HAMAM

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 (Highly accurate breast cancer diagnosis through integration of biological knowledge, novel imaging modalities, and modelling) project 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 further information, please visit:
http://www.hamam-project.eu

Project co-ordinator:
EIBIR gemeinnuetzige GmbH zur Foerderung der. Erforschung der biomedizinischen Bildgebung

Partners:

  • Boca Raton Community Hospital Inc (USA)
  • MeVis Research GmbH (Germany)
  • MeVis Medical Solutions AG (Germany)
  • University College London (United Kingdom)
  • Radboud Universiteit Nijmegen - Stichting Katholieke Universiteit (Netherlands)
  • Charité - Universitätsmedizin Berlin (Germany)
  • The University of Dundee (United Kingdom)
  • Eidgenössische Technische Hochschule Zürich (Switzerland)

Timetable: from 09/2008 - to 08/2011

Total cost: € 4.250.000

EC funding: € 3.100.000

Programme Acronym: FP7-ICT

Subprogramme Area: Virtual physiological human

Contract type: Collaborative project (generic)


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