Improving breast cancer detection by integrating medical images, models
VIENNA, AUSTRIA -- Funded by the European Union with three million Euros, the project HAMAM on Highly Accurate Breast Cancer Diagnosis through Integration of Biological Knowledge, Novel Imaging Modalities, and Modeling has launched as of September 2008. This three-year project aims to seamlessly integrate the available multi-modal images and patient information on a single clinical workstation.
VIENNA, AUSTRIA -- Funded by the European Union with three million Euros, the project HAMAM on Highly Accurate Breast Cancer Diagnosis through Integration of Biological Knowledge, Novel Imaging Modalities, and Modeling has launched as of September 2008. This three-year project aims to seamlessly integrate the available multi-modal imagery and patient information on a single clinical workstation.
The EIBIR's (European Institute for Biomedical Imaging Research) Image Analysis Platform proposed the project in response to the EU's 7th Framework Programme ICT call. Based on knowledge gained from a large multi-disciplinary database, the project will characterize and classify suspicious breast tissue. To accomplish this, HAMAM plans to:
- Build the tools needed to integrate datasets/modalities into a single interface.
- Provide pre-processing/standardization tools that will allow for optimal comparison of disparate data
- Build spatial correlation methodology to allow for anatomical cross-linking between modalities and examinations to enhance both multimodal reading and analysis.
- Build in adaptability that allows for the integration of other sources of knowledge such as tumour models, genetic data, genotype, phenotype and standardized imaging.
According to the EIBIR, early detection and accurate diagnosis of breast cancer are unresolved challenges despite tremendous advances in modern imaging technology. A variety of imaging modalities and image-guided biopsy procedures exist -- but 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 tumors frequently go undetected until a stage where therapy is costly or unsuccessful.
HAMAM will make a leap forward by using statistical knowledge extracted from the large case database. A goal of the project is to compare and evaluate imaging protocols for specific clinical situations. The workstation thus guides the clinician in establishing an optimal patient-specific imaging procedure. This ultimately leads to a more specific and sensitive individual diagnosis.
Project partners include:
- European Institute for Biomedical Imaging Research (AT)
- University College London (UK)
- MEVIS Research (DE)
- MEVIS Medical Solutions (DE)
- Swiss Federal Institute of Technology (CH)
- Radboud University Medical Centre (NL)
- University of Dundee (UK)
- Charité Medical University Berlin (DE)
- Boca Raton Community Hospital (USA)
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