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Highly accurate breast cancer diagnosis through integration of biological knowledge, novel imaging modalities, and modelling (HAMAM)

Contact the HAMAM Coordinator:

Dr Wiro Niessen

(Project Coordinator), Erasmus MC, Department of Medical Informatics and Radiology
P.o. Box 2040, 3000 CA, Rotterdam, Netherlands

email: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

 

HAMAM participants

• EIBIR gemeinnuetzige GmbH zur Foerderung der Erforschung der biomedizinischen Bildgebung (AT)
• University College London (UK)
• MeVis Research gGmbH (DE)
• MeVis Medical Solutions AG (DE)
• Eidgenoessische Technische Hochschule Zuerich (CH)
• Radboud Universiteit Nijmegen - Stichting
• Katholieke Universiteit (NL)
• The University of Dundee (IE)
• Charite-Universitätsmedizin Berlin (DE)
• Boca Raton Community Hospital, Inc (US)

 

 

 

Despite tremendous advances in modern imaging technology, both early detection and accurate diagnosis of breast cancer are still unresolved challenges. Today, a variety of imaging modalities and image-guided biopsy procedures exist to identify and characterize morphology and function of suspicious breast tissue. However, a clinically feasible solution for breast imaging, which is both highly sensitive and specific with respect to breast cancer, is still missing. As a consequence, unnecessary biopsies are taken and tumours frequently go undetected until a stage where therapy is costly or unsuccessful.

HAMAM will tackle 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 large multi-disciplinary database, populated within the scope of this project, suspicious breast tissue will be characterised and classified.

HAMAM will achieve this by;
- Building the tools needed to integrate datasets / modalities into a single interface.
- Providing pre processing / standardization tools that will allow for optimal comparison of disparate data
- Building spatial correlation information datasets to allow for new similarity and multimodal tissue models. These will be key in the detection and diagnosis of breast cancer
- Building in adaptability that allows for the integration of other sources of knowledge such as tumour models, genetic data, genotype, phenotype and standardised imaging.

The exact diagnosis of suspicious breast tissue is ambiguous in many cases. HAMAM will resolve this using the statistical knowledge extracted from the large case database. The clinical workstation will suggest additional image modalities that may be captured to optimally resolve these uncertainties. The workstation thus guides the clinician in establishing a patient specific optimal diagnosis. This ultimately leads to a more specific and individual diagnosis.

For more information on HAMAM click HERE