In this study, based on a large cohort of 95812 patients from SEER database, we identified seven risk factors and construct a competing risk nomogram based on these prognostic factors to predict the probability of the occurrence of CSD within 5 years after RP for each patient with PCa. Furthermore, based on the difference in nomogram scores, we developed a novel risk stratification for postoperative CSD in patients with PCa. Our risk stratification has potential clinical value and may help clinicians better identify patients who still need active intervention after RP. The results showed that the discrimination of our stratification system was not weaker than the commonly used EAU risk stratification based on D'Amico stratification.
Competing risk nomogram is a kind of widely used risk predicting model in many fields in oncology such as lung cancer, breast cancer, and colorectal cancer [22–24]. The nomogram can incorporate many key factors of the disease into the prognosis prediction model and can consider the weight of each variable to make the prediction model more accurate. In addition, the graphical representation helps to more intuitively evaluate the individual situation of each patient, which is more practical. [25] At the same time, competing risk nomogram has its unique advantages compared to traditional nomogram or other prognosis predicting models. The competitive risk nomogram is based on competing risk analysis methods such as CIF and Fine and Gray’s proportional subdistribution hazard approach, rather than the Kaplan-meier method and Cox proportional risk regression commonly used in other types of models [16]. Competitive risk analyses not only consider the survival and death of patients, but also consider the impact of death caused by other factors on the endpoints of interest such as CSD. This is especially important in the research of PCa, because a large part of PCa patients may die due to other factors before developing CSD [13]. To our knowledge, there is still no research reported on the competitive risk prognosis prediction model for the prognosis of PCa patients after RP.
In the field of PCa, the currently commonly used nomogram is Stephenson nomogram. It is developed by Stephenson et al. to predict disease progression after salvage radiotherapy (SRT), with data from a multi-institutional retrospective cohort of 1540 patients. Seven variables were used to construct the nomogram including PSA before SRT, surgical margins, GS, PSA double time before SRT, lymph node metastasis and androgen deprivation therapy administration before or during SRT. [26] However, there are still some defects with Stephenson nomogram. Due to the limitation of inclusion and exclusion criteria, it is not widely applicable to PCa patients who have received RP. At the same time, it paid little attention to hard endpoints such as CSD. In the cohort of the original study, its c-index was 0.69, and the c-index obtained after the test in another study was even lower [27]. Therefore, for predicting the survival of PCa patients after RP better, a more accurate and versatile nomogram is still needed.
In our study, the competing risk analyses identified 7 prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and GS biopsy. Among them, GS had the greatest influence on survival outcomes. Many studies have reported the relationship between GS and the prognosis of PCa [28–30]. International Society of Urological Pathology (ISUP) reported that GS can be divided into five groups (2–6, 7(3 + 4), 7(4 + 3), 8, ≥ 9) according to prognosis, and this was consistent with our research results [30]. With the increase of GS, the patient's nomogram score was also increasing, that is to say, the possibility of the patient developing CSD within 5 years was increasing. In the nomogram, we could find that GS 4 + 3 = 7 group was with an obviously higher score than GS 3 + 4 = 7 group with. This was also consistent with the latest American Urological Association (AUA) clinical guideline, which indicated that many researches had demonstrated that the prognosis of GS 4 + 3 was significantly worse than GS 3 + 4 [31, 32]. Pathological extension was another important prognostic factor whose weight was second only to GS. It has been widely accepted that poor pathological findings such as extracapsular invasion and seminal vesicle invasion are related to disease recurrence and poor prognosis [33–35].
In addition to the above-mentioned well-known prognostic factors, our study also found the impact of race and marital status on the prognosis of PCa patients. Our nomogram showed that African Americans had the highest risk of CSD after RP, followed by Caucasian and other races. This finding was consistent with some studies published in recent years. According to statistics from researchers, the average annual incidence of PCa among African Americans was 60% higher than that of Caucasian men. Besides, compared with other races, African Americans have the highest mortality rate [36, 37]. The causes of the result were very complicated. For example, In the United States, PCa tended to be larger in African Americans and was more likely to metastasize than white men [38]. From a genetic perspective, some gene mutations related to disease progression are more common in African Americans, such as TP53 mutations and MYC amplification [39]. Several risk-associated single nucleotide polymorphisms were found to be overexpressed in African Americans [40]. At the same time, African Americans may face some social barriers such as health insurance, which may affect the treatment and management of the disease [41]. Our competing risk analyses also identified marital status as an independent prognostic factor. More and more researchers have paid attention to the impact of this sociological factor on the disease. Outcomes of numerous studies showed that married marital status was a protective factor for the occurrence and development of a variety of tumors, including PCa. Marriage may be a multifaceted representation of many protective factors including social support. [42, 43]
EAU risk stratification based on D’ Amico stratification is currently a common risk stratification system for PCa patients, which divided patients into Low-risk group, Intermediate-risk group, and High-risk group for predicting the risk of disease recurrence [2]. In our study, we compared the novel risk stratification based on the nomogram with EAU risk stratification. The results showed that our risk stratification system had better discrimination with a C-index over 0.8 and could better detect patients at higher risk of the occurrence of postoperative CSD after adjustment of competing risk analyses. The high-risk group obtained through our risk stratification had a significantly higher risk of CSD than OCSD, which could better exclude the interference of death caused by non-tumor factors on the model. Our advantages may come from many aspects, such as a large cohort, more prognostic factors, and independent analyses of competing risks. At the same time, our research provides quantitative and graphical prognostic tools, which help to make more accurate assessments of each patient.
Our study revealed 7 main independent prognostic factors that affect the occurrence of CSD in patients after RP and explored the application of these factors in identifying high-risk patients through the nomogram and risk stratification. At present, the guidelines pointed out that there were multiple managements for patients undergoing RP, including adjuvant treatment, salvage treatment, watchful waiting, etc. However, due to the lack of high-quality prospective data, the inclusion and exclusion criteria of patients are still controversial. [2, 44] In our study, the risk stratification proposed by the nomogram provided a reference for the selection criteria for the postoperative management of patients. Taking into account the differences in the risk of CSD, the high-risk group may require more active intervention, while the low-risk group may be more suitable for watchful waiting.
There are still several limitations to our study. First, our research is based on a large retrospective cohort. We still need more prospective clinical trials to contribute more precise data. Second, due to the SEER database’s limitations, we are unable to obtain some data that can enrich our research outcomes, such as patients' functional status and disease progression, as well as some more detailed clinical parameters such as PSA double time. Although our prediction model has reached a relatively high accuracy (C-index > 0.8), in the future we can try to use these parameters to further optimize the nomogram and risk stratification system. Third, we also lack additional independent external validation sets, and this is our important work goal in the future.