Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC
Multi-layer molecular, imaging and clinical data are integrated to improve diagnosis, predict chemotherapy resistance and suggest targeted therapies in high-grade serous ovarian cancer. It supports precision treatment selection.
- What this means:
- A curator has read the source and confirmed our reading of it — the classifications that let this record be compared with others — along with the AI product and the clinical use. That is a check of our work, not a judgement on the AI.
- What would change it:
- The pair enters the AIH factory, where clinical, technical and governance findings are worked section by section.
- Who established it:
- An agent re-read the source and confirmed OUR reading of it — the family, the clinical problem, the point of care, what the AI outputs. Those are our judgements, not the registry's, which is why they are the ones checked. It also confirmed the record resolves to its source. No person has read it. This is not an independent check.
- Submitted
- 2Curated
- 3Maturing
- 4Validated
- 5Scaled
Plain-language summary
AIChoose an audience and generate a tailored summary on demand. The AI uses only what is on this page.
- Clinical problem
- Recommend a treatment
- Point of care
- Treatment planning
- Nature of AI output
- A recommendation
- Clinical specialty
- Oncology
- Care setting
- Hospital — inpatient
- Patient population
- Patients with high-grade serous ovarian carcinoma (HGSOC) · Cohort: N=200; ages 18+; female.
- Intended use
- Multi-layer molecular, imaging and clinical data are integrated to improve diagnosis, predict chemotherapy resistance and suggest targeted therapies in high-grade serous ovarian cancer. It supports precision treatment selection.
Full study description
Specific aims include: * Develop tools and methods for personalized medicine approaches to cancer patients. * Develop open-source visualization and interpretation software that facilitate clinical decision making via data integration and interpretation of multilevel data from cancer patients. * Rapidly identify HGSOC patients who are likely to respond poorly to current therapies combining information on digitalized histopathology samples, genomic and clinical data with AI methods. * Deploy validated personalized medicine treatment options using longitudinal measurement and ex vivo organoid cultures from cancer patients in clinical care.
- AI technique
- Classical machine learning, Hybrid
- Input data
- Genomic data, Medical imaging, Laboratory results
- Output type
- Recommendation
- Autonomy level
- Informs a human (advisory)
- Model provenance
- Research model
- Country
- Finland
- Deployment date
- 1 February 2012
- Sites
- 1
- Medical device
- No
- Data identifiability
- Not stated in the source
Similar deployments
AIStudy details
- Study type
- Interventional
- Sample size
- 200 participants
Contributors
- Deploying organisation
- Turku University Hospital · Hospital / health system · Finland
- Source record
- ClinicalTrials.gov · NCT04846933