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

Open Call HORIZON-HLTH-2021-TOOL-06-03: …

This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination 5 "Unlocking the full potential of new tools, technologies and digital...

CSAM Acquires Optima - Further Boosts eH…

CSAM Health Group AS ("CSAM"), the leading provider of niche eHealth solutions in the Nordics, today announced it has signed a deal to acquire eHealth player Optima Corporation Ltd from...

Novartis and Hewlett Packard Enterprise …

Novartis and Hewlett Packard Enterprise (HPE)announced a collaboration that aims to accelerate the use of data and digital technologies within Novartis efforts to reimagine global health and improve access to...

COVID-19 Origins Still a Mystery

Scientists using computer modelling to study SARS-CoV-2, the virus that caused the COVID-19 pandemic, have discovered the virus is most ideally adapted to infect human cells - rather than bat...

Using Artificial Intelligence to Overcom…

Depression is a worldwide problem, with serious consequences for individual health and the economy, and rapid and effective screening tools are thus urgently needed to counteract its increasing prevalence. Now...

Deep Machine Learning Completes Informat…

The Structural Bioinformatics and Network Biology laboratory, led by ICREA Researcher Dr. Patrick Aloy, has completed the bioactivity information for a million molecules using deep machine-learning computational models. It has...

EU Digital COVID Certificate Enters into…

The EU Digital COVID Certificate Regulation enters into application. This means that EU citizens and residents will now be able to have their Digital COVID Certificates issued and verified across...

Philips and the Spanish National Center …

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, has participated in an important research project to develop a magnetic resonance (MR) imaging technique [1,2] that could...

Scottish Health and Care Professionals G…

Scottish health and care professionals across a wide range of clinical settings including NHS Scotland health boards are being given access to an individual's COVID-19 vaccine status through the Orion...

Ethics and Governance of Artificial Inte…

Artificial Intelligence (AI) refers to the ability of algorithms encoded in technology to learn from data so that they can perform automated tasks without every step in the process having...

It's Going to be Quite a Handover…

Health and social care secretary Matt Hancock has been abruptly replaced by Sajid Javid. The Highland Marketing advisory board consider the huge agenda he is now facing, and what it...

Philips Accelerates Stroke Diagnosis and…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, announced a strategic partnership agreement with NICO.LAB, a MedTech stroke care company. Together with the recently expanded stroke...