Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence
Authors: Phedias Diamandis
Liron Pantanowitz
Morteza Babaie
Clinton J V Campbell
Shivam Kalra
Amir Safarpoor
Sobhan Shafiei
Sultaan Shah
Charles Choi
Savvas Damaskinos
H R Tizhoosh
Journal: npj Digital Medicine (2020)
Discipline: CLINICAL MEDICINE
Keywords: Cancer imaging; Data mining; Machine learning.
Affiliations: Huron Digital Pathology, St. Jacobs, ON Canada., 2Kimia Lab, University of Waterloo, Waterloo, ON Canada., 3Vector Institute, MaRS Centre, Toronto, ON Canada., 4General Hospital/Research Institute (UHN), Toronto, Canada., 5Stem Cell and Cancer Research Institute, McMaster University, Hamilton, Canada., 6Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada., 7Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA USA.

Abstract:  The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new 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) of whole-slide images from almost 11,000 patients. We successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000 × 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 section 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. 

Retracted: No

PubPeer comments:
 This paper applies machine learning (ML) methods to histopathology images from The Cancer Genome Atlas. In general a trend is observed whereby increased number of images for a particular pathology leads to better Accuracy and Recall performance. The following text appears in the article Results: In addition, the Cox-Stuart trend test44 was used to check the upward monotonic trend of accuracy with respect to patients number. Having an increasing trend is considered as the null hypothesis for this test. The ud835udc5d-values for the horizontal (vertical) search are 1 (0.9991) and 0.9844 (0.9713) for frozen and diagnostic slides, respectively. Since the ud835udc5d-values are greater than the significance level (0.05), the null hypothesis is accepted. Consequently, there is a strong evidence of an upward monotonic trend. Surely the authors meant to say,  Since the ud835udc5d-values are greater than the significance level (0.05), the null hypothesis _cannot_ be rejected. The statement about strong evidence, in this context, seems vastly overblown and unsupported by the stated level of evidence. ,