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

Orion Health Supports Professional Recor…

Orion Health is supporting the Professional Record Standards Body's partnership scheme by applying to become a 'quality partner'. The company, which is one of the UK’s leading providers of shared care...

FDA Authorizes Software that Can Help Id…

Today, the U.S. Food and Drug Administration authorized marketing of software to assist medical professionals who examine body tissues (pathologists) in the detection of areas that are suspicious for cancer...

Northumbria Healthcare Picks CliniSys to…

Pathologists at one of England's most innovative trusts have chosen the CliniSys laboratory information system (LIMS) as part of a digital strategy to support its drive to continually improve patient...

Study Finds Telemedicine Appointments Re…

Telemedicine appointments combined with in-person visits significantly reduced the risk of further illness for children with medically complex cases, according to results of a new study by researchers with The...

Contact-Tracing Apps could Improve Vacci…

Mathematical modeling of disease spread suggests that herd immunity could be achieved with fewer vaccine doses by using Bluetooth-based contact-tracing apps to identify people who have more exposure to others...

A Computer Algorithm Called 'Eva' May Ha…

A prescriptive computer program developed by the USC Marshall School of Business and Wharton School of Business of the University of Pennsylvania for Greece to identify asymptomatic, infected travelers...

FDA Clears First Major Imaging Device Ad…

Today, the U.S. Food and Drug Administration cleared the first new major technological improvement for Computed Tomography (CT) imaging in nearly a decade. "Computed tomography is an important medical imaging tool...

Using Internet in Retirement Boosts Cogn…

Using the internet during your retirement years can boost your cognitive function, a new study has found. Researchers from Lancaster University Management School, the Norwegian University Science and Technology and...

AI Tool Improves Accuracy of Breast Canc…

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already...

Study Shows Trust is still at Heart of N…

A new study has shown that issues surrounding trust are still at the heart of people's reluctance to download and use the NHS App, particularly among Black, Asian and minority...

Time until Dementia Symptoms Appear can …

Researchers at Washington University School of Medicine in St. Louis have developed an approach to estimating when a person who is likely to develop Alzheimer’s disease, but has no cognitive...