Microscopic examination of tissue pieces sampled from a patient's cancer is crucial to providing a correct diagnosis, which will indicate the likely behaviour of that particular cancer and help to decide the best treatment course. Providing such diagnoses is the job of pathologists and, for over 100 years, these diagnoses have been made by interpreting stained glass slides, representing thin sections of the cancer tissue.
In recent decades, there has been a major research focus in the discipline of Digital Pathology, whereby stained glass slides are scanned at high resolution to provide digital images to pathologists for primary reporting. This has now reached the stage of clinical implementation, taking advantage of this cutting edge research, with high performance computers and monitors making the microscope redundant for an increasing number of pathologists. In the Belfast Health and Social Care Trust Department of Cellular Pathology, for example, full migration to digital pathology was implemented in 2021 and we now scan over 1000 slides daily, for primary diagnostic reporting by the large team of consultant pathologists.
Whilst the diagnostic image interpretation process is largely unchanged, ready access to digital images makes it easy to share cases with other pathologists for second opinions where required, and to demonstrate important pathology features to clinical teams at cancer multidisciplinary team meetings, helping inform patient management.
Specific tasks such as measuring tumour size to help determine stage, measuring distances of tumour from surgical margins or assessing additional immunohistochemically stained tissue sections, side by side if necessary, are all performed more accurately and efficiently by digital image compared to glass slide, and incrementally enhance the overall standard of pathology reporting. Finally, digital pathology is the tool which unlocks the potential of applying artificial intelligence (AI) to this aspect of diagnostic healthcare. There is likely to be significant expansion of this field in years to come but already we see specific applications, such as "deep learning" algorithms applied to digital images before pathologist viewing, and drawing their attention to areas of possible concern, notably applied to date in the field of prostate cancer. The medical specialty of pathology is thereby undergoing its biggest and most exciting transformation in a generation, with resultant benefits of digital pathology for pathologists, clinicians and, most importantly, patients.
Contributor: Dr Maurice Loughrey