Cancer Big Data
Through the analysis of large, complex data sets, we can improve the patient experience by predicting and detecting cancer earlier, developing new treatments or optimising existing ones.
Interrogation of the vast amount of data about patients and their tumours can yield remarkable insights that enable clinicians to detect cancer earlier and treat it better. However, as the volume and complexity of data generated in clinics and laboratories grows, the successful integration of data science is becoming the limiting factor in delivering high-impact cancer research. By coordinating research through this theme, Oxford Cancer seeks to revolutionise the scale and efficiency of cancer research, by applying the next generation of cutting-edge data science into the clinical settings which have the greatest chance of transforming patient care.
Find out more about the challenges Oxford cancer researchers are addressing in this theme below.
Big Data News
What are the challenges we are tackling?
1. Making the most out of health data
Health data is information concerning an individual’s healthcare record, environment and lifestyle. Gathering, linking, and analysing these datasets allows researchers to tailor cancer care. This is done by identifying factors that correlate with cancer diagnosis, treatment response and patient outcome and using it to inform national public health policy, screening programmes and treatment selection.
Oxford is uniquely placed to utilise new and existing large health data sets due to our breadth of research expertise that spans the fields of Electronic Health Record Epidemiology, Risk Stratification and Primary Care.
The greatest barrier preventing these researchers from delivering better care for patients is that heath data is often siloed and analysed independent of complementary biological programmes. Oxford Cancer’s vision for fully leveraging this expertise is to provide both the capacity to integrate comprehensive healthcare records with existing molecularly defined cohorts, early detection, and immune-oncology programmes; as well as to empower patient driven research.
2. Making the integration of computational biology in cancer research routine
The growth in the scale and complexity of molecular and clinical data that is emerging from cancer studies provides opportunities to apply mathematical modelling and simulations, which can help to extract mechanistic information and guide future studies.
Oxford is uniquely placed to trigger an exponential growth in the scale and impact of this approach to discovery research, utilising researcher expertise in the fields of mathematical biology and molecular informatics.
As with health data, the greatest barrier that is impacting our understanding of cancer and ability to deliver better care for patients, is that computational biology is often siloed and carried out independent of complementary biology programmes, and rarely used to inform future studies in a timely manner. Oxford Cancer’s vision for fully leveraging this expertise is to provide new opportunities for convening and support multidisciplinary groups informed by the best science, supported by the latest technical solutions and iteratively interpreted with accurate cutting edge mathematical models.
3. Applying computer vision to medical imaging
Images play a critical role in our ability to understand, detect and treat cancer. This can be through radiologists inspecting clinical images, pathologists reviewing tissue morphology and architecture, gastroenterologists carrying out endoscopic procedures, or laboratory researchers carrying out a range of spatio-molecular analyses.
However, as research and diagnostic technologies develop, our ability to capture complex imaging data has outpaced our ability to extract meaningful mechanistic or diagnostic information. Oxford’s computer vision research community is ideally placed to address this growing gap.
Oxford Cancer’s ambition is to ensure that cancer images are widely available for the City’s computer vision experts to apply their cutting-edge approaches, and that the fruits of these analyses are translated into the clinic and onward research. To achieve this, we provide an electronic research environment that enables the capture and sharing of data across the community as well as the opportunity to clinically test the outcomes of this research.