The Computational Cancer Microstructure Imaging group focuses on developing computational methods for non-invasive quantification of tumour microstructure and their translation into the clinical pathway.
Currently cancer diagnosis and therapy treatment depends on traditional histology, which is the only way to obtain sufficiently specific information about the cells that make up the tissue to distinguish different cancer types and grades and thus inform the correct treatment. However, histology is an uncomfortable procedure where tissue is extracted with a biopsy needle. It can have permanent and debilitating side effects. Furthermore, because it targets only a small area, it can often miss tumour regions and need to be repeated multiple times. MRI has major potential advantages: i) it operates in vivo, so can be performed on a living organism allowing for both anatomical and functional information, ii) it is non-invasive; completely non-destructive procedure unlike histology, iii) it is innocuous, as it does not require ionising radiation, and iv) allows a non-localised view of the whole organ or region of interest, avoiding false negatives to which biopsy is prone. However current MRI techniques lack discriminatory power.
Our vision is early, accurate and personalised treatment decisions in cancer without the need for uncomfortable and risky biopsies. We use computational modelling, machine learning and design leading-edge MRI methods to establish new non-invasive biomarkers that discern morphological tumour heterogeneity. The long-term goal is construction of the next generation of clinical diagnostic tools to replace unnecessary biopsies as the primary diagnostic procedure.
UCL Ccami started with an EPSRC-funded fellowship (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/N021967/1) and has studenships from EPSRC CDT i4health (https://www.ucl.ac.uk/intelligent-imaging-healthcare/epsrc-centre-doctoral-training-intelligent-integrated-imaging-healthcare-i4health).
We are located at the Centre for Medical Image Computing (https://www.ucl.ac.uk/medical-image-computing/centre-medical-image-computing-cmic).