Prostate cancer has a subtype called small-cell carcinoma (SCCP), which is rare but extremely aggressive and malignant. It accounts for about 0.36% of newly detected cases (1, 2). The majority of patients (70% to 80%) are diagnosed with advanced disease. Chemotherapy is the mainstay of clinical treatment (3, 4). Nevertheless, a number of studies have demonstrated that radiation or surgical procedures can enhance the prognostic outlook for these tumors (5–7). The observed discrepancy between these findings and the prevailing consensus may be attributed to limitations in the study, including a small sample size and an uneven distribution of patients (8–10).

The utilization of data models for analysis and prediction has become increasingly prevalent in the domains of population health assessment and oncology with the advent of big data medicine and precision medicine in today’s world (11, 12). Given the limited occurrence and variances among patients (8), the personalized evaluation of cancer survival duration and tailored therapies for diverse patients assume significant significance in the realm of SCCP. In the realm of rare diseases, the utilization of Surveillance, Epidemiology, and End Results (SEER) databases greatly enhances prognostic evaluations (13). These databases are esteemed for their extensive temporal scope and population-based data. Additionally, nomograms offer physicians a personalized and visually informative approach to prognostic assessment (14). By serving as a visual prediction model, nomograms aid in the comprehensive examination of prognostic risk factors, thereby facilitating treatment decision-making.

To our knowledge, limited research has been conducted on the prognostic assessment of SCCP. Furthermore, there is no comprehensive nomogram for assessing cancer- specific survival (CSS) in patients with SCCP. Therefore, the aim of this research is to evaluate and contrast the predictive features of SCCP in patients identified with small-cell prostate cancer. Prognostic factors will be integrated, using SEER’s multicenter case collection, and a nomogram will be developed to predict the 12- and 24-month CSS of patients. The nomogram’s intuitive and easy-to-use characteristics make it a crucial instrument for promoting precision medicine, as it aids in delivering individualized prognostic forecasts for diverse patients, thereby enhancing the standard of interaction between doctors and SCCP patients.

Pure prostate small cell carcinoma (PSCC) is an uncommon prostate tumor that exhibits an exceptionally high level of malignancy. Despite its rarity, there has been a noticeable upward trend in its incidence over the years. From 2012 to 2015, the number of newly diagnosed patients more than doubled compared to the period from 2004 to 2007, consistent with previous studies. This increase can be attributed to advances in diagnostic techniques, such as improved immunohistochemistry and genomics, enhancing SCCP diagnosis accuracy (16, 17). Notably, INSM1 has emerged as a sensitive and specific marker for SCCP identification (18).The biomarkers P501S and PSMA demonstrate greater reliability in discerning SCCP source compared to PSA (19).

The uncertainty surrounding the mechanism by which age functions as an independent prognostic factor requires further investigation (20). It is crucial to explore whether the unfavorable prognosis associated with older patients can be attributed to the heightened aggressiveness of tumor biology or is merely a consequence of their diminished physical condition and suboptimal chemotherapy. Racial disparities in prognoses emphasize the need to implement evidence-based strategies and ensure equitable access to resources, particularly in communities of color, to mitigate these disparities (21). The AJCC system, while founded on extensive data regarding prostate cancer, appears to lack efficacy in accurately staging SCCP. Furthermore, the overall staging performance in SCCP is comparable to that seen in small cell lung cancer.

Similar to previous research (22), PSA screening for prostate cancer may have some benefits, but its efficacy was limited in the context of SCCP (23). In cases of pure small cell carcinoma of the prostate (SCCP), where there is no disruption of follicular structure, serum PSA levels remain unaltered even in the presence of extensive metastases. Conversely, patients with a combination of small cell carcinoma and adenocarcinoma may exhibit elevated serum PSA levels, indicating limited diagnostic utility and lack of specificity for SCCP. Our study further revealed that among patients with available PSA data, 65% had PSA levels below 10 ng/ml, 76.25% had levels below 20 ng/ml, and only 23.7% had levels exceeding 20 ng/ml.

Although chemotherapy is a standard treatment option (24, 25), it has yielded unsatisfactory results (26). The development of new chemotherapeutic agents lacks momentum (27), and chemotherapy regimens using doxorubicin, etoposide, and cisplatin have some activity but high treatment-related toxicity (3). Their main shortcomings include a small sample size, low recruitment rate, and high treatment-related adverse events. The primary reason this study did not find radiotherapy and surgery to improve survival is that most patients were in advanced stages, consistent with previous studies. The significance of localized treatment was demonstrated in the National Cancer Database (NCDB) (5, 28, 29). It is evident that patients can derive greater benefits through early detection alone (16, 30). The efficacy of localized therapy depends heavily on the effectiveness of systemic chemotherapy (31).

The development of a prognostic assessment tool provides the benefit of offering an initial intuitive estimation of survival, enabling collaborative decision-making on appropriate treatment options for clinicians and patients s (32).However, it is important to emphasize that prognostic tools should not replace clinical judgment. Clinicians must consider individual variations, such as the severity of comorbidities and physical condition, when making decisions. While numerous predictive models exist for prostate cancer (33, 34) and neuroendocrine prostate cancer (35), our nomogram is specifically designed for pure SCCP with well-defined staging, eliminating potential data bias resulting from unclear staging. To fill this void, we created a training dataset that achieved a C-index of 0.768 ± 0.024, and a validation dataset with a C-index of 0.661 ± 0.041, respectively. These datasets were built by incorporating information from the SEER database (36–38) and building upon the findings of the prior study. New insights for clinical application can be obtained by constructing nomograms that predict overall survival at 1 year and 2 years.

In this study, it is necessary to take into account various constraints, including its retrospective nature. Our dataset did not allow the examination of chemotherapy, patient performance status, or co-morbidities, all of which could impact survival outcomes in cancer patients. In contrast to studies conducted at a single center, the precision of staging and pathologic diagnoses in national registries can differ greatly among different institutions. However, we addressed this limitation by focusing on measuring prostate cancer-specific survival rather than overall survival. This study has notable strengths, including the use of a population-based design and a substantial sample size, both of which are highly relevant in analyzing infrequent neoplasms.

CSS in SCCP was influenced by factors such as age, ethnicity, Summary stage, and the administration of chemotherapy. The nomogram for predicting CSS in SCCP was established in our pioneering population-based study on these risk factors. The nomogram exhibited excellent precision and practicality in both internal and external validation, showcasing its accuracy and clinical usefulness. It demonstrates superior potential utilization compared to traditional TNM staging and can assist healthcare professionals in making well-informed choices, offering a personalized prognostic benchmark for individuals with SCCP.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

YD: Writing – original draft, Writing – review & editing. JS: Conceptualization, Investigation, Software, Writing – review & editing. ZS: Data curation, Writing – review & editing. YW: Methodology, Supervision, Writing – original draft. LM: Project administration, Resources, Validation, Visualization, Writing – review & editing.