In the field of medical research, tumor risk prediction models are used to predict the future incidence and prognosis of certain tumors. Specifically, nomograms represent a mean to establish a statistical model of the quantitative relationship between multiple risk factors and tumor occurrence and/or prognosis. The purpose of such models include: informing patients of the risk of onset or prognosis, screening high-risk groups, and helping doctors make clinical decisions. In 2003, van Zee et al.  first proposed a nomogram model to predict the risk of non-sentinel lymph node metastasis in sentinel node-positive breast cancer. This nomogram was based on a regression model to intuitively present the probability of outcomes. The advantage is that it can provide better individualized prognostic risk assessments in the form of intuitive graphics, which have definite value in clinical practice and can provide a reference for individualized clinical decision-making. Thus, such predictive nomograms are widely used in clinical oncology [15–17]. All of the nomograms presented showed better discriminatory capacity than did classical staging systems. However, a lack of external verification was the common limitation for these studies. External validation of cohorts from other countries or prospective randomized clinical trials are necessary to confirm a model’s performance.
A subtype of EOC, SOC accounts for approximately 85% of EOC diagnoses. Therefore, it is important to separately analyze the performance of nomograms with regards to their prognostic predictions for this EOC subtype. In this study, we used the external verification method. We analyzed a training cohort of 6957 SOC patients from the SEER database and a validation cohort of 1244 SOC patients from two tertiary institutional hospitals to develop and validate easy-to-use nomograms for predicting the OS at 3- and 5-years. Our study identified age, grade, and AJCC stage as independent predictors of OS. We observed that the important predictors of improved OS were younger age, early clinical stage, and well-differentiated grade, which is consistent with previous studies . In this study, the majority of cases (> 85%) were diagnosed in women over 50-years-old; the older the patient was, the worse the prognosis was. Generally, older patients were more likely to present worse survival outcomes due to lower immune responses .
Many scholars believe that clinical staging is an important factor that affects the prognosis of ovarian cancer. In our study, > 70% of patients were diagnosed with advanced (III-IV) ovarian cancer. The later the clinical stage, the lower the 3- and 5-year OS rates. Patients with early FIGO staging could be more thoroughly removed after surgery, as the residual lesions were relatively small, chemotherapy-sensitive, and had a low risk of recurrence and metastasis; thus, the prognosis of these patients was good. Patients with late FIGO stage disease have tumor cells in the body that spread more widely, making it difficult to implement complete surgical treatment. Patients with a poor tolerance to chemotherapy have poor prognoses. Especially after stage IIIA, the later the stage, the higher the risk of death and the worse the prognosis .
HGOS is the most common subtype of EOC, and a majority of HGOS patients subsequently develop platinum-resistance with relapse; which demonstrates their overall poor prognosis . In this study, 84.4% of patients were HGOS. As can be seen from the nomogram, the prognosis of these patients was worse than that of LGOS. It is generally believed that tumors with low histological grades have high degrees of malignancy, such as rapid disease progression and poor survival and prognosis due to adverse biological behaviors (rapid cell proliferation, diffusion, and strong invasion. The higher the degree of tissue differentiation, the slower the proliferation of tumor cells in the body. Weaker invasion of body tissues was associated with lower degrees of malignancy, slower disease progression, relative sensitivity to chemotherapy, longer survival times, and better prognoses.
We validated the accuracy of our nomograms using the C-index, ROC analysis, and calibration curves in both the training and validation cohorts. The C-index and ROC curves all exceeded 0.6 for OS in the external verification processes. Calibration curves also demonstrated good performance of the nomograms. These results show that our final nomogram exhibited good discriminatory performance and calibration. Previous studies have reported using the SEER database to establish nomograms to predict the prognosis of EOC. For example, Wang et al. developed and internally validated nomograms to predict OS and cancer-specific survival (CSS) of EOC patients, achieving a C-index of 0.733 for OS and 0.747 for CSS . Chen et al. constructed and validated nomograms to predict OS and CSS in patients with ovarian clear cell carcinoma, achieving a C-index of 0.802 (95% CI, 0.773–0.831) for OS and 0.802 (95% CI, 0.769–0.835) for CSS. The nomograms were validated and found to be of satisfactory predictive value, which could aid in future clinical decision making .
The traditional AJCC staging system does not appear to be able to accurately assess tumor prognosis because it only includes a limited subset of important prognostic factors. Several studies have reported better performance of nomograms than conventional staging systems and have proposed such models as promising tools for evaluating prognosis. Shi et al. generated nomograms to predict OS and CSS in patients with gastric cardia cancer, achieving a C-index of 0.714 (95% CI, 0.705–0.723) for OS and 0.759 (95% CI, 0.746–0.772) for CSS. This study showed that these nomograms gave better predictions than nomograms based on TNM stages . Xu et al. developed and validated a nomogram based on log of odds between the number of positive lymph nodes and the number of negative lymph nodes to predict OS and CSS for EOC patients, achieving a C-index of 0.757 (95% CI, 0.746–0.768) for OS and 0.770 (95% CI, 0.759–0.782) for CSS. Additionally, this model performed favorably compared with the currently used Federation of Gynecology and Obstetrics (FIGO) model, with concordance indices of 0.699 (95% CI, 0.688–0.710; P < 0.05) and 0.719 (95% CI, 0.709–0.730; P < 0.05) for OS and CSS, respectively. This study also suggested that log of odds between the number of positive lymph nodes and the number of negative lymph nodes works as an independent prognostic factor for survival in EOC patients regardless of tumor stage; thus, the nomogram may be superior to the currently used FIGO staging system for predicting OS and CSS among post-operative EOC patients .The nomogram developed in our study also showed better predictive accuracy for survival compared with the AJCC 7th staging system. This nomogram model enabled risk stratification of patients; thus facilitating personalized treatment plans and follow-up schedules.
Our study had several advantages. First, we developed and validated nomograms using clinically important long-term oncological OS outcomes, which reduced bias by scoring the model performance using high-quality data based on the large sample sizes from SEER. Second, although some studies have established nomograms to predict the prognosis of SOC, but the nomogram model of these studies only received internal validation. In contrast, this study adopted the method of external verification, using cohorts from other countries, which was necessary to confirm performance. Third, compared with the AJCC-stages, DCA curves in this study showed that our nomograms provided excellent clinical utility. All variables included in the SOC nomogram could be obtained easily, which could facilitate the application of nomograms in clinical practice. Furthermore, we aimed to establish a prediction model for general SOC patients with common characteristics, such that results were not affected by ethnic and regional differences. Although the SEER database contains information from the US population, our external validation population was from China. The C-statistic and AUC values of the training and validation sets were very similar, demonstrating the great discriminatory power of the nomogram. It also showed that this nomogram was applicable to regions other than the United States, which will facilitate the application of nomograms in clinical practice.
However, there were also several limitations to this study. First, the included variables were relatively simple and there were more detailed data that could have been included, such as family history of ovarian cancer, primary tumor diameter, positive lymph nodes, ascites cytological results, location of metastasis, chemotherapy regimens and cycles, sensitivity to chemotherapy, and genetic results. Second, this study was constructed using retrospective data, among which there may have been some undetected potential factors that introduced bias.