Highly Accurate Breast Cancer Diagnosis (HAMAM) integrates biological knowledge, novel imaging modalities and modelling for more accurate breast cancer detection.

Despite tremendous advances in modern imaging technology, both early detection and accurate diagnosis of breast cancer are still unresolved challenges. Unnecessary biopsies are taken and tumours frequently go undetected until a stage where therapy is costly or unsuccessful. The EU funded project HAMAM tackles this challenge by providing a means to seamlessly integrate the available multi-modal images and the patient information on a single clinical workstation. Based on knowledge gained from a multi-disciplinary database, populated within the scope of this project, suspicious breast tissue will be characterised and classified.

Specifically, the HAMAM project will:

• develop tools needed for integrated visualization and analysis of datasets from different  imaging modalities, most notably X-ray mammography, DCE MRI, 2D/3D ultrasound, and positron emission mammography (PEM)
• provide proper pre-processing and standardisation tools that will allow for optimal comparison of disparate data
• develop spatial correlation methods to allow for improved multimodal workflows with respect to reading efficiency and security, eventually leading to combined, multi-modal tissue and lesion models
• derive advanced schemes for computer aided detection and diagnosis based on multi-modal data that will be key in improving the accuracy in identifying breast cancer
• build in adaptability that allows for the integration of other sources of knowledge such as biophysical tumour models, known risk factors including family history of cancer, hormonal and environmental factors, and genetic data, and
• build a teaching file to be used to train clinicians in actually using the technologies and knowledge acquired in this project.

With HAMAM, Europe has the potential to strengthen its leadership in the whole area of image-based breast cancer diagnoses.