Enhancing Female Health Outcomes
Ovarian cancer
Aim is to understand and improve early detectionas well as personalized treatment of ovarian cancer using real-world data from the UNCAN.eu platform.
Addressing Lethality & Screening Gaps
The Challenge: Overcoming Advanced-Stage Diagnosis
Ovarian cancer can affect the ovaries, fallopian tubes, and the primary peritoneal cavity. Ovarian cancer is the eight most common and the most lethal of all female cancers globally. Worldwide, approximately 324,000 new diagnoses and around 207,000 deaths per year are reported.
Silent Progression
Early symptoms are rare, leading to advanced-stage diagnosis in the majority of cases and hence poor prognosis.
Screening Limitations
To date, there is no reliable non-invasive screening test for early detection of ovarian cancer.
To date, there is no reliable non-invasive screening test for early detection of ovarian cancer. Early symptoms are rare, leading to advanced-stage diagnosis in the majority of cases and hence poor prognosis and quality of life. In addition, there are treatment challenges due to the heterogeneity of ovarian cancer. Current therapies often fail to address this diversity, leading to suboptimal outcomes. Early detection, accurate diagnosis, and personalized and targeted treatment of ovarian cancer is crucial to improve survival.
By using the potential of UNCAN.eu platform to enable data access from multiple hospitals across Europe, real-world evidence coupled with causal design and analytic methods can be used to better understand ovarian cancer initiation and progression as well as to derive risk-based, personalized ovarian cancer treatment strategies.
AI & Causal Inference
The Use Case Objectives
The objective of the Ovarian cancer use case is to improve treatment while accounting for risk factors and factors that modify a response to treatment and to improve early detection and accurate diagnosis of ovarian cancer. As such the Ovarian Cancer Use Case will demonstrate how the use of data via the UNCAN.eu platform can inform and improve both screening and treatment of ovarian cancer.
- Understand cancer initiation and progression based on patient profiles and clinical parameters.
- Derive risk-tailored, personalized treatment recommendations using causal design and decision-analytic modeling.
- Develop an AI model for a semiautonomous robotic system to enhance ultrasonography screening.
- Improve diagnostic accuracy and reduce healthcare professionals' workload through automated image analysis.
The first objective of the Ovarian Cancer Use Case is to better understand ovarian cancer initiation and progression based on patients’ profile and clinical parameters in different risk groups. Based on benefit-harm balance and cost effectiveness, and quality of life using a combination of causal designs, analytic causal inference methods and decision-analytic modeling, recommendations for risk-tailored, personalized ovarian cancer treatment for these different risk groups will be derived. As such the Ovarian Cancer Use Case will demonstrate the potential of developing data-driven recommendations for health decision science and policy making using real world evidence via UNCAN platform and facilitate future data use in research.
The second objective is to develop an AI model for a semi autonomous robotic system to enhance ultrasonography screening of ovarian cancer by using image data integrated with the UNCAN platform. By automating image acquisition, processing, and analysis, the system aims to reduce healthcare professionals’ workload and improve early detection and diagnostic accuracy for ovarian cancer early detection.
Advancing Evidence-Based Medicine
Expected Impact
Through the purposeful use of harmonized data from a decentralized hub in combination with advanced causal design, causal inference and health decision science methods, the findings of this Ovarian Cancer Use Case aim to reduce ovarian cancer mortality and improve quality of life of women through personalized, risk-stratified treatments. This approach promises significant impacts on personalized medicine, healthcare efficiency, and patient outcomes. It will robustly evaluate risk-adapted treatments, by avoiding biases and providing concrete clinical answers, thus optimizing clinical workflows through decision-analytic models for better long-term health outcomes, benefit-harm balances, quality of life, and cost effectiveness. Results of this Use Case will improve evidence-based decision making and guide health technology assessments, clinical guidelines, and healthcare policies.
For researchers
Researchers will benefit from this Use Case as the framework can be applied to areas beyond oncology.
For healthcare professionals
This approach promises significant impacts on personalized medicine, healthcare efficiency, and patient outcomes by optimizing clinical workflows through decision-analytic models.
For policymakers
Results of this Use Case will improve evidence-based decision making and guide health technology assessments, clinical guidelines, and healthcare policies.
A SUSTAINABLE RESEARCH LEGACY
Importantly the applied framework of data use in the Use Case will improve data interoperability and reuse and demonstrate the usability and functionality of the UNCAN platform and provide guidance for future data providers and users.