Aims. The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Methods. Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results. A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752. Conclusion. This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment. Cite this article: Bone Joint J 2023;105-B(6):702–710

Aims

Socioeconomic and racial disparities have been recognized as impacting the care of patients with cancer, however there are a lack of data examining the impact of these disparities on patients with bone sarcoma. The purpose of this study was to examine socioeconomic and racial disparities that impact the oncological outcomes of patients with bone sarcoma.

Deformity of the proximal femur in fibrous dysplasia leads to deviation of the mechanical axis of the hip, which may lead to the development of secondary osteoarthritis (OA). This study investigated the prevalence and predisposing factors for the development of OA in patients with fibrous dysplasia of the proximal femur. We reviewed the records of 209 patients from our institutional database with fibrous dysplasia of the proximal femur, investigating possible predisposing factors including patient demographics, the extent of the coxa vara deformity, the presence of peri-articular disease, and the overall burden of skeletal disease. Of the 209 patients, 24 (12%) had radiological evidence of OA in the ipsilateral hip. The prevalence was significantly higher in patients with polyostotic fibrous dysplasia compared with those with monostotic disease (p < 0.001). In a subgroup analysis of patients with polyostotic disease, the extent of deformity (quantified using the neck–shaft angle), and the presence of peri-articular disease (whether in the head of the femur or the acetabulum) were significant predictors of osteoarthritis (neck–shaft angle likelihood ratio (LR) = 0.847 per 1° increase, p = 0.004; presence of lesion in the head of the femur LR = 9.947, p = 0.027; presence of lesion in the acetabulum LR = 11.231, p = 0.014).

Our data suggest that patients with polyostotic fibrous dysplasia have a high risk of developing secondary OA of the hips. This risk is higher in patients with peri-articular disease, and those with a more severe deformity of proximal femur.

Cite this article: Bone Joint J 2015;97-B:1007–11.