Consensus statements and treatment algorithm to guide clinicians in the selection of maintenance therapy for patients with newly diagnosed, advanced ovarian carcinoma: Results of a Delphi study

Introduction. Standard treatment of newly diagnosed, advanced ovarian carcinoma (OC) consists of cytoreductive surgery followed by platinum-based chemotherapy with or without bevacizumab. Maintenance therapy with PARP inhibitors and olaparib-bevacizumab has recently shown to significantly improve progression-free survival in the first-line setting. Some practical aspects of maintenance therapy, however, are still poorly defined.Aim of the study. To provide guidance to clinicians in the selection of maintenance therapy for newly diag-nosed, advanced ovarian carcinoma.Methods. A board of six gynecologic oncologists with expertise in the treatment of OC in Italy convened to ad-dress issues related to the new options for maintenance treatment. Based on scientific evidences, the board pro-duced practice-oriented statements. Consensus was reached via a modified Delphi study that involved a panel of 22 experts from across Italy.Results. Twenty-seven evidence-and consensus-based statements are presented, covering the following areas of interest: use of biomarkers (BRCA mutations and presence of homologous recombination deficiency); timing and outcomes of surgery; selection of patients eligible for bevacizumab; definition of response to treat-ment; toxicity and contraindications; evidence of synergy of bevacizumab plus PARP inhibitor. Two treatment al-gorithms are also included, for selecting maintenance therapy based on timing and outcomes of surgery, response to platinum-based chemotherapy and biomarker status. A score for the assessment of response to che-motherapy is proposed, but its validation is ongoing.Conclusions. We provide here consensus statements and treatment algorithms to guide clinicians in the selec-tion of appropriate and personalized maintenance therapy in the first-line setting of advanced OC management.& COPY; 2023 Elsevier Inc. All rights reserved.