AI to support and improve breast cancer treatment
Ongoing research project
Meet BreaCS
BreaCS is an innovative research project within the healthcare sector that focuses on developing an AI-based clinical decision support system (CDSS) to improve breast cancer treatment selection.
The primary goal of BreaCS is to create a quasi-real time AI system that combines various types of data, such as pathology, radiology, and clinical information, to generate uniquely smart AI models. These models will help clinicians make more informed decisions regarding breast cancer treatment options.
Our research partners
The consortium is a collaboration of six distinguished partners, comprising four EUSOMA certified hospitals and two technology partners. This collaboration fosters a multidisciplinary approach that seamlessly integrates cutting-edge technology with clinical expertise, resulting in more effective and innovative solutions. The synergy between the partners ensures that the project benefits from diverse perspectives and knowledge, ultimately driving the development of a state-of-the-art AI-based clinical decision support system that revolutionises breast cancer treatment and leads to improved patient care and outcomes.
- 1. Analysis of breast scans
- 2. Preventing unnecessary removal
- 3. Predicting patient response
AI models for enhanced decision making
The research consortium focuses on developing a system that combines various types of data, including pathology, radiology, and clinical information, to generate uniquely smart AI models. These models aim to provide healthcare professionals with comprehensive and up-to-date information that enables them to make more informed decisions regarding treatment options tailored to individual patients’ needs and circumstances. We'll work on the development of 3 distinct models.
1. Analysis of breast scans
The first AI model focuses on analysing radiology breast scans, including mammograms and MRIs, to predict pathological tumour size preoperatively. Accurate tumour size prediction is crucial for determining the most appropriate surgical approach, such as breast-conserving surgery or more invasive options.
2. Preventing unnecessary removal
The second model aims to preoperatively predict the need for axillary lymph node removal. Reducing false-positive rates can significantly decrease the number of unnecessary lymph node removals, preventing possibly serious side effects and complications for patients.
3. Predicting patient response
The third model focuses on predicting patient response to primary systemic therapy. By improving the precision and specificity of the model, the number of patients receiving ineffective treatment can be reduced, optimising the administration of systemic treatments.
The BreaCS project represents a significant step forward in the application of AI in healthcare. If successful, the resulting CDSS could revolutionise the way breast cancer treatment decisions are made, ultimately leading to improved patient care and outcomes.
Improved decision-making
By leveraging AI to analyse a wide range of data, the CDSS will provide healthcare professionals with comprehensive and up-to-date information to make better treatment decisions.
Personalised treatment
The AI models will allow for a more tailored approach to breast cancer treatment by considering the specific circumstances and medical history of each patient, ultimately improving patient outcomes.
Increased patient comfort
Personalised treatment avoids unnecessary treatments resulting in an overall improved mental and emotional well-being of the patient.
Increased efficiency
The CDSS could streamline the decision-making process, reducing the time and resources spent on manual data analysis and interpretation.
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