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
Dedifferentiated chordoma is a rare and aggressive variant of the conventional tumour in which an area undergoes transformation to a high-grade lesion, typically fibrous histiocytoma, fibrosarcoma, and rarely, osteosarcoma or rhabdomyosarcoma. The dedifferentiated component dictates overall survival, with smaller areas of dedifferentiation carrying a more favourable prognosis. Although it is more commonly diagnosed in recurrences and following radiotherapy, there have been a few reports of spontaneous development. We describe four such cases, which were diagnosed
