Artificial intelligence methods for predictive image-based grading of human cancers

Surgical pathology and the practice of general medicine are often described as having legitimate components of art and science. That perception is perhaps less relevant in the current information age with the adoption of genomic medicine, largely due to the growth in disease-specific causal knowledge. As a surgical pathologist, gestalt and intuition are words you may not use to describe how you arrive at a specific patient’s diagnosis, but it is certainly understood that with experience, in particular with a deep immersion in a subspecialty, comes an understanding of histopathology that cannot easily be transmitted or formalized. This is not in any way to delegitimize pathology or relegate it to the unscientific, but rather to illustrate the strength of the human brain. For well over 100years using a microscope, pathologists have been applying this strength to group and separate disease entities into diagnoses and their subtypes. The brain seems to be good at pattern recognition, filtering noise, and dealing with variability, and from a histopathology perspective, this has complemented the medical need quite well for over a century. Although the concept of tumor differentiation and its relationship to clinical outcome dates back to the early days of histopathology until fairly recently, limited treatment options diminished the necessity for more precise and standardized grading. The following chapter explores how the growing fields of computational pathology, machine learning, and deep learning are coming together to address the need for more precise and standardized grading. Starting with tissue preparation and image acquisition, the discussion roughly follows the steps involved in developing an image analysis-based multivariate grading system with attention drawn in each subsection to inherent obstacles, published state-of-the-art techniques, and the authors’ own experience with solutions.