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:
- The source has been read and our reading of it confirmed — the classifications that let this record be compared with others, the AI product, and the clinical use. That is a check of our work, not a judgement on the AI.
- What happens next:
- Nothing, unless this use case is taken up for assessment. Then the AI product and its clinical use are examined together in seven sections — purpose, evidence, CE conformity, legal duties, ethics, safety, and what a hospital must have in place — each signed by a named institution.
- Checked by:
- Software, not a person. It re-read the source and confirmed our classification, and that the record resolves to its source. This is not an independent check.
- Submitted
- 2Curated
- 3Maturing
- 4Validated
- 5Scaled
- 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