Affiliation:
1. Department of Hospital Pathology, College of Medicine, The Catholic University of Korea , Seoul 06591, Republic of Korea
2. Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea , Seoul 06591, Republic of Korea
Abstract
Abstract
Purpose
Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review.
Methods
A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed.
Results
Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal.
Conclusion
AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.
Funder
Korea Health Industry Development Institute
Ministry of Health & Welfare, Republic of Korea
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
1 articles.
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