction and validation

We created the nomogram prediction model based on the training set, and the internal validation and external validation set served as the basis for determining the model’s precision (Figure 1). Each factor was assigned a score in the nomogram, and the combined score represented the OS rate at 1, 3, and 5 years, where a greater total score indicated a negative perspective. The nomogram C-index of OS was 0.656 [95% confidence interval (CI): 0.634–0.678], 0.676 (95% CI: 0.645–0.707) and 0.670 (95% CI: 0.621–0.719) in the training, internal validation and external validation groups, correspondingly. The AUC values of the model were 0.686 at 1 year, 0.713 at 3 years, and 0.748 at 5 years in the training cohort (Figure 2A), 0.679 at 1 year, 0.732 at 3 years, and 0.735 at 5 years in the validation cohort (Figure 2B) and 0.724 at 1 year, 0.711 at 3 years, and 0.750 at 5 years in the external validation cohort (Figure 2C). Furthermore, the calibration plots of the three groups demonstrated that the model’s expected probabilities and real probabilities were tightly aligned (Figure 3). DCA indicated that the net benefit of our nomogram model was superior to that of TNM staging, indicating its high clinical utility (Figure 4).

Figure 1 Nomogram for predicting 1-, 3- and 5-year overall survival. Position patient values on each axis. Draw a vertical line on the points axis to determine how many points each variable value has. Sums the points of all variables. Find the sum in the ‘Total points’ row. Draw a vertical line toward 1-year, 3-year and 5-year survival probability. The axes determine survival probabilities at 1-year, 3-year and 5-year. PSA, prostate-specific antigen; D/S/W, divorced/separated/widowed.

Figure 2 Receiver operating characteristic curves of the nomogram predicting 1-year, 3-year and 5-year overall survival in the training cohort (A), validation cohort (B) and external validation cohort (C). AUC, area under the curve.

Figure 3 The calibration curves of the nomogram were used to predict OS in mPCa patients treated with radiotherapy at 1-year (A,D,G), 3-year (B,E,H), and 5-year (C,F,I) in the training cohort (A-C), the validation cohort (D-F) and the external validation cohort (G-I), respectively. OS, overall survival; mPCa, metastatic prostate cancer.

Figure 4 DCA to evaluate the 1-, 3-, and 5-year OS in the primary group (A-C), the validation group (D-F) and the external validation group (G-I). DCA, decision curve analysis; OS, overall survival.

Risk stratification system

We created a risk classification system based on the abovementioned independent prognostic indicators linked to OS to further verify the reliability and efficacy of the nomogram. The optimal cut-off numbers for the total risk score were determined using X-tile software (Figure S1). We stratified all patients into two groups based on their total scores: a high-risk group (total score >209) and a low-risk group (total score ≤209). The two risk subgroups showed a significant difference (P<0.001) in their survival outcomes (Figure S2). Patients in the high-risk group had a worse OS than those in the low-risk group, demonstrating how well-reliable the mortality risk classification method created using the prognostic nomogram is at predicting mortality.

Discussion

PCa is the second most frequent form of cancer in men, with a peak incidence between the ages of 65 and 70 years (18). In 2019, there were an estimated 174,650 new confirmed cases of PCa in America, equal to one out of every five new cancer diagnoses, posing a public health issue in the male group (19). The backbone of treatment for metastatic disease typically consists of systemic treatment regimens, including chemotherapy and second-generation antiandrogens (20). As for radiotherapy for mPCa, it is hypothesized that radiation induces vascular remodeling (leakage and regression) in addition to direct tumor killing through DNA damage, which decreases tumor oxygenation and leads to tumor cell apoptosis and necrosis (21). A study shows that patients with oligo mPCa treated with radiation therapy have significantly improved progression-free survival time (22).

The construction of predictive survival models enables the classification of treatment based on the risk of death for each patient. This model facilitates the development of tailored treatment plans that consider the patient’s risk level and response to treatment, thereby optimizing clinical outcomes. The efficacy of radiotherapy on mPCa has been confirmed in several meta-analyses and clinical studies. Onal et al. found that clinical T-stage, PSA, GS and International Society of Urological Pathology (ISUP) grading were independent predictors of prostate-specific membrane antigen (PSMA)-positive pelvic lymph nodes in a large cohort study of treatment-naïve patients with moderate-to-high-risk PC (23). Reverberi et al. showed that data had demonstrated the benefit of systemic androgen deprivation therapy (ADT) in combination with radiotherapy for primary tumors with low metastatic disease. Further studies have demonstrated improved local control and increased progression-free survival with immediate treatment of metastases (24). Morgan et al. identified a clinically significant improvement in OS in patients with radiotherapy treated with metastatic hormone-sensitive prostate cancer (mHSPC) (25). However, few studies have focused on finding survival-related independent prognostic predictors in mPCa patients receiving radiotherapy. Therefore, we constructed a nomogram to determine the individual OS for this subset of the population based on patient-related and tumor-related factors. Our extensive population-based study revealed that age, marital status, PSA, GS, T-stage, M-stage, and chemotherapy were significant independent prognostic factors for OS. Furthermore, we thoroughly evaluated the nomogram’s performance and validated its accuracy.

