Lung Metastasis Probability in Ewing Sarcoma: A Nomogram Based on the SEER Database

Abstract

Background. Up to now, an accurate nomogram to predict the lung metastasis probability in Ewing sarcoma (ES) at initial diagnosis is lacking. Our objective was to construct and validate a nomogram for the prediction of lung metastasis in ES patients. Methods. A total of 1157 patients with ES from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively collected. The predictors of lung metastasis were identified via the least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis. The discrimination and calibration of the nomogram were validated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical usefulness and net benefits of the prediction model. Results. Factors including age, tumor size, primary site, tumor extension, and other site metastasis were identified as the ultimate predictors for the nomogram. The calibration curves for the training and validation cohorts both revealed good agreement, and the Hosmer–Lemeshow test identified that the model was well fitted (p > 0.05). In addition, the area under the ROC curve (AUC) values in the training and validation cohorts were 0.732 (95% confidence interval, CI: 0.607–0.808) and 0.741 (95% CI: 0.602–0.856), respectively, indicating good predictive discrimination. The DCA showed that when the predictive metastasis probability was between 1% and 90%, the nomogram could provide clinical usefulness and net benefit. Conclusion. The nomogram constructed and validated by us could provide a convenient and effective tool for clinicians that can improve prediction of the probability of lung metastasis in patients with ES at initial diagnosis.

Is the Number of National Database Research Studies in Musculoskeletal Sarcoma Increasing, and Are These Studies Reliable?

“…Of the 94 studies that shared a major study question with at least one other study, 35% (33 of 94) reported discordant key findings concerning 10 study questions [1, 6, 8, 11‐13, 18, 23, 26, 29, 36, 39‐41, 44, 49, 53, 59, 63, 67, 68, 72, 79, 83, 93, 95, 98‐100, 106, 109‐111] (Table 2), 29% (27 of 94) reported mixed key findings concerning six study questions [3, 7, 15, 19, 22, 25, 27, 28, 30, 35‐37, 43, 45, 47, 50, 56, 75, 76, 82, 88‐91, 103, 104, 107] (Table 3), and 44% (41 of 94) reported concordant key findings concerning 12 study questions [2, 4, 5, 14, 16‐18, 20, 24, 31, 33, 38, 42, 46, 48, 52, 57, 58, 61, 71, 74, 75, 77, 78, 81, 84‐87, 91, 92, 96, 97, 101, 102, 105, 106, 108, 110‐112] (Table 4). Of the 299 studies in total, 20% (59 of 299) reported discordant or mixed findings [1, 3, 6‐8, 11‐13, 15, 18, 19, 22, 23, 25‐30, 35‐37, 39‐41, 43‐45, 47, 49, 50, 53, 56, 59, 63, 67, 68, 72, 75, 76, 79, 82, 83, 88‐91, 93, 95, 98‐100, 103, 104, 106, 107, 109‐111] and 14% (41 of 299) reported concordant findings [2, 4, 5, 14, 16‐18, 20, 24, 31, 33, 38, 42, 46, 48, 52, 57, 58, 61, 71, 74, 75, 77, 78, 81, 84‐87, 91, 92, 96, 97, 101, 102, 105, 106, 108, 110‐112]. Examples of topics with discordant or mixed findings include the effect of radiation on survival in several sarcoma types and the influence of race on treatment and survival.…”

Radiomics analysis based on CT for the prediction of pulmonary metastases in Ewing sarcoma

“…Some previous studies reported that gender was not a risk factor for pulmonary metastases of ES, larger tumors had a higher chance of pulmonary metastases, and the results of age, and primary location were controversial. Li et al found that surgery was a protective factor against pulmonary metastases and chemotherapy had no signi cant difference between MT and non-MT groups (12)(13)(14)(15).…”

Overall survival comparison between pediatric and adult Ewing sarcoma of bone and adult nomogram construction: a large population-based analysis

“…T stage is one of the prognostic factors in our nomogram, and some previous studies have shown that a larger tumor volume is related to poor prognosis ( 28 – 30 ). M stage is also a prognostic factor, representing whether metastasis will affect the patient's OS.…”

“…Wang J built a model for predicting ES mortality using least absolute shrinkage and selection operator (LASSO) analysis and multivariate logistic analysis with five factors (age, tumor size, primary site, tumor extension, and other site metastasis). However, that nomogram does not include treatment approach ( 28 ). Chen L created a nomogram to predict the OS of pelvic ES with four factors (age, race, tumor stage, and surgery) ( 9 ).…”