cancer stage
Improved ways to access and analyse stored medical data for the staging of lung cancer patients
Much of the current global information explosion is due to the emergence of new digital technologies for acquiring and storing medical data. This is especially true in the health domain; in particular, new digital medical imaging systems are emerging with increasing resolution and new enterprise-wide information systems are accumulating vast amounts of both structured and unstructured medical data. While the richness of this data brings a range of potential benefits to the health system, often these can be overwhelmed by the burden of processing such sheer volumes of data.
Multimedia Content Analysis (MCA)
MCA is a field of research that facilitates the management and interpretation of large amounts of multimedia data, encompassing text, image, audio and video, as well as traditional structured databases. The field draws on diverse research areas, including sensor technologies, signal processing, pattern recognition, machine learning, information retrieval and human computer interaction.
Research Focus
The CSIS project builds upon a core research capability in multimedia content analysis within the health domain, focusing on a particular application: support systems for cancer management, both for individual patients and population-level analyses.
The cancer "stage" is a categorisation of its progression in the body, in terms of the extent of the primary tumour and any spreading to local or distant body sites. While staging has a fundamental role in cancer management, due to the expertise and time required and the multi-disciplinary nature of the task, cancer patients are not always routinely staged. By automating the collation, analysis, summarisation and classification of relevant patient data, the reliance on expert clinical staff can be lessened, improving the efficiency and availability of cancer staging.
Initial work will investigate the summarisation and categorisation of patient reports to assist with staging lung cancer. Longer term research will investigate extending this in three ways:
§                                 Extensions to handle other data and cancer types. Initial work is focusing on staging lung cancer using text reports radiology, histology), however opportunities exist to extend this to bowel and other cancers, and also to use information extracted from other forms of data, for example, radiological images.
§                                 Classifying cancer characteristics other than stage. The techniques used to classify cancer stage may be extended to other tasks, such as filtering of patient data, for example, screening for cancer / non-cancer, or classification of cancer types.
§                                 Population-level analyses. Statistical models may be used to identify trends and anomalies in cancer patient demographics or treatment / response characteristics, based on metadata extracted through the automatic content analysis techniques (for example, cancer type, cancer stage, etc). The CSIS project will produce engineered software prototypes and evaluate these in appropriate proof-of-concept and clinical user trials.