Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

Abstract

The emergence of digital pathology has opened new horizons for histopathology and related fields such as cytology. Computer programs and, in particular, artificial-intelligence algorithms, are able to operate on digitized slides to assist pathologists with diagnostic and theranostic tasks. Whereas machine learning involving classification and segmentation methods have obvious benefits for performing image analysis in pathology, image search represents an alternate and fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologist a novel and real-time approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas [TCGA] program by National Cancer Institute, USA) of whole slide images from almost 11,000 patients depicting different types of malignancies. For the first time, we successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000x*1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative "**majority voting"** to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen sections slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%).The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype. ⇤ Corresponding author: tizhoosh@uwaterloo.ca⇥ pathology.Content-based image search [8][9][10][11] implies that the input for search software is not text (e.g., disease description in a pathology report) but rather the input is an image such that the search and retrieval can be performed based on image pixels (visual content). Content-based image search is inherently unsupervised, which means that its design and implementation may not need manual delineation of a region of interest in the images 12-14 . More importantly, image search does not make any direct diagnostic decision on behalf of the pathologist; instead, it searches for similar images and retrieves them along with corresponding metadata (i.e., pathology reports), and displays them to the pathologist as decision support.Variability in the visual inspection of medical images is a well-known problem [15][16][17] . Both inter-and intra-observer variability may affect image assessment and subsequently the ensuing diagnosis [18][19][20][21] . A large body of work...

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

“…Macrophages are particularly difficult for pathologists to identify solely under H&E staining. While the accuracy of an individual pathologist identifying macrophages may be poor, our models represent an aggregate estimate based on training from hundreds of pathologist annotators, which may carry a more reliable signal 82 , 83 . Future development of our approach could extend to multiplex immunofluorescence technologies that measure spatial protein expression.…”

Image Recognition and Simulation Based on Distributed Artificial Intelligence

“…Related scholars combined the heavy-ball momentum method in centralized optimization with the distributed gradient tracing algorithm (Am algorithm) using row random and column random double matrices and proposed the ABm algorithm, which successfully integrated the centralized first-order optimization method used in distributed algorithms [15]. rough theoretical analysis and simulation experiments, relevant scholars have proved that the algorithm after adding the momentum term can converge to the global optimal solution at a linear speed faster than the original algorithm [16]. Based on the Nesterov method, the researchers proposed two distributed optimization algorithms under a fixed undirected network [17].…”

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer

“…Macrophages are particularly difficult for pathologists to identify solely under H&E staining. While the accuracy of an individual pathologist identifying macrophages may be poor, our models represent a consensus across hundreds of pathologist annotators which may carry a more reliable signal 61,62 . Furthermore, morphologically similar cells (e.g.…”