HAMAM - Highly Accurate Breast Cancer Diagnosis

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HAMAM - Highly Accurate Breast Cancer Diagnosis

Web address of the case:

http://www.hamam-project.org

Country of the case:

Austria

City/region:

Vienna

Breast cancer diagnosis | Imaging modalities | modelling

Posting Date: 26 November 2009
Last Edited Date: 26 November 2009

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Author:

Sonja Guttenbrunner (EIBIR)Austria

Sonja Guttenbrunner

Type of initiative

Project or service

Case Abstract

HAMAM-Highly Accurate Breast Cancer Diagnosis through Integration of Biological Knowledge, Novel Imaging Modalities, and Modeling-is a three year project that started in September 2008 and consists of 9 international scientific partners from seven countries, with European Institute for Biomedical Imaging Research (EIBIR) as coordinating partner.

Despite tremendous advances in modern imaging technology, both early detection and accurate diagnosis of breast cancer are still unresolved challenges. Unnecessary biopsies are taken and tumours frequently go undetected until a stage where therapy is costly or unsuccessful. The EU funded project HAMAM tackles 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 multi-disciplinary database, populated within the scope of this project, suspicious breast tissue will be characterised and classified.

Spesifically, the HAMAM project will:

Develop tools needed for integrated visualization and analysis of databases from different imaging modalitities, most notably X-ray mammography, DCE MRI, 2D/3D ultrasound, and positron emission mammography (PEM).
Provide proper pre-processing and standardisation tools that will allow for optimal comparison of disparate data.
Develop spatial correlation methods to allow for improved multimodal workflows with respect to reading efficiency and security, eventually leading to combined, multi-modal tissue and lesion models.
Derive advanced schemes for computer aided detection and diagnosis based on multi-modal data that will be key in improving the accuracy in identifying breast cancer.
Build in adaptability that allows for the integration of other sources of knowledge such as biophysical tumour models, known risk factors including family history of cancer, hormonal and environmental factors, and genetic data;
Build a teaching file to be used to train clinicians in actually using the technologies and knowledge acquired in the project .

With HAMAM, Europe ahs the potential to strengthen its leadership in the whole area of image-based breast cancer diagnoses.

Project Size and Implementation

Type of initiative

Inclusive services of general interest

Overall Implementation approach

Partnerships between administration and/or private sector and/or non-profit sector

Technology choice

Not applicable/not available

Funding source

Public funding EU

Implementation and Management Approach

HAMAM is structured around nine different work packages, leading from clinical and diagnostic requirements (WP1) to project management (WP9). HAMAM integrates excellent European centres for imaging science with strong skills on breast imaging. To ensure the clinical impact, leading European clinicians in the area of breast cancer diagnosis are contributing as members of the clinical advisory board.

Technology solution

The translation of the final results into medical products and patient benefit is guaranteed through the industrial partner MeVis Medical Solutions AG (MMS) with outstanding experience and performance in translating research projects into successful products in the field of breast cancer diagnosis.

Impact, innovation and results

Impact

After successful completion of HAMAM, the developed prototype workstation will implement the final multimodal workflow and use case as defined through interactions with the clinical advisory board, all developed and relevant modality-specific and inter-modality algorithms will be available in this workstation, which will be delivered to clinical and scientific partners, new diagnostic parameters will be derived from imaging and multi-disciplinary data, and new insight will be achieved in the relationship between various factors affecting the risk to develop breast cancer and the multi-modality data will be made possible more efficiently, and the assessment od suspicious areas will be more sensitive and more reliable. Potentially, new biomarkers will play a role in clinical decision making and patient care.

The project results will be used scientifically by the technical and clinical partners in various directions. Most importantly, the workstation will be a technological basis for subsequent clinical and methodological studies and the database setup will offer a framework for additional multi-disciplinary research efforts in the field of biomedical imaging. The workstation will most probably in total or in parts be further developed and marketed as cutting-edge software product to support multi-modal breast imaging.

Lessons learnt

HAMAM has successfully completed the first project year that already brought the first major achievements:

a first functioning online database prototype was developed and made accessible for storing multi-disciplinary data from all partners and
the first workstation prototype was implemented.

Furthermore, the clinical goals of the project were discussed and defined in detail, modality specific tasks were described, the scope of tumour modeling and risk analysis integration was refined and delineated, the clinical workflows and indications were clarified and a first complete description of all relevant use cases was delivered.

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