Evidence map›Paper›PMID 40524858›Full record

ReviewMolecular therapy. Oncology2025

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

Morgann Madill, Arpit Aggarwal, Anant Madabhushi, Britt K Erickson, Andrew C Nelson, Emil Lou, Martina Bazzaro

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Molecular therapy. Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Morgann MadillMasonic Cancer Center and Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MA 55455, USA.
Arpit AggarwalDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30322, USA.
Anant MadabhushiDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30322, USA.
Britt K EricksonMasonic Cancer Center and Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MA 55455, USA.
Andrew C NelsonDepartment of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, MN 55455, USA.
Emil LouDivision of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA.
Martina BazzaroMasonic Cancer Center and Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MA 55455, USA.

Funding

BLRD VA IK6 BX006185
6 · The paper itself

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 (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.

Indexed as

artificial intelligenceepithelial ovarian cancerMT: Regular Issueoutcomesprognostic factorstumor stromatumor-stromal proportiontumor texture

Identifiers

PMID40524858
PMCPMC12169772

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.