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The emergence of digital pathology has opened new horizons for histopathologyand cytology. Artificial-intelligence algorithms are able to operate ondigitized slides to assist pathologists with diagnostic tasks. Whereas machinelearning involving classification and segmentation methods have obviousbenefits for image analysis in pathology, image search represents a fundamentalshift in computational pathology. Matching the pathology of new patients withalready diagnosed and curated cases offers pathologist a novel approach toimprove diagnostic accuracy through visual inspection of similar cases andcomputational majority vote for consensus building. In this study, we reportthe results from searching the largest public repository (The Cancer GenomeAtlas [TCGA] program by National Cancer Institute, USA) of whole slide imagesfrom almost 11,000 patients depicting different types of malignancies. For thefirst time, we successfully indexed and searched almost 30,000 high-resolutiondigitized slides constituting 16 terabytes of data comprised of 20 million1000x1000 pixels image patches. The TCGA image database covers 25 anatomicsites and contains 32 cancer subtypes. High-performance storage and GPU powerwere employed for experimentation. The results were assessed with conservative"majority voting" to build consensus for subtype diagnosis through verticalsearch 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 thymoma100%). The key finding of this validation study was that computationalconsensus appears to be possible for rendering diagnoses if a sufficientlylarge number of searchable cases are available for each cancer subtype.

Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence