Testing the MitProfiler tool for detecting and counting dividing cells in stained tissue samples

ISRCTN ISRCTN16858627
DOI https://doi.org/10.1186/ISRCTN16858627
IRAS number 342246
Secondary identifying numbers MitPro Study Protocol 2.4
Submission date
13/08/2025
Registration date
19/09/2025
Last edited
19/09/2025
Recruitment status
No longer recruiting
Overall study status
Ongoing
Condition category
Cancer
Prospectively registered
Protocol
Statistical analysis plan
Results
Individual participant data
Record updated in last year

Plain English summary of protocol

Background and study aims
MitProfiler is a computer algorithm which uses machine learning, a form of artificial intelligence, to train computers to recognise mitotic figures in pathology samples (that is, samples of human or animal tissue prepared for microscopic examination). Mitotic figures are seen in cells which are dividing. They are the chromosomes in the cells which can be seen preparing to divide and separating into the daughter cells as the cells divide. They are an important feature of normal tissue biology (e.g. in growing regenerating tissue) and diseases such as cancer (where cells grow and divide in an uncontrolled manner). Pathologists see and count mitotic figures routinely in their work. Having the computer do this task saves time, reduces fatigue in pathologists and should deliver more consistent results as observations between individual human pathologists vary to some extent. By getting human pathologists and the MitProfiler algorithm to count mitoses in the same samples, this study aims to measure how the MitProfiler algorithm performs in comparison to human pathologists.

Who can participate?
Patients with haematoxylin and eosin-stained tissue sections from various tumour types

What does the study involve?
This study involves comparing the human pathologists' counts of mitotic figures in stained tissue sections with an automated mitotic counting algorithm.

What are the possible benefits and risks of participating?
If the results are as good or better than human pathologists, the data derived will be used to support the regulatory approval of the device through the Medicines and Healthcare Regulatory Authority.

Where is the study run from?
Histofy Ltd (UK)

When is the study starting and how long is it expected to run for?
February 2025 to September 2025

Who is funding the study?
Innovate UK

Who is the main contact?
Prof. David Snead, david.snead@uhcw.nhs.uk

Study website

Contact information

Prof David Snead
Public, Scientific, Principal Investigator

Histofy
Venture centre
Sir William Lyons Rd
West Midlands
CV4 7AL
United Kingdom

ORCiD logoORCID ID 0000-0002-0766-9650
Phone +44 (0)2476968649
Email d.snead@histofy.ai

Study information

Study designObservational cohort study
Primary study designObservational
Secondary study designCohort study
Study setting(s)Laboratory
Study typeDiagnostic
Scientific titleValidation of the MitProfiler algorithm for the detection and quantification of mitotic figures in haematoxylin and eosin-stained tissue sections
Study acronymMitPro validation study
Study objectivesMitProfiler is a computer algorithm which uses machine learning, a form of artificial intelligence, to train computers to recognise mitotic figures in pathology samples (that is, samples of human or animal tissue prepared for microscopic examination). Mitotic figures are seen in cells which are dividing. They are the chromosomes in the cells which can be seen preparing to divide and separating into the daughter cells as the cells actually divide. They are an important feature of normal tissue biology (e.g. in growing regenerating tissue) and diseases such as cancer (where cells grow and divide in an uncontrolled manner). Pathologists see and count mitotic figures routinely in their work. Having the computer do this task saves time, reduces fatigue in pathologists and should deliver more consistent results as observations between individual human pathologists vary to some extent. By getting human pathologists and the MitProfiler algorithm to count mitoses in the same samples, this study aims to measure how closely the MitProfiler algorithm performs in comparison to human pathologists. If the results are as good or better than human pathologists, the data derived will be used to support the regulatory approval of the device through the Medicines and Healthcare Regulatory Authority.
Ethics approval(s)

Approved 20/02/2025, London - Fulham Research Ethics Committee (2 Redman Place, Stratford, London, E20 1JQ, United Kingdom; +44 (0)207 104 8084, (0)207 104 8286, (0)207 104 8109; fulham.rec@hra.nhs.uk), ref: 25/LO/0158

Health condition(s) or problem(s) studiedSarcoma, melanoma, carcinoma, carcinoid
InterventionThis observational study involves comparing the blinded counts of mitotic figures in archived hematoxylin and eosin (H&E) sections with an automated mitotic counting algorithm.
Intervention typeDevice
Pharmaceutical study type(s)Not Applicable
PhaseNot Applicable
Drug / device / biological / vaccine name(s)MitPro
Primary outcome measureInterobserver variation of mitotic counts measured using data generated by the MitProfiler algorithm and human pathologists at one timepoint
Secondary outcome measuresTime taken to produce mitotic counts measured using data generated by the MitProfiler algorithm and human pathologists at one timepoint
Overall study start date20/02/2025
Completion date30/09/2025

Eligibility

Participant type(s)Patient
Age groupMixed
Lower age limit1 Year
Upper age limit100 Years
SexBoth
Target number of participants500
Key inclusion criteriaPatients with haematoxylin and eosin-stained tissue sections from tumour types:
1. Breast cancer
2. Neuroendocrine tumour
3. Melanoma (including uveal melanoma)
4. Soft tissue sarcoma
5. Leiomyoma
6. Lung cancer
7. Glioblastoma
8. Meningioma
9. Thyroid cancer
10. Retinoblastoma
Key exclusion criteriaHaematoxylin and eosin-stained tissue section slides are unavailable
Date of first enrolment13/08/2025
Date of final enrolment20/09/2025

Locations

Countries of recruitment

  • England
  • United Kingdom

Study participating centre

North Tees and Hartlepool NHS Foundation Trust
University Hospital of Hartlepool
Holdforth Road
Hartlepool
TS24 9AH
United Kingdom

Sponsor information

Funders

Funder type

Government

Innovate UK
Government organisation / National government
Alternative name(s)
innovateuk
Location
United Kingdom

Results and Publications

Intention to publish date31/10/2025
Individual participant data (IPD) Intention to shareNo
IPD sharing plan summaryData sharing statement to be made available at a later date
Publication and dissemination planPlanned publication in a peer-reviewed journal
IPD sharing planThe data sharing plans for the current study are unknown and will be made available at a later date

Study outputs

Output type Details Date created Date added Peer reviewed? Patient-facing?
Protocol file version 2.4 21/08/2025 No No

Additional files

47837_PROTOCOL_V2.4.pdf

Editorial Notes

13/08/2025: Study's existence confirmed by Health Research Authority (HRA) (UK).