The aim is to develop and translate novel imaging biomarkers and novel computational methods to advance radiogenomics and quantitative imaging. Our approach aims to allow very rapid clinical translation of novel imaging agents, from pre-clinical discoveries to proof-of-principle experimental medicine trials and ultimately routine clinical practice.
New tumour imaging technologies such as hyperpolarised 13C Magnetic Resonance Imaging (MRI) pioneered by Cambridge scientist Professor Kevin Brindle, can detect a biological tumour response to just a single dose of drug. This is in stark contrast to insensitive conventional imaging (e.g., CT, MRI) currently used to assess tumour shrinkage in clinical trials following weeks of drug treatment.
The team’s approach leverages the world-leading expertise and infrastructure at Cambridge including a hyperpolarised-probe pharmacy, a PET/MR attached to a hyperpolariser spin lab, one of only two used clinically in the world to develop novel PET and MR imaging probes, including 18F-labelled C2Am, hyperpolarised C13 fumarate and pyruvate and advanced diffusion weighted magnetic resonance imaging (DW-MRI) methods.
Cell death is an important read out for imaging the early response of tumour to treatment and with our four approaches we are able to image
These methods provide an unprecedented and holistic view of cell death. They are complimentary and all important in optimizing treatment management. Eventually the plan would be to use them in combination which will be defined as we get additional clinical information about their sensitivity and use. Both hyperpolarised [1,4-13C2] fumarate and C2Am are novel imaging probes that we aim to develop to the first-in-man stage of testing to detect early cell death by 2021.
Our long-term goal is the integration of quantitative non-invasive imaging measurements of cell death as an early marker of treatment response together with genomic, ctDNA and other longitudinal measures of the cancer phenotype.
Further information can be found on the Radiogenomics and Quantitative Imaging Group page.
Radiogenomics is a method that extracts a large amount of features from radiographic medical images (CT, MRI, PET…) using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics that fail to be appreciated by the naked eye or that cannot easily be quantified.
The conversion of images to structured data and the resulting quantitative features can be used in mathematical models, often learned, for finding a dependence or inter-relationships between features and a medical question such as nodule malignancy, tumour aggressiveness and prediction of treatment response.
One of the well-known characteristics of cancer is tumour heterogeneity. Hence, small biopsy specimens may not be representative of a whole tumour. Moreover, tumour histology often changes over time. This makes habitat detection a subtle process. Up-to-date habitat detection using radiomic methods can be divided into two categories.
Multi-parametric or multi-modality methods such as T1, T2, Flair MRI imaging or PET/CT imaging provide enough data for the detection of physiologically similar sub-regions (“habitats”) within a nodule or a tumour. Single-modality imaging provides less information, in this case radiomic texture features associated with heterogeneity of a nodule are used.
The Mark Foundation Institute for Integrated Cancer Medicine (MFICM) at the University of Cambridge aims to revolutionise cancer care by affecting patients along their treatment pathway.