Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface

Abstract

Background. This study aimed at establishing and validating a quantitative and visual prognosis model of Ewing Sarcoma (E.S.) via a nomogram. This model was developed to predict the risk of lung metastasis (L.M.) in patients with E.S. to provide a practical tool and help in clinical diagnosis and treatment. Methods. Data of all patients diagnosed with Ewing sarcoma between 2010 and 2016 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A training dataset from the enrolled cohorts was built (n = 929). Predictive factors for L.M. were identified based on the results of multivariable logistic regression analyses. A nomogram model and a web calculator were constructed based on those key predictors. A multicenter dataset from four medical institutions was established for model validation (n = 51). The predictive ability of the nomogram model was evaluated by the receiver operating characteristic (ROC) curve and calibration plot. Decision curve analysis (DCA) was applied to explain the accuracy of the nomogram model in clinical practice. Results. Five independent factors, including survival time, surgery, tumor (T) stage, node (N) stage, and bone metastasis, were identified to develop a nomogram model. Internal and external validation indicated significant predictive discrimination: the area under the ROC curve (AUC) value was 0.769 (95% CI: 0.740 to 0.795) in the training cohort and 0.841 (95% CI: 0.712 to 0.929) in the validation cohort, respectively. Calibration plots and DCA presented excellent performance of the nomogram model with great clinical utility. Conclusions. In this study, a nomogram model was constructed and validated to predict L.M. in patients with E.S. for medical human-computer interface—a web calculator (https://drliwenle.shinyapps.io/LMESapp/). This practical tool could help clinicians make better decisions to provide precision prognosis and treatment for patients with E.S.

Incidence, prognostic factors, and a nomogram of lung metastases at diagnosis: a pan-cancer population-based study

“…The epidemiology of SLM has not been adequately evaluated in the available literature. In addition, studies to date have focused on one speci c primary cancer site and the impact of SLM on that speci c patient population, but there still have been no studies assessing trends, incidence, and survival across age, sex, ethnicity, grade, tumor size, and N stage groups based on different primary cancer types [22][23][24][25]. Especially, the largest body of literature on lung metastasis has focused on digestive system primary cancer [4; 26-30].…”

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

“…They help physicists and urologists distinguish between patients who should undergo pelvic Over time, nomogram is no longer limited to the study of oncology, but more applied to the study of diagnosis and prediction of non-tumor-related diseases. For example, nomograms were used to analyze the correlation between factors in genomics or proteomics and the development and progression of diseases (32)(33)(34); Nomograms were used to predict adverse outcomes in obstetrics or reproductive medicine (35-38); it can also be combined with radiomics to analyze the relationship between different image features or image segmentation factors and disease diagnosis (39, 40), predict chronic disease incidence in orthopedics (41)(42)(43), or analyze and detect the incidence of chronic diseases combined with metabolomics (37). In addition, the display modes of nomogram are gradually diversified.…”