Development and Validation of Nomogram-Based Prognosis Tools for Patients with Extremity Osteosarcoma: A SEER Population Study

Abstract

Objective. Osteosarcoma, usually occurring in the extremities, is the most common malignant bone tumour. The purpose of this study is to develop and validate nomogram-based prognosis tools for survival (OS) and cancer special survival (CSS) of patients with osteosarcoma of the extremities via the application of survival analysis. Materials and Methods. A total of 1427 patients diagnosed with osteosarcoma of the extremities during 2004–2015 were selected from the National Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results- (SEER-) Medicare database. The samples were randomly assigned to either the training set ( n = 856 ) or the validation cohort ( n = 571 ). Kaplan–Meier (K–M) survival analysis was conducted to calculate patients’ 1-, 3-, and 5-year OS and CSS rates. Cox proportional hazard ratio (HR) regression models were employed to identify and examine the factors that have a significant impact on OS and CSS with data from the training cohort. Results. The results of univariate and multivariate analyses performed in the training cohort indicated that older age, increased tumour size, higher grade, distant tumour extension, amputation, or no surgery (all HR > 1 , P < 0.05 ) were risk predictors of poor OS and CSS. Subsequently, the independent prognosis signatures were utilised to construct nomograms. The concordance index (C-index), calibration plot, and decision curve analysis (DCA) were simultaneously used to validate the nomograms. The internally validated C-index values of the OS and CSS prediction models for the training set were 0.752 (95% confidence interval [CI]: 0.738–0.765) and 0.754 [95% CI: 0.740–0.768], respectively. Then, the models were validated in the validation cohort population, which also demonstrated the models had good reliability for prognostication. Conclusions. The SEER cohort of patients with osteosarcoma of the extremities can be employed to produce effective tools that can assist in prognosis modelling.

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

“…Improved CSS was associated with female sex compared with male sex and surgery compared with no surgery. The influencing factors are consistent with previous studies [7,34,37,[39][40][41].…”

Prognostic factors for overall survival of conventional osteosarcoma of the appendicular skeleton

“…In our series, increasing age was protective, reducing the likelihood of mortality by 4%. In contrast, Huang et al 20 noted an increased risk of mortality in patients over the age of 22 years. This finding should, however, be interpreted cautiously amid the small sample size and the small number of patients aged over 30 years.…”

“…We observed that a single peak median age of 19 years (IQR 14.0 to 27.0) is similar to that previously reported in HICs. 4 , 20 , 21 Cole et al 4 and Duong et al 21 interrogated the epidemiology of osteosarcoma and emphasized bimodal distribution with an increased incidence rate in the second decade of life. In our series, increasing age was protective, reducing the likelihood of mortality by 4%.…”

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

“…The employment of validated nomogram has become more popular in clinical practice due to the model’s precision and usability. 101 In the TARGET cohort study of osteosarcoma patients, two significant independent risk factors, namely metastasis status and PRS-score level, were identified and were used to construct validated nomograms for predicting 1-, 2-, 3-, 4-, and 5-year OS rates. Previous studies 3 , 4 implied that 15–20% of osteosarcoma patients have detectable metastasis at diagnosis, 85–90% of which occurs in the lungs.…”