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. 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.Aims
Methods
The clinical utility of routine cross sectional imaging of the
abdomen and pelvis in the screening and surveillance of patients
with primary soft-tissue sarcoma of the extremities for metastatic
disease is controversial, based on its questionable yield paired
with concerns regarding the risks of radiation exposure, cost, and
morbidity resulting from false positive findings. Through retrospective review of 140 patients of all ages (mean
53 years; 2 to 88) diagnosed with soft-tissue sarcoma of the extremity
with a mean follow-up of 33 months (0 to 291), we sought to determine
the overall incidence of isolated abdominopelvic metastases, their
temporal relationship to chest involvement, the rate of false positives, and
to identify disparate rates of metastases based on sarcoma subtype.Objectives
Methods
