The gold standard of cancer diagnosis is performed by pathologists reviewing thin slices of chemically stained (Hematoxylin & Eosin based, H&E) tumor tissues. This technique has changed very little in the past 100+ years. The quality and speed of Whole Slide Imaging (WSI) scanners have recently reached the ability to be used clinically. These scanners convert a glass digital slide into a giga-pixel image and have recently obtained FDA clearance.
With the recent advancements of Artificial Intelligence (AI) algorithms, more specifically referred to as Machine Learning (ML) and Deep Learning (DL) algorithms, offer the ability to extract pixel level information from these gigapixel images that can not be otherwise observed by the visual interpretation by human pathologists. While this has significant promise in improving the accuracy and consistency of diagnoses in the clinical practice of pathology, this new technology has an even great potential impact in identifying predictive image based biomarkers for Companion Diagnostic (CDx) use and to identify drug mechanisms of action by identifying and quantifying perturbations in the cellular By Jason Hipp, Senior Director, Head of Pathology Data Science & Innovation, Translational at AstraZeneca THE FUTURE OF IMAGING AND THE ROLE OF THE PATHOLOGY DATA SCIENTIST IN DRUG DISCOVERY Tumor MicroEnvironment (TME); as opposed to traditionally developed molecular biomarkers and assays. Although there are greater than 50 articles in a recent pubmed search of pathology and artificial intelligence and images, there is very little available in the burgeoning field of applying it to drug discovery.
While a whole slide image of a patient’s tumor represents the morphological (size, shape, and texture) features of tumor cells, it also serves as a window into the genetic and molecules being made by the tumor cells. For example, recent papers have shown that from a H&E image, the mutational status of the tumor can be predicted with ML. Thus, one can logically hypothesize that the tumor morphology is induced by the tumors DNA/RNA/Protein expression. Furthermore, other studies are showing that specific protein expression can be predicted from H&E images alone, such as PDL1. If such encoded information is in the H&E images of tumors, one can also imagine other variables, such as clinical and treatment related response, that can be similarly predicted from H&E images.
When thinking of the future, one can find even more data with a higher likelihood of finding more predictive variables of drug response by integrating digital pathology images with orthogonally related radiology images. Radiological images of tumors also obtain key tumor related data by its examination at a different length scale than in pathology. For example, current studies have shown the ML can predict chemo response and tumor infiltrating lymphocytes in CT images.