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)


Related news article:

Most Popular Now

NHS Staff Punished as 500,000 Rely on Wh…

WhatsApp, Facebook Messenger and other unauthorised instant messaging (IM) apps are being used by approximately 500,000 NHS staff at work, as a growing number turn to consumer tools to communicate...

Call for Abstracts: European Telemedicin…

27 - 29 May 2018, Sitges, Barcelona, Spain. The European Telemedicine Conference 2018 (ETC18) is an interdisciplinary forum for healthcare professionals, directors, managers, and researchers with the intent of bringing together...

conhIT 2018: The stage is Set for Dialog…

17 - 19 April 2018, Berlin, Germany. Finding out about and supporting all aspects of the digital transformation of the healthcare system: that is what this year's conhIT, Europe's largest event...

Smartphone 'Scores' can Help Doctors Tra…

Parkinson's disease, a progressive brain disorder, is often tough to treat effectively because symptoms, such as tremors and walking difficulties, can vary dramatically over a period of days, or even...

Portable Device Detects Severe Stroke in…

A new device worn like a visor can detect emergent large-vessel occlusion in patients with suspected stroke with 92 percent accuracy, report clinical investigators at the Medical University of South...

Focus on the Digital Transformation - A …

17 - 19 April 2018, Berlin, Germany. How is the digitalisation of the healthcare system affecting the relationship between patients and doctors? What new innovations and solutions does the health IT...

Imitation is the Most Sincere Form of Fl…

For every two mobile apps released, one is a clone of an existing app. However, new research published in the INFORMS journal Information Systems Research shows the success of the...

Blackpool Teaching Hospitals Triggers Di…

Blackpool Teaching Hospitals NHS Foundation Trust has laid the foundations for an ambitious digitisation programme, by deploying IMS MAXIMS technology in its emergency department. The go-live is already helping staff...

Merck Partners with Medisafe to Help Imp…

Merck, a leading science and technology company, today announced a new collaboration with US-based Medisafe to help its cardiometabolic patients better manage medication intake and adhere to prescribed treatment regimens...

Philips Research-led Big Data Consortium…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, together with its consortium partners, today announced that it has received funding from the EU's Horizon 2020 program...

Smartphone App Performs Better than Trad…

A smartphone application using the phone's camera function performed better than traditional physical examination to assess blood flow in a wrist artery for patients undergoing coronary angiography, according to a...