CLinical Evaluation Of a comPuter Algorithm To Report BreAst cAncers (CLEOPATRAA)

Breast Cancer

Digital Pathology

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.

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.

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