Software‐assisted decision support in digital histopathology

Abstract

Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the daily task for pathologists is changing drastically and will become increasingly demanding in order to take advantage of the development of modern computer technologies. The role of pathologist has rapidly evolved from exclusively describing the morphology and phenomenology of a disease, to becoming a gatekeeper for novel and most effective treatment options. This is possible based on the retrieval and management of a wide range of complex information from tissue or a group of cells and associated meta-data. Intelligent and self-learning software solutions can support and guide pathologists to score clinically relevant decisions based on the accurate and robust quantification of multiple target molecules or surrogate biomarker as companion or complimentary diagnostics along with relevant spatial relationships and contextual information from digital H&E and multiplexed images. With the availability of multiplex staining techniques on a single slide, high-resolution image analysis tools, and high-end computer hardware, machine and deep learning solutions now offer diagnostic rulesets and algorithms that still require clinical validation in well-designed studies. Before entering the clinical practice, the 'human factor' pathologist needs to develop trust in the output coming from the 'digital black box of computational pathology', including image analysis solutions and artificial intelligence algorithms to support critical clinical decisions which otherwise would not be available.

Opening the black box of AI‐Medicine

“…Tissue diagnostics is the expertise of pathologists who focus on morphology and diagnosis. Digitalization of histology slides can now be made to stratify patients, enabling the administration of personalized therapy and potentially improve outcome of patients 7 . Using deep learning technology and examine directly the conventional hematoxylin and eosin stain of colorectal cancer tissue, an international group demonstrated that biomarkers have been identified by ML, which predict outcome of patients 8 .…”

Recent Advances in Pathology: the 2020 Annual Review Issue of The Journal of Pathology

“…They discuss that, together with high end computer hardware, machine-and deep-learning solutions, there is a huge opportunity to improve the potential of personalised medicine to enable the administration of modern and emerging therapeutics. They emphasise especially that these digitalised and computerised approaches and the resulting software-driven clinical decision-making process will require significant changes in education and training for their introduction into clinical routines [16]. The final two articles in the 2020 Annual Review Issue concern the use of newly developed single cell methodologies and their impact on understanding both normal physiology and the pathophysiology of selected conditions.…”

A Deep Learning Model for Molecular Label Transfer that Enables Cancer Cell Identification from Histopathology Images

“…The increasing production of WSIs requires advanced computational approaches to be developed to analyze these medical images in a fast, robust and accurate manner, ultimately leading to applications in automated cancer diagnosis [6][7][8][9] .…”