Value of prediction models in determining treatment strategies in patients with advanced stage epithelial ovarian cancer

Abstract

This thesis aims to evaluate the value of prediction models in determining treatment strategies in patients with advanced stage epithelial ovarian cancer (EOC). The background of treatment and treatment decisions in these heterogeneous patient population is described in the first chapter. Chapter 2 investigates the accuracy of an offhand clinical assessment of irresectable disease and proposes a predictive model of suboptimal cytoreduction based on computed tomography (CT) scan and clinical parameters. Chapter 3 evaluates the incidence and causes of postoperative mortality after cytoreductive surgery for EOC. Chapter 4 aims to identify predictive parameters and generate a predictive model of 30-day morbidity. Chapter 5 concerns development of a predictive model of prognosis in patients with advanced stage EOC. Finally, chapter 6 discusses the content of this thesis and recommendations for further research are suggested