Treatment selection in advanced ovarian cancer

Abstract

In this thesis, we evaluated different prediction models predicting the outcome of cytoreductive surgery to facilitate treatment selection for patients with advanced stage ovarian cancer. Treatment options are primary cytoreductive surgery follow by chemotherapy, or neoadjuvant chemotherapy combined with interval surgery. We investigated different prediction models, based on CT scan, laparoscopy, clinical parameters and serum biomarkers. We performed two systematic reviews of the literature to investigate all literature on the subject and to define the remaining knowledge gaps. We developed and externally validated a new model based on CT scan parameters. In a randomized clinical trial we found that incorporation of a diagnostic laparoscopy in the work-up before start of treatment for advanced stage ovarian cancer patients prevents futile laparotomies, without increasing health care costs. And performed the external validation of different prediction models. Furthermore, we evaluated the new subdivision of stage IV in IVA and IVB and suggested a different subdivision based on lymph node metastasis instead of pleural effusion. All different models are combined in a decision tree for treatment selection, to aid the clinical implementation of the described models