Reframing response in neoadjuvant chemotherapy for ovarian cancer: Integrating molecular and functional biomarkers

Neoadjuvant chemotherapy (NACT) is an established treatment strategy for advanced epithelial ovarian cancer, particularly for patients with high tumour burden or poor surgical fitness. Despite improvement in resectability and reducing perioperative morbidity, current methods for evaluating response, namely imaging, serum biomarkers and histopathology, lack sensitivity and fail to provide timely insights for treatment adaptation.

This review critically examines how advances in molecular diagnostics and computational analytics are reshaping the assessment of therapeutic response during NACT in ovarian cancer. The work focuses on the translational potential of liquid biopsy-derived analytes as dynamic biomarkers of treatment efficacy. In parallel, we evaluate the contribution of machine learning and digital twin technologies in refining response prediction, surgical planning, and personalized therapy design.

This review synthesizes established and emerging approaches for monitoring response to neoadjuvant chemotherapy in ovarian cancer. We begin by summarizing current clinical tools and highlighting key developments in response-guided treatment strategies. We then examine the potential of liquid biopsy biomarkers, particularly circulating tumour DNA (ctDNA), to provide real-time insights into treatment response. Next, we discuss how clinical and molecular diagnostics combined with computational methods, including machine learning and digital twin technologies, could support personalized treatment design.

Future management of ovarian cancer during NACT is likely to move toward real-time, integrated assessment platforms that merge molecular, imaging, and clinical information. While these multimodal approaches show strong promise for enabling precision-guided neoadjuvant care and more individualized therapy, their clinical adoption will depend on robust prospective validation, standardization, and demonstration of meaningful benefit in patient outcomes.