Metronomic Chemotherapy in Ovarian Cancer: Current Scenario and AI-Integrated Future Strategies

Ovarian cancer (OC) is a globally prevalent malignancy associated with a high mortality rate and marked biological heterogeneity, with most cases arising from epithelial cells and presenting as serous, endometrioid, or clear cell subtypes. Standard management is largely stage dependent and primarily involves cytoreductive surgery followed by platinum- and taxane-based chemotherapy, with modifications based on disease extent and patient factors.

In recent years, the therapeutic landscape of OC has evolved with the introduction of maintenance strategies and targeted therapies, particularly driven by advances in molecular profiling and the identification of biomarkers such as BRCA mutations and homologous recombination deficiency (HRD). These developments have led to the clinical integration of PARP inhibitors and anti-angiogenic agents such as bevacizumab, which have improved disease control and survival outcomes as part of standard treatment strategies in OC, whereas immunotherapeutic approaches remain largely investigational and are currently limited to clinical settings. Metronomic chemotherapy (MCT), characterized by the continuous administration of low-dose chemotherapeutic agents, has emerged as a promising alternative to conventional maximum tolerated dose regimens. MCT offers reduced systemic toxicity while exerting sustained antitumor effects through modulation of the tumor microenvironment, inhibition of angiogenesis, and enhancement of antitumor immune responses, thereby addressing key limitations of standard chemotherapy, including resistance and cumulative adverse effects. Furthermore, the integration of artificial intelligence (AI) into metronomic treatment strategies holds significant potential for optimizing drug selection, dosing schedules, and patient stratification. AI-driven tools can facilitate predictive modeling, high-throughput data analysis, and personalized treatment planning, ultimately enhancing therapeutic efficacy while minimizing toxicity.

This review summarizes current management strategies in OC with particular emphasis on maintenance therapies, targeted approaches, and emerging evidence supporting MCT and AI-enabled approaches as potential future directions in OC therapy.