Fast-track cancer diagnosis

Histopathology has traditionally been the method of choice for the diagnosis and grading of different types of cancer. However, it is a labour-intensive and low-throughput technique that relies on the subjective annotation of numerous tissue slides.

Faster alternatives are therefore needed and are emerging in the form of digital image analyses. A combination of manual and digital annotations has led to the creation of image libraries in various academic and industrial sectors around the globe.

To fully exploit the speed of digital analysis in cancer diagnosis, there is an urgent requirement to streamline the process and to provide a comprehensive set of tools for data integration and mining. High-performance computing tools should enable the integration of multiple libraries and exploitation of existing knowledge.

In this context, the EU-funded FAST-PATH project will develop tools to enable digital pathology workflow. Using a web-based interface, the consortium will integrate digital pathology resources and related manual annotations.

Currently, the criteria for discriminating between potentially lethal tumours and non-aggressive cancer are not very clear. Although the Gleason grading system is used as an indication of cancer aggressiveness, it is a time-consuming process. Detection of the prostate-specific antigen has revolutionised prostate cancer screening, but is now associated with over-treatment.

To address these shortcomings and improve and speed up prostate cancer diagnosis, FAST-PATH will use multiplex immunohistochemistry biomarkers. In close collaboration with industrial partners, researchers have set out to develop and validate novel prostate biomarkers, alongside computational solutions for the fast and quantitative analysis of immunohistochemistry data.

The proposed automated and high-performance technologies focus on key aspects of prostate cancer pathology. Combining them under the FAST-PATH workflow should considerably improve the accuracy of prostate diagnosis, avoid unnecessary treatments and decrease cancer-related mortality.