Using machine learning to estimate time-to-progression in ovarian cancer based on cancer registry and administrative health data

Background

Cancer progression and recurrence are not routinely registered in Belgium. A machine-learning algorithm was developed using administrative data to retrospectively estimate time-to-progression (including recurrence) of epithelial ovarian cancer. The algorithm was validated against a multicentric clinical reference dataset. Patients and methods: From the Belgian Cancer Registry, we extracted patients diagnosed with epithelial ovarian cancer during 2014-2018 and treated for their primary tumor in seven participating centers in Belgium. The reference dataset was constructed through a manual review of hospital records for the selected patients. Using a unique personal identification number, the cancer registry data and administrative records were linked, including reimbursed medical procedures and medication, vital status, and causes of death. The reference dataset was split into a training set and a test set. To estimate time-to-progression, we developed and validated a machine-learning algorithm (XGBoost classifier), framing the task as a discrete-time classification problem at a patient-month level.