Artificial intelligence in diagnostic prostate MRI to improve outcomes

There has been increasing interest in applying computational methods in medicine, to make sense of cancer’s ‘big data’ problem by exploiting recent advances in data-processing and machine learning to capture and integrate clinical, genomic, and image data collated from hundreds of cancer patients in real-time. Such methods can be applied to digital clinical images to extract image information about patterns of pixels that are not perceivable to the human eye, allowing characterisation of tumour.  Prostate cancer is the 2nd commonest male cancer worldwide, and MRI is the diagnostic tool of choice, however, MRI can miss 10% of significant tumours and leads to unnecessary (invasive) biopsy in around 1/3rd patients who do not have cancer.  

We will use a prototype AI system (Pi) developed with Lucida Medical on retrospective data, in a prospective clinical study. We plan to link histological data to imaging features derived from MRI (including texture analysis) to identify predictors of lesion aggressiveness and need for sampling, using biopsy cores and surgical specimens from the prospective cohort. Further work will link biopsy tissue to MRI data to identify radiogenomic markers of disease aggressiveness. The project presents an opportunity for AI to answer key clinical questions at the intersection of interpretation, imaging and biopsy.  

The project will involve working with an established interdisciplinary programme of researchers and help in the assessment of cross-cutting “multi-omic” approaches to cancer assessment, involving integration of advanced image analysis, transcriptomic, genomic, tissue, and patient outcomes to inform the design of diagnostic strategies.