Ovarian yolk sac tumor (OYST), often referred to as an endodermal sinus tumor, is a rare subset of ovarian malignancies that comprises approximately 2%–3% of all ovarian tumors [1, 2]. As a malignant ovarian germ cell tumor (MOGCT), it arises from differentiation of the extra-embryonic yolk sac and occurs predominantly in young premenopausal women. Among the subtypes of MOGCT, OYST is the second most prevalent histological type, accounting for roughly 20% of cases [3, 4]. Its prevalence is particularly notable in children, where it represents up to 60% of malignant ovarian tumors. Although OYST primarily originates in gonadal tissues, such as the ovaries and testes, it can also develop in extragonadal locations, including the mediastinum, brain, and retroperitoneum [5].

Due to its rarity, current knowledge about OYST is predominantly based on small retrospective studies. However, many of these studies include mixed cohorts of OYST and other MOGCT subtypes [6, 7], which introduces significant confounding factors. Moreover, there is a lack of large-scale or prospective data to comprehensively evaluate the prognostic factors for OYST. In this study, we performed a systematic, retrospective analysis using the Surveillance, Epidemiology, and End Results (SEER) database to explore potential prognostic factors for OYST. Furthermore, we created and validated a nomogram to predict clinical outcomes, with the aim of assisting clinicians identify high-risk patients, provide optimized prognostic counseling, and ultimately improve patient management.

MOGCT comprise several histological subtypes, including dysgerminoma, OYST, immature teratoma, and mixed germ cell tumors. Among these, OYST is recognized for its aggressive behavior and poor prognosis [7, 11]. Patients with OYST often lack specific symptoms in early stages, making diagnosis challenging [11, 12]. Most patients present with chronic abdominal pain or pelvic masses, while a minority seek emergency care due to complications such as ascites, tumor rupture, or torsion.

Research on OYST has predominantly consisted of small-scale retrospective analyses, combined analyses within ovarian germ cell tumors, or case reports due to its rarity [13, 14, 15, 16, 17, 18]. In this study we conducted a comprehensive analysis using the SEER database. Age, AJCC stage, regional lymph node involvement, and liver metastasis were identified as independent prognostic factors for OYST through LASSO-Cox regression modeling. Importantly, we developed and validated a nomogram to predict 3-, 5-, and 10-year survival rates, making this the first study to provide such a predictive tool for OYST.

Age is a well-recognized determinant of cancer prognosis, often linked to the accumulation of genetic mutations and immune senescence [19, 20]. Previous studies have emphasized its critical role in the survival of ovarian cancer patients [21, 22, 23]. Consistent with an earlier study [24], our analysis revealed that patients aged 24–39 years had a 4.8-fold increased risk of mortality compared to patients aged $\le$23 years, while those aged $\ge$40 years had a 10.2-fold increased risk. This underscores the importance of patient age as a significant and independent risk factor for survival from OYST. Analysis of the variance inflation factor (VIF) indicated there was minimal collinearity among model variables (age VIF = 1.12), reinforcing the robustness of these findings. However, the multivariate hazard ratio (HR) for age $\ge$40 years (10.16) was lower than the univariate HR (20.2), likely due to adjustments for other key prognostic factors such as AJCC stage and liver metastasis. Although the wide 95% CI (3.31–31.21) shows the variability of this estimate, the significant HR highlights the prognostic importance of patient age. Future studies with larger sample sizes are needed to refine the impact of age across different subgroups.

AJCC staging was identified as a pivotal prognostic factor, demonstrating the importance of tumor extent in determining survival outcomes [25]. Notably, the HR for AJCC Stage IV (16.69) was accompanied by a wide 95% CI (1.16–240.50), reflecting limited precision due to the small number of Stage IV cases. While statistically significant, the variability in this estimate necessitates cautious interpretation in clinical practice. The wide 95% CI indicates a need for larger studies to validate this finding and improve the precision of risk estimation for advanced-stage OYST. The presence of liver metastasis was identified as a critical prognostic factor in our model, consistent with its association with advanced disease and systemic dissemination [26]. Given its rarity in OYST, liver metastasis highlights the aggressive nature of these cases and the urgent need for effective systemic therapies tailored to high-risk patients.

