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

The Future of Medicine is Data

At the 2023 Annual J.P. Morgan Healthcare Conference, Owkin Co-founder and CEO Thomas Clozel, MD will outline how data is the future of medicine - from the development of new...

Study Surveys Landscape of Apps Built on…

A study led by Regenstrief Institute Research Scientist Titus K. Schleyer, DMD, PhD, is among the first to survey the current landscape of FHIR® apps, providing a snapshot of how...

New Computer Program 'Learns' to Identif…

Genetic mutations cause hundreds of unsolved and untreatable disorders. Among them, DNA mutations in a small percentage of cells, called mosaic mutations, are extremely difficult to detect because they exist...

Applications Open for SpinLab Accelerato…

The start-up accelerator supports entrepreneurial and innovative teams that want to grow sustainably and successfully scale their business model. With a strong hands-on mentality and a lot of passion, the...

Bayer to Accelerate Drug Discovery with …

Bayer AG and Google Cloud today announced a collaboration to drive early drug discovery that will apply Google Cloud's Tensor Processing Units (TPUs), which are custom-developed accelerators designed to run...

Allscripts Announces Corporate Name Chan…

Allscripts Healthcare Solutions, Inc. announced that, effective January 1, 2023, it has changed its name to Veradigm Inc. (NASDAQ: MDRX). Allscripts had been transitioning its solutions to the Veradigm brand...

220M€ Investment in Testing and Experime…

To make the EU the place where AI excellence thrives from the lab to the market, the European Union is setting up world-class Testing and Experimentation Facilities (TEFs) for AI. Together...

AI Tool Developed to Predict Risk of Lun…

Lung cancer is the leading cause of cancer death in the United States and around the world. Low-dose chest computed tomography (LDCT) is recommended to screen people between 50 and...

Artificial Nerve Cells - Almost Like Bio…

Researchers at Linköping University (LiU), Sweden, have created an artificial organic neuron that closely mimics the characteristics of biological nerve cells. This artificial neuron can stimulate natural nerves, making it...

Bayer Acquires Blackford Analysis Ltd. B…

Bayer announced the acquisition of the global strategic imaging AI platform and solutions provider Blackford Analysis Ltd. The acquisition is part of Bayer's strategy to drive innovation in radiology, including...

For Shared Decision-Making, Telemedicine…

Telemedicine may be just as effective as in-person visits when it comes to shared decision-making and communication for patients undergoing a first-time surgery consultation, according to a study published as...

Veradigm Announces Strategic Investment …

Veradigm Inc. (NASDAQ: MDRX), formerly Allscripts Healthcare Solutions, Inc., announced an investment in Holmusk, a global behavioral health real-world evidence and data analytics company, as part of Holmusk's $45 million...