Abstract A018: Identifying and integrating global ovarian cancer patient priorities to guide artificial intelligence (AI) research in ovarian cancer
Bibliographic record
Abstract
Abstract Background: Ovarian Cancer Research Alliance (OCRA; USA), Ovarian Cancer Action (OCA; UK), Ovarian Cancer Canada (OCC; Canada) and the Ovarian Cancer Research Foundation (OCRF; Australia) have launched the Global Ovarian Cancer Research Consortium. The Consortium unites four leading ovarian cancer research funding organizations to combine resources, expertise and determination to accelerate progress where it’s desperately needed. The Consortium’s inaugural joint initiative is a $1M (USD) international research grant program to harness the power of Artificial Intelligence (AI) to improve ovarian cancer outcomes. To ensure this program aligns with patient needs, the Consortium sought to understand research priorities from individuals with lived experience of ovarian cancer, the results of which will help inform this funding call. Objectives: To understand priorities from people with lived experience of ovarian cancer, in the context of applying AI to improve outcomes, and to support direction of the AI Accelerator Grant funding round. Methods: Each organization conducted a survey to collect qualitative data from their respective patient and public involvement (PPI) networks. In total, there were 657 respondents: 62% diagnosed with ovarian cancer, 19% caregivers, and 19% others affected. Responses were pooled and thematically analyzed to identify shared priorities. Results: Consistent research priorities emerged across all geographies: Early detection: reliable early detection tools or screening tests, biomarker research, symptom awareness to support earlier clinical intervention. Better treatments: more effective and less toxic options, targeted therapies, immunotherapy, alternatives to chemotherapy, personalized medicine. Disease recurrence: recurrence drivers, methods to detect, delay or prevent recurrence, supportive care for those living with recurrent or incurable disease. Risk & prevention: improved risk prediction for those with a family history or known genetic mutation, expanding preventative strategies. Respondents in the UK were additionally asked about the use of AI in ovarian cancer research. Between 75-80% expressed positive or broadly supportive views. Perceived benefits included the ability to analyze large, complex patient datasets, identify recurrence risk or treatment responses faster, and pattern recognition that may not be identifiable to the human eye. The most common concern expressed was that AI should not replace compassionate care or clinical judgment. Conclusions: People affected by ovarian cancer are broadly supportive of the use of AI in ovarian cancer research to accelerate progress in a cancer type with limited treatment options and poor survival rates. While broadly optimistic, patients emphasized that AI must complement, not replace, human oversight in clinical settings. Embedding lived experience into research funding strategies, including emerging fields like AI, ensures that innovation is patient-informed, ethically grounded, and directed toward the areas of greatest need. Citation Format: Sarah DeFeo, Faye Hobbs, David Hunt, Jessica Lawson, Kristin McGowan, Marie-Claire Platt, Alicia Tone, Amy Wilson. Identifying and integrating global ovarian cancer patient priorities to guide artificial intelligence (AI) research in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr A018.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".