I lead the Computational Oncology group at Imperial College, based jointly between the Dept. of Computing and Division of Surgery & Cancer.
We are interested in mathematical and computational approaches to clinical problems, including the use of machine learning, computational argumentation and the use of clinical 'big data'. In particular, we are interested in improving access to and use of clinical data, with the aim of developing and applying computational techniques to these data. We also work on using computational techniques to reason with clinical trial data. Our current work covers a range of areas, including novel techniques for systemic reviews and evidence aggregation and mathematical modelling of prognosis in patients receiving palliative chemotherapy. We have begun to collect this work under the banner of computational oncology - see here for more details, and here for details of our recent conference.
I have several local & national positions working with cancer data to understand and improve cancer care in the UK and beyond, including NCRAS and Macmillan.
Clinically, I deliver radiotherapy and chemotherapy to patient with brain and spinal tumours, including IMRT and stereotactic radiotherapy. My clinical work is based at Charing Cross hospital, although I also visit Northwick Park and Mount Vernon hospitals weekly. At Charing Cross we have an excellent multi-disciplinary team of surgeons, radiologists, nurses, oncologists and others who provide diagnostic and treatment services across, and beyond, north-west London, with some of the best brain tumour outcomes in the country.
My clinical research into patterns of care and outcomes in patients with a wide range of tumours, and developing better ways of assessing patient outcomes. This is increasingly focused around brain tumours, and I am the PI on a national patterns of care study in high-grade glioma.
et al., 2018, A Study of the Focal Adhesion Kinase Inhibitor GSK2256098 in Patients with Recurrent Glioblastoma with Evaluation of Tumor Penetration of [11C]GSK2256098., Neuro Oncol
et al., 2018, A methodology to extract outcomes from routine healthcare data for patients with locally advanced non-small cell lung cancer, Bmc Health Services Research, Vol:18, ISSN:1472-6963
et al., 2017, Estimating progression-free survival in patients with glioblastoma using routinely collected data, Journal of Neuro-oncology, Vol:135, ISSN:0167-594X, Pages:621-627
Williams M, Morton CE, 2018, Computational Medicine: Coding for Medics, Elsevier, ISBN:9780702076039
et al., A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks, Conference on Artificial Intelligence in Medicine in Europe, Springer