Treatment algorithm in patients with ovarian cancer

Area of Science:

• Oncology
• Genetics
• Pharmacology

Background:

• Ovarian cancer is often diagnosed at advanced stages, leading to poorer survival and complex treatment decisions.
• Recent advances in understanding ovarian cancer subtypes and molecular features enable personalized treatment approaches.
• Key developments include improved diagnostic tools and systemic therapies like chemotherapy, targeted agents, and antiangiogenic drugs.

Purpose of the Study:

• To present a treatment algorithm for primary and recurrent ovarian cancer patients in Belgium.
• To integrate recently developed targeted agents into ovarian cancer treatment strategies.
• To support patient-tailored therapeutic decisions based on molecular characteristics and disease stage.

Main Methods:

• Review of current knowledge on ovarian cancer histological and molecular characteristics.
• Incorporation of advances in genetic testing for germline and somatic mutations (e.g., DNA repair).
• Development of a treatment algorithm considering patient and tumor-specific features, including targeted agents.

Main Results:

• An algorithm for ovarian cancer treatment is proposed, tailored to individual patient and tumor profiles.
• The algorithm includes options for primary and recurrent disease, emphasizing targeted therapies.
• Advances in genetic testing and targeted agents are highlighted as crucial for personalized medicine.

Conclusions:

• Personalized treatment strategies are essential for improving ovarian cancer patient outcomes.
• The presented algorithm provides a framework for implementing individualized medical strategies in Belgium.
• Integrating molecular insights and targeted agents is key to advancing ovarian cancer care.