A key feature of the diagnosis of any disease, but particularly various forms of cancer, is the critical information obtained through a biopsy. A biopsy involves the removal of a small sample of tissue, or a few cells, from the patient for examination by a pathologist looking down an optical microscope. In current practice, the sample is stained with a combination of dyes to help gain some contrast in the image which helps the pathologist see the cells. Generally, based upon this visual inspection of the sample and other relevant medical information, a diagnosis is made. This process, however, is far from ideal since it relies on the subjective expertise of the clinician concerned and is subject to intra and inter observer error. In other words, the process in not exact and depends upon the opinion of the clinicians which may differ. Recently, a number of developments have been made in the field of Digital Pathology and Artificial Intelligence (AI), whereby a high-resolution digital image of the biopsy slide is taken and examined by a computer algorithm which helps the pathologist make a diagnosis. However, analysing the data from effectively three colours in the visible region of the spectrum severely limits the information content of the images obtained.

In recent years several proof-of-concept studies have shown that molecular spectroscopic techniques, such as infrared absorption and Raman scattering, are capable of distinguishing diseased from non-diseased cells and tissue, based upon the inherent biochemistry contained within the cells. There is also mounting evidence that these technologies can help predict likely outcomes of the disease. The regions of the spectrum that these alternative technologies use have 40 times the bandwidth of the visible region and therefore contain 40 times the amount of information.

The UK is at the forefront of these developments in both Digital Pathology and Artificial Intelligence, and in biomedical Infrared and Raman spectroscopies, but at present these two communities are distinct and independent, and thus currently do not interact. Consequently, the advances made in one area cannot be translated to another. In both areas of research there are many hurdles that need to be overcome if the technology is to move from the proof-of-concept stage, through the translational stage, and into the clinical setting, where ultimately it will directly benefit patients. It is the belief of the academic community that we will overcome these hurdles if we pool our resources, bring in both industrial and clinical partners, and work on these generic problems in a dynamic and synergistic way.

CLIRPath-AI is an EPSRC funded Network