Breast Pathology Database
Research type
Research Database
IRAS ID
256309
Contact name
Emad Rakha
Contact email
Research summary
Breast Pathology Database
REC name
East Midlands - Leicester Central Research Ethics Committee
REC reference
19/EM/0162
Date of REC Opinion
4 Jul 2019
REC opinion
Further Information Favourable Opinion
Data collection arrangements
Data to be stored will include:
1. Digital images derived from stained histological sections of diagnostic pathological material
2. Clinicopathological data derived from patient records
3. Biomarker data derived from pathological material used in research studies
4. Genomic and transcriptomic data from pathological material used in research studiesA data acquisition team will collect the relevant data using NHS Trust computer systems at the Nottingham University Hospitals (NUH) NHS site. They will have full training in accessing local systems within the NHS and will be compliant with the Nottingham University Hospitals (NUH) NHS Trust data protection, confidentiality and disclosure policy. The NHS Trust, quality information governance team, will oversee this work.
The data acquisition team will be responsible for pseudonymisation of the data, prior to its release to the data manager at the University of Nottingham.
A monitoring and auditing system will be in place to oversee confidentiality and ensure protocols are being followed. There will be a release of data audit which is an automated audit by the Trust IT systems and information government team.
All data will be pseudonymised and stored securely on password protected, backed up, University of Nottingham computer systems.Research programme
Digital images are becoming an increasingly important medium for diagnostic histopathology, making the process more efficient and facilitating consultation between practising pathologists. There is pressure to develop artificial intelligence (AI) - guided systems to aid diagnosis by developing objective tools for disease recognition and characterisation. Data is required both to train the AI systems and to test their performance and accuracy. This database, comprising high quality digital images with linked data from a large patient cohort, will be used to: • Train AI systems to recognise disease states and distinguish between them • Validate algorithms derived from the training process • Aid the discovery of novel prognostic / diagnostic biomarkers This database comprises one the largest and unique breast tissue databases in the world which has linked patient follow-up. The addition of high-resolution digital images will increase the usability of the database in terms of bringing together morphological and biological data as well as aiding in the development of AI and other computational approaches to further improve digital pathology and ultimately transform digital pathology practice. Creating a first-class breast disease big database in the UK which will ultimately speed up the process of developing novel prognostic and diagnostic tests for breast cancer patients.
Research database title
Breast Pathology Database
Establishment organisation
University of Nottingham
Establishment organisation address
University Park
Nottingham
NG7 2RD