A nomogram for determining the disease-specific survival in Ewing sarcoma: a population study

Ewing sarcoma (ES) is the second most common malignant primary osseous sarcoma in children and adolescents [1]. Bone ES constitutes a family of malignant small round blue cell tumors with neuroectodermal origins, among which 85–90% have the classic t (11; 22) EWS/FLI1 translocation [1, 2]. The overall survival (OS) rate for ES has improved remarkably over the past two decades due to advances in multimodality therapies. In the US, the 5-year survival rates increased from 16% in the 1970s to 39% in the 1990s/early 2000s among patients with metastatic disease. The survival parameter in patients with localized disease increased from 44 to 68% [3]. Despite these improvements, a large proportion of patients with ES still suffer from disease- or treatment-related morbidity or mortality. The early identification of high-risk patients can help provide adjuvant therapies or trial options. Given the clinical uniqueness of ES, prognostic tools are urgently needed to predict survival in ES patients accurately.

Nomograms are reliable and convenient tools for estimating tumor prognosis [4, 5]. In this study, we aimed to establish a comprehensive prognostic evaluation system. The data of ES patients in the Surveillance, Epidemiology, and End Results (SEER) program database registries during 1990–2015 were screened and extracted. We then analyzed the extracted data and subsequently created and validated a nomogram containing significant and reliable variables for quantifying the survival of ES patients.

ES is an rare and aggressive type of malignancy that normally develops in young patients from childhood to early adulthood [13]. ES is the second most common primary malignant bone tumor in people younger than 30 years (second only to osteosarcoma) and the most common primary malignant bone tumor in those younger than 10 years. The annual incidence of ES among Caucasians is less than 3 per 1,000,000 [3], thereby indicating that data from single-center studies cannot provide adequate sample sizes. Therefore, this study was based on a large-sample database of the SEER program, which initially started with eight registries in 1973 and has continuously added other participating sites over time. At present, the database includes 18 geographically diverse areas representing 26% of the US population with efforts to reflect the racial, economic, and social diversity of the country as a whole [2, 6, 14]. The neoadjuvant chemoradiation treatment of ES began in the early 1990s [15]. To obtain reliable research results, we identified 2,643 patients with ES in the SEER program database from 1990 to 2015. ES mostly occurs in young people. In our study, most of the patients were ≤ 30 years old, accounting for 78.4 and 79.8% in the training and validation cohorts, respectively. Table 1 presents that most of the patients were male, white, had a marital status of single/domestic partner, diagnosed in the 2000s or 2010s, treated with surgery, and treated with chemotherapy; these results were consistent with previous research findings [16‐19]. Although ES has the highest incidence in people under the age of 30 [20], the prognosis is better for those with a younger age of onset and worse for those with a higher age of onset [1]. Similarly, in our nomogram (Fig. 1), the prognosis of people older than 30 was worse than that of people younger than 30. Regarding the cause of this phenomenon, Lee et al. [19] and Grevener et al. [21] found that adult patients received few cases of chemotherapy, and older patients were more likely to have multiple comorbidities, including diabetes, high blood pressure, and secondary cancer, which complicated the situation.

The long-term survival rate of ES for nonmetastatic disease at presentation has improved from 10 to 15% to 60–70% since the early 1990s through the application of multimodality approaches, including surgery, radiotherapy, and neoadjuvant chemotherapy [12, 22, 23]. However, ES exhibits an aggressive behavior that often results in lung metastasis, which is a poor prognostic factor given that only 20% of patients with metastases can survive for a long time [1, 2, 20]. The early identification of high-risk ES patients is helpful in providing adjuvant treatment or trials. Existing clinical staging systems only consider tumor size and histological metastasis. For example, the staging system of the American Joint Committee on Cancer can only estimate the limited clinical risk of ES. Therefore, the use of Cox regression analysis and the developed nomogram provides a comprehensive predictive model that includes not only the system demographics but also the therapy of surgery and other clinical parameters.

A nomogram is a convenient graphical representation of a mathematical model. It provides an intuitive way to combine important factors and predict a specific endpoint. The nomogram is also a reliable tool for quantifying risk and widely used in applied tumor prognoses. A well-developed clinical nomogram is a popular decision-making tool that can be used to predict the outcome of an individual and benefit both clinicians and patients [24]. Nomograms in many studies [2, 16, 17, 20, 25] indicate that being black and aged appear to be high-risk factors. However, small size tumor and surgery treatment demonstrate improved outcomes in DSS for ES. This trend is understandable given the aggressive therapies needed to treat such disease. Patients with metastatic diseases at the initial presentation have worse prognoses than those with confined diseases [2, 13, 18, 25]. Knowledge of these features will be helpful in clinical decisions.

Similar to previous studies [10, 26, 27], we applied IDI and NRI to evaluate whether the newly constructed prognostic model performed well and whether it should be used in clinical practice. Compared with radiotherapy and chemotherapy, surgery is the most effective means of treating ES [2, 18]. The new model containing the therapy of surgery showed good discrimination and calibration, in which both IDI and NRI for 3, 5, and 10 years of follow-up examinations showed improved C-index, as mentioned in the Results section.

Finally, our newly constructed nomogram model included a wide range of clinical risk factors, namely, age at diagnosis, race, EOD, tumor size, and surgery, which were easily available and routinely collected from historical records. Figure 4 shows the results of our DCA, wherein the abscissa and ordinate are the threshold probability and net benefit rate, respectively [28‐31]. To the best of our knowledge, this study is the first to use IDI, NRI, and DCA in the verification of the predictive abilities of nomograms for ES. Thus, the nomogram is helpful to accurately predict the 3-, 5-, and 10-year survivals of ES patients.

Nomograms are an important component of modern medical decision-making. We developed a reliable nomogram for determining the prognosis and treatment outcomes of ES patients in the US. However, external data verification is still required in future applications, especially for regions outside the US.