Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers

• Published in: Briefings in bioinformatics Vol. 24; no. 3
• Main Authors: Alam, Mohammad Rizwan, Seo, Kyung Jin, Abdul-Ghafar, Jamshid, Yim, Kwangil, Lee, Sung Hak, Jang, Hyun-Jong, Jung, Chan Kwon, Chong, Yosep
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.05.2023; Oxford Publishing Limited (England)
• Subjects: Artificial Intelligence; Benchmarking; Body organs; Cancer; Chemotherapy; Databases, Factual; Datasets; Developed countries; Drug therapy; Genomes; Head & neck cancer; Humans; Image analysis; Image processing; Literature reviews; Lungs; Mutation; Performance measurement; Point mutation; Thyroid; Thyroid Neoplasms; precision medicine; systematic review; gene mutation; artificial intelligence; whole slide image
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbad151

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.