Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Research Protocols

Date Submitted: Apr 30, 2025
Date Accepted: Nov 30, 2025

The final, peer-reviewed published version of this preprint can be found here:

Deep-Learning Solution Providing Molecular Marker Subtyping of Breast Cancer Whole Slide Images: Protocol for a UK Clinical Service Evaluation Study

Walsh E, Rathore D, Geraldes A, Arlan S, Hanby AM, Millican-Slater R, Drummond M, Rotimi O, Kumar N, Pandya P, Orsi NM

Deep-Learning Solution Providing Molecular Marker Subtyping of Breast Cancer Whole Slide Images: Protocol for a UK Clinical Service Evaluation Study

JMIR Res Protoc 2026;15:e76785

DOI: 10.2196/76785

PMID: 42302265

Deep-learning solution providing molecular marker subtyping of breast cancer whole-slide images: Protocol for a UK clinical service evaluation study

  • Elizabeth Walsh; 
  • Dildar Rathore; 
  • André Geraldes; 
  • Salim Arlan; 
  • Andrew M. Hanby; 
  • Rebecca Millican-Slater; 
  • Michael Drummond; 
  • Olorunda Rotimi; 
  • Narender Kumar; 
  • Pahini Pandya; 
  • Nicolas M. Orsi

ABSTRACT

Background:

As the histopathology workforce continues to struggle and service demand continues to increase, it has become prudent to consider viable avenues to try and alleviate diagnostic workload burden. One such avenue is computer-based technologies (CBTs). Breast cancer (BC) is the most common malignancy in the UK and requires additional testing for oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) status at the time of histological diagnosis. This makes BC diagnostics a promising candidate for the application of an efficient CBT. However, for clinical acceptance, these technologies must prove that they work within a real-life diagnostic environment.

Objective:

We present a study protocol for a prospective clinical service evaluation aimed to validate a UKCA-marked CBT’s ability to provide ER/PR/HER2 results for invasive BCs from scanned haematoxylin and eosin-stained whole slide images (WSIs).

Methods:

This protocol has been designed to utilise and mimic a pre-existing digital pathology workflow within an NHS tertiary referral cancer centre without disrupting normal patient care. Eligible cases are identified prospectively through the laboratory information management system and their WSIs are extracted from the clinical digital workflow. These are then analysed by the CBT in a separate environment providing results for ER, PR and HER2. These results are compared to the ER/PR/HER2 status given on the corresponding pathology report. This comparison will form the basis to determine the CBT’s performance.

Results:

There are no results to present.

Conclusions:

This design assesses a CBT within a clinical environment whilst effectively eliminating any unwanted effects on patient care. This type of service evaluation provides a useful step to establish confidence in a CBT before trialling its effect on patient care. It also offers the opportunity to support interventional randomised controlled trials, health economic evaluations and usability studies. This protocol will hopefully prove useful to others who wish to conduct a similar service evaluation at their own institution.


 Citation

Please cite as:

Walsh E, Rathore D, Geraldes A, Arlan S, Hanby AM, Millican-Slater R, Drummond M, Rotimi O, Kumar N, Pandya P, Orsi NM

Deep-Learning Solution Providing Molecular Marker Subtyping of Breast Cancer Whole Slide Images: Protocol for a UK Clinical Service Evaluation Study

JMIR Res Protoc 2026;15:e76785

DOI: 10.2196/76785

PMID: 42302265

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.