Quantitative and qualitative metrics of tumor stroma in predicting ovarian cancer outcomes and expansion of its study with AI-based tools

Epithelial ovarian cancer remains one of the deadliest gynecologic malignancies, with late-stage diagnosis, high recurrence rates, and resistance to platinum-based chemotherapy contributing to poor survival outcomes. Central to the effective management of ovarian cancer is the thorough evaluation of diagnostic and prognostic indicators. Critical determinants encompass the extent of the tumor; its stage and grade; and level of the circulating biomarker, CA-125. Additional tumor cell-centric factors such as BRCA1/2 mutation status, homologous recombination deficiency, and folate receptor-alpha (FRα) protein levels inform initial treatment and maintenance strategies. Unfortunately, these markers alone cannot fully predict outcomes or significantly improve survival rates. This review emphasizes the body of data suggesting that both quantitative and qualitative metrics of tumor stroma play a crucial role in the prognosis and outcomes of epithelial ovarian cancer. We examine quantitative and qualitative metrics such as stromal proportion, tumor density, stiffness, and texture. We explore how artificial intelligence (AI) tools advance the measurement of these parameters, offering unprecedented opportunities to integrate stromal biomarkers into clinical decision-making. By synthesizing emerging evidence, we propose a framework for leveraging stromal properties—individually and in combination—as novel prognostic indicators to improve outcomes for patients with ovarian cancer.