Overall Survival Prediction Models For Gynecological Endometrioid Adenocarcinoma With Squamous Differentiation (ge-asqd) Using Machine-learning Algorithms

This study aimed to construct machine learning models to forecast the 5-year survival rate of GE-AsqD patients. Data was extracted from the Surveillance, Epidemiology, and End Results (SEER) database, with the inclusion criteria being patients diagnosed between 2004 and 2015 with primary sites in the endometrium and ovary. Clinical pathologic variables such as age at diagnosis, race, sequence number, marital status, stage, surgery status, radiation status, chemotherapy status, regional nodes examined, AJCC T, N, M stage, and primary site were selected. The X-tile software was used to analyze the data. No ethical issues were present as the information was de-identified and publically available.