Histo-MRI map
Research type
Research Study
Full title
Learning MRI and histology image mappings for cancer diagnosis and prognosis
IRAS ID
251440
Contact name
Shonit Punwani
Contact email
Sponsor organisation
Research Governance Manager, Joint Research Office (part of the Research Support Centre)
Clinicaltrials.gov Identifier
Duration of Study in the UK
3 years, 0 months, 0 days
Research summary
This project aims to exploit recent advances in machine learning to address acute problems in cancer management, most directly prostate cancer. We plan to do so by acquiring co-localised MRI and histology data from 50 prospective prostate cancer patients, in order to develop a revolutionary tool for cancer diagnosis and prognosis based solely on MRI, limiting the need for invasive biopsies.
The current standard approach of making treatment decisions via biopsy and histology has two key limitations: it is i) invasive and ii) subjective/inconsistent. We will develop computational tools supporting new solutions that resolve both issues.
Specifically, we aim to enable non-invasive MRI to become the primary diagnostic tool, avoiding a large number of unnecessary biopsies, which carry significant risk of life-changing side-effects, and reserving the procedure for marginal cases. We also plan to relate MR signals to quantitative tissue features to enable more reliable treatment decisions.
We are uniquely positioned to obtain associated MRI and histology images to support a learning and estimation process from MRI to histological features. Such a mapping can provide invaluable new information for clinical decision making via optimally designed MRI protocols.
The project involves a number of engineering challenges:
i) overcoming the intrinsic challenge of aligning images obtained via MRI and histology;
ii) provide easily understandable measures telling end users how certain cancer predictions made by the trained computer programs are;
iii) find features in huge data sets that are relevant for cancer characterisation;
iv) develop strategies that minimse the number of MRI images required by the computer programs for fully non-invasive MRI-based cancer diagnosis.REC name
London - Queen Square Research Ethics Committee
REC reference
19/LO/1803
Date of REC Opinion
23 Jan 2020
REC opinion
Further Information Favourable Opinion