| Title | Artificial intelligence for automating the measurement of histologic image biomarkers. |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Authors | Cornish, TC |
| Journal | The Journal of clinical investigation |
| Volume | 131 |
| Issue | 8 |
| Date Published | 2021 Apr 15 |
| ISSN | 1558-8238 |
| 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. |
| DOI | 10.1172/JCI147966 |
| PDF Link | |
| Alternate Journal | J Clin Invest |
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