Furthermore, regional lymph node involvement emerged as another significant indicator of poor prognosis. Patients with unknown lymph node status exhibited worse survival outcomes than those with negative lymph nodes, hinting at the potential value of systematic lymphadenectomy in assessing disease burden. However, the role of lymphadenectomy in the management of OYST patients remains controversial. While retrospective studies and meta-analyses suggest that lymphadenectomy may improve OS in advanced cases, its impact on progression-free survival (PFS) is less clear [27, 28]. As stated above, patients in our study with unknown regional lymph node status had poorer survival outcomes than node-negative patients, suggesting there may be a potential survival benefit from lymphadenectomy. However, Harter et al. [29, 30] reported no significant improvement in either PFS or OS from lymphadenectomy in advanced ovarian tumors, while highlighting increased postoperative complications and 60-day mortality. Larger prospective studies are therefore needed to comprehensively assess the benefits and risks of lymphadenectomy in the management of OYST patients.

Our study confirmed the accuracy and clinical utility of the nomogram-based predictive model for OYST prognosis. The C-indices for the training and validation cohorts (0.868 and 0.813, respectively) indicate strong discriminatory power, while calibration curves showed excellent agreement between predicted and observed survival rates. Time-dependent ROC analyses further support the model’s robust predictive capability, with AUC values exceeding 0.8. Moreover, DCA highlighted the superior net benefit of the nomogram in predicting 3-, 5-, and 10-year OS compared to the AJCC staging system. Additionally, the risk stratification system derived from the nomogram effectively classified patients into high- and low-risk groups, with Kaplan-Meier survival curves showing significantly poorer outcome in the high-risk group.

While our study offers valuable insights, several limitations must be acknowledged. First, as a retrospective analysis the study was inherently subject to selection bias and lacks prospective validation. Second, detailed treatment data such as chemotherapy regimens, dosages, cycles, and radiotherapy protocols were unavailable, thus limiting our ability to evaluate the impact of specific therapeutic approaches. Third, grading data was missing for 84% of patients, necessitating its exclusion from analysis and preventing a better understanding of the prognostic role of tumor grade. Fourth, missing data were categorized as “unknown” and were included in the analyses without imputation, potentially affecting the interpretation of certain variables such as regional lymph node status. Fifth, patients with a survival time shorter than one month were excluded, which may introduce survivorship bias by excluding cases of perioperative mortality or acute complications. Finally, external validation using independent datasets was not performed, restricting the generalizability of our findings.

Future multicenter, prospective studies with larger cohorts are essential to address these gaps and allow further refinement of the nomogram. Additionally, the collection of comprehensive treatment and pathological data should improve our understanding of prognostic factors and facilitate the development of tailored therapeutic strategies for OYST.

This study has presented a validated nomogram incorporating age, AJCC stage, regional lymph node status, and liver metastasis as independent prognostic factors. The nomogram provides accurate and clinically applicable survival predictions for OYST patients. This tool offers a visualized and quantitative approach for prognostic assessment, thereby facilitating personalized postoperative management and counseling. Further research is warranted to validate the model and explore its integration into clinical practice, with the ultimate aim of improving outcomes for this rare and aggressive malignancy.

The datasets used in this study are available in SEER databases (https://seer.cancer.gov/), and the data analysis process can be obtained from the corresponding author.

HC was responsible for the research design, data acquisition and analysis, data visualization and manuscript writing. DZ and DL were responsible for the research design, data verification and manuscript review. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

The study used data from published public data and all original research was ethically approved. Therefore, no ethical approval was required for this study.

Not applicable.

This research received no external funding.

The authors declare no conflict of interest.

During the preparation of this work the authors used ChatGpt-3.5 in order to check spell and grammar. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/CEOG31361.