Artificial intelligence for automating the measurement of histologic image biomarkers. J Clin Invest 2021 Apr 15;131(8)
Date
04/16/2021Pubmed ID
33855974Pubmed Central ID
PMC8262491DOI
10.1172/JCI147966Scopus ID
2-s2.0-85104201082 (requires institutional sign-in at Scopus site) 1 CitationAbstract
Artificial intelligence has been applied to histopathology for decades, but the recent increase in interest is attributable to well-publicized successes in the application of deep-learning techniques, such as convolutional neural networks, for image analysis. Recently, generative adversarial networks (GANs) have provided a method for performing image-to-image translation tasks on histopathology images, including image segmentation. In this issue of the JCI, Koyuncu et al. applied GANs to whole-slide images of p16-positive oropharyngeal squamous cell carcinoma (OPSCC) to automate the calculation of a multinucleation index (MuNI) for prognostication in p16-positive OPSCC. Multivariable analysis showed that the MuNI was prognostic for disease-free survival, overall survival, and metastasis-free survival. These results are promising, as they present a prognostic method for p16-positive OPSCC and highlight methods for using deep learning to measure image biomarkers from histopathologic samples in an inherently explainable manner.
Author List
Cornish TCAuthor
Toby Charles Cornish MD, PhD Professor in the Pathology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Artificial IntelligenceBiomarkers
Head and Neck Neoplasms
Humans
Image Processing, Computer-Assisted
