Breast cancer is the most common cancer in women. In the EU and the US, one in eight will develop it at some point in their lives, and it will kill one in 28. But harnessing the power of the grid could help increase the accuracy of diagnoses.
Mammography examinations save thousands of women’s lives every year. However the rate of misdiagnosis can be high – in some instances up to 30%. This is due in part to physical differences across patient populations, differences in equipment and procedures, and difficulty in using computers to help detect changes in breast tissue.
Computer-aided detection of this potentially fatal cancer, especially when used together with the traditional method of visually screening mammograms, can not only shorten the time needed for analysis, but can also help increase the accuracy of diagnoses.
Novel approach to comparative diagnoses
The team in the European IST project MammoGrid, which ended in August 2005, aimed to apply the power of the grid to see if they could more accurately detect breast cancer. The prototype software that resulted is already enabling users – hospitals, doctors, clinicians, radiologists and researchers – to harness the massive capacity of grid computing to run advanced algorithms on digital mammograms, stored Europe-wide.
The project team also developed a geographically distributed, grid-based database of standardised images and associated patient data. Already, there are 30,000 images stored from over 3,000 patients, equally balanced between the University Hospital of Cambridge in the UK and Udine in Italy.
The novelty of the MammoGrid approach lies in the application of grid technologies to medical diagnoses, and in providing the data and tools to enable users to compare new mammograms with existing ones in the grid database. Users can access mammograms from a variety of sources, and also computer-aided detection algorithms to detect micro-calcifications (tiny specks of calcium in the breast that could indicate cancer) and monitor breast density (dense tissue is considered a major risk factor).
Source: IST Results
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