Month: November 2020

An Introduction

An Introduction to Computational Oncology

…..and – why a Blog?


These starter posts are always hard, so…..this blog is the companion to the lab website. But that is static, and tells you who is who, and what papers we have published, but doesn’t let you talk about the work we are doing now, or the software we build that doesn’t get “published” (but is released), or the stuff that never get published, or the stuff that is published but is complicated and would benefit from some explanation.

Hence a blog

It is also a way to force us all to write something, more regularly than just papers and grant submissions. I am a great believer that writing IS research: the process of writing involves shaping your thoughts, deciding what you think is true from you have evidence for, deciding how to structure an argument, and working out how to present that to an audience. I think that sounds like research.

It is also a good way to let others know what else we are doing; across Imperial, and more widely. It can be difficult from reading just published work to decide what people are really interested in. It also gives an opportunity for everyone in the lab to contribute – not just to writing, but also reviewing, editing and disseminating that work.

It also lets us rehost some older content that appeared on the (currently defunct) Computational Medicine blog that I ran with Caroline Morton: some of that content is still available, but before it vanishes, we will repost it here (who can fail to be interested in my take on why Jane Austen has some lessons for how we think about the deployment of AI-based systems in healthcare). Which leads us to the question of what Computational Oncology is.

Broadly, we define it as the mathematical and computational approaches to clinical problems in cancer. Our basic unit of analysis is the patient, rather than genetic data (as in bioinformatics) or populations (as in epidemiology), but we look to apply computational approaches. We do this because computational approaches let us do things we can’t normally do: it lets us scale up, it lets us collect things we can’t normally collect, it lets us monitor in a way that we can’t normally do, and extract previously hidden information from routinely acquired data.

This is a vague definition, and is as much a set of things it isn’t as what it is, but it has coherence, and we are beginning to see how our projects link together, linking work on automated imaging interpretation with patients who are enrolled in a clinical trial (more of this later). It also misses some of the most important things about our work: the focus on patient-centred work, the Patient & Public Involvement work we do, and the fact that a lot of the work is based around brain tumours.

We will update this every couple of weeks, as people post about updates of their work, and if you are interested, feel free to drop us a line: matthew.williams -at-