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

Journal: Molecular therapy. Oncology
Published Date: Jun 18, 2025

Abstract

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 (FRĪ±) 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.

Authors

  • Morgann Madill
    Masonic Cancer Center and Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MA 55455, USA.
  • Arpit Aggarwal
    Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
  • Anant Madabhushi
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
  • Britt K Erickson
    Masonic Cancer Center and Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MA 55455, USA.
  • Andrew C Nelson
    Department of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, MN 55455, USA.
  • Emil Lou
    Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA.
  • Martina Bazzaro
    Masonic Cancer Center and Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MA 55455, USA.

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