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Trial Title:
CLinical Evaluation Of a comPuter Algorithm To Report BreAst cAncers (CLEOPATRAA)
NCT ID:
NCT06110845
Condition:
Breast Cancer
Conditions: Official terms:
Breast Neoplasms
Conditions: Keywords:
Digital Pathology
Study type:
Observational
Overall status:
Not yet recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Device
Intervention name:
QPlasia OncoReader Breast (QPORB)
Description:
Automated diagnostic algorithm
Summary:
The goal of this non-interventionist observational study is to test the performance of a
computer algorithm (QPORB) which examines breast cancer biopsy digital images to provide
diagnostic support. The main question[s] it aims to answer are:
1. The principal research aim is to determine whether 4D Path's Technology Q-Plasia
OncoReader Breast, that has been developed in the research setting, works robustly
in the clinical environment (i.e. to define its real-life clinical utility) in terms
of breast carcinoma grading and molecular subtyping
2. The secondary research aim is to perform an economic analysis alongside the trial in
order to establish the time, resource and cost savings that the technology could
afford the NHS, the ideal price point for engaging with the technology and the
cost:benefit ratio for evaluation by the National Institute for Clinical Excellence
3. The tertiary research aim is to gather long-term follow-up data to better understand
long-term response to therapy and prognosis and potential future uses of the
algorithm
Participants's specimens will be tested alongside routine clinical workflows without
intervention or consent. Researchers will compare the algorithm's results to those of
routine diagnostic standard of care workflows.
Detailed description:
This study follows on from work conducted over the last 4 years focusing on developing
computer-aided diagnostic algorithms in histopathology, with a focus on the detection and
typing of breast cancer. The algorithm extracts information from the same digital image
of a breast biopsy glass slide as used by pathologists to make a diagnosis. The algorithm
can identify the presence of invasive cancer, grade it (i.e. how aggressive it looks) and
determine its molecular subtype, all from the same image and in a single step. Whilst a
pathologist can identify invasive cancer and grade it on a biopsy specimen, grading is
inconsistent across reporting clinicians (i.e. prone to inter-observer variability) and
only about 80% aligned with the 'true' grade of the tumour obtained at surgery. Moreover,
additional time-consuming and expensive laboratory tests are required for molecular
subtyping. They are however essential for informing oncologists of the most
effective/targeted therapy for the patient in each case. The aim of this study is to
perform a final real-life acid test of the technology in the clinical environment
separately but in parallel with routine diagnostic services. If successful, the
technology will have acquired a sufficient body of data to convince end-users and
regulatory bodies of its merit and will effectively make it ready for deployment in the
clinical environment. In that domain, this diagnostic solution health economics
assessment will run alongside the clinical evaluation of the algorithm to analyse and
compare cost and time-related factors between the routine clinical service and the
algorithm workflow. This will provide further information required by regulatory bodies
to aid the consideration of utilising the technology within current health systems.
Furthermore, follow-up data will be collected at 5 and 10 years to investigate patient
prognosis and treatment response as well as future potential applications of the
technology.
Criteria for eligibility:
Study pop:
Patients biopsied for breast cancer
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- all patients with a breast biopsy specimen processed at the pathology laboratory at
SJUH, LTHT.
Exclusion Criteria:
- Age <16 or >110 years, non-carcinoma malignancies (e.g., sarcomas, malignant
phyllodes tumours), carcinomas not arising from within the breast (e.g., cutaneous
malignancies).
Gender:
All
Minimum age:
16 Years
Maximum age:
110 Years
Locations:
Facility:
Name:
St James's University Hospital, Leeds Teaching Hospitals Trust
Address:
City:
Leeds
Zip:
LS97TF
Country:
United Kingdom
Contact:
Last name:
Nicolas M Orsi, BSc MBChB PhD
Phone:
+447903389824
Email:
n.m.orsi@leeds.ac.uk
Contact backup:
Last name:
Elizabeth M Walsh, BSc MBChB
Email:
e.m.walsh@leeds.ac.uk
Start date:
December 1, 2023
Completion date:
November 30, 2034
Lead sponsor:
Agency:
The Leeds Teaching Hospitals NHS Trust
Agency class:
Other
Collaborator:
Agency:
University of Leeds
Agency class:
Other
Collaborator:
Agency:
4DPath
Agency class:
Other
Source:
The Leeds Teaching Hospitals NHS Trust
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06110845
