Artificial intelligence for automating the measurement of histologic image biomarkers.

Citation metadata

Author: Toby C. Cornish
Date: Apr. 15, 2021
From: Journal of Clinical Investigation(Vol. 131, Issue 8)
Publisher: American Society for Clinical Investigation
Document Type: Report
Length: 2,460 words
Lexile Measure: 1420L

Document controls

Main content

Abstract :

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.

Source Citation

Source Citation   

Gale Document Number: GALE|A659258536