Our study found that the OS of mPCa patients receiving radiotherapy decreased with age, and patients over 78 years old had a worse outlook than patients younger. In addition to factors of their physiological function, this may also be due to lagging diagnosis or inadequate treatment in older patients (26). Age is an independent predictor of elevated GS and rising pathological stage, with significantly higher clinical stage and biopsy Gleason grade at the time of PCa diagnosis in older men than younger men, associated with a poorer prognosis (27,28). According to our research, marriage status had an autonomous impact on OS in mPCa patients receiving radiotherapy, which also found that marital status impacts prognosis. Unmarried, divorced, separated and widowed patients had lower survival rates than married patients, which is consistent with some studies (29-31). Through a variety of pathways, marital status is linked to cancer mortality. Married patients may have more financial resources and extensive social networks, resulting in a healthier lifestyle and improved access to healthcare (32-34). As a blood-based cancer biomarker, PSA is unique. It is used in all significant PCa detection and patient management phases, i.e., screening, risk of recurrence stratification, post-diagnostic surveillance, and treatment monitoring (35). High PSA levels in PCa patients often indicate tumor recurrence or metastasis (36,37); this could explain the poor prognosis of patients with high PSA levels (>67 ng/mL) in our study. Previous studies have shown the relationship between the prognosis of PCa and GS (38,39). These findings demonstrate that the chance of death for men with PCa rises as the GS does (40). Izumi et al. also pointed out that metastasis and high GS were unfavourable factors for survival in PCa treated with radiation (41). This was confirmed in our study, where for mPca patients treated with radiotherapy, patients with a GS >7 had a worse prognosis than patients with GS =7 and <7. From the nomogram, the T- and M-stages are other important independent prognostic variables for OS in PCa patients. According to earlier research, it is critical for a patient prediction that T-stage and M-stage are linked to disease development in most PCa patients (41,42). Some studies show the benefit of receiving chemotherapy on OS of mPCa (43-45). Nevertheless, chemotherapy was connected to a harder prognosis, according to our nomogram study. This may be because patients who received chemotherapy had a poorer underlying condition, leading to worse outcomes. Alternatively, our study’s limited sample size of patients who received chemotherapy may have introduced bias into our results.

Several predictive survival models in PCa patients have been reported (37,46-48). Still, as far as we know, our study provides the first nomogram to predict survival in mPCa patients receiving radiotherapy. Our study provides valuable insights for the future development of treatment and prognosis for PCa patients, especially those receiving radiotherapy with distant metastases. The use of a comprehensive nomogram for individualized patient evaluation can facilitate the development of personalized treatment strategies, thus enhancing prognosis and maximizing patient benefit. However, some limitations must be considered. Firstly, retrospective studies are susceptible to selection bias. Secondly, some essential indicators, such as patient lifestyle, radiotherapy cycles and doses, and patient complications, were not covered in our database records. Lastly, the bulk of the study’s subjects were Americans. Therefore, prospective therapeutic trial studies are required to determine whether the results can be applied to various populations.

Conclusions

In this study, we investigated the prognostic factors of patients with mPCa receiving radiotherapy. Our findings indicate that age, marital status, PSA, GS, T-stage, M-stage, and chemotherapy are independent prognostic factors for patient OS. To facilitate individualized evaluation and precise treatment of patients, we developed a nomogram to predict patient OS, which was internally and externally validated for its accuracy and reliability. Our nomogram can aid physicians in predicting the prognosis of this patient population and developing appropriate monitoring and follow-up strategies.

Acknowledgments

Funding: This study was supported by the Jiangxi Provincial “Double Thousand Plan” Fund Project (grant No. jxsq2019201027).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-24-313/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-24-313/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-24-313/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-24-313/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study obtained ethical approval from the Ethics Committee of The First Affiliated Hospital of Nanchang University [Approval ID: (2022) CDYFYYLK (11-031)]. Prior to participation, all patients were fully informed about the study’s purpose, procedures, potential risks, and benefits. Written informed consent was obtained from each patient, ensuring that they understood and agreed to their involvement in the study.

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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