In a new study published in the journal Nature Medicine, scientists at the NYU School of Medicine trained a Google deep learning algorithm called Inception v3 to distinguish between two of the most common types of lung cancers, adenocarcinoma and squamous cell carcinoma, with 97% accuracy.
Additionally, the study’s AI analyzed images to determine whether abnormal versions of six genes linked to lung cancer – including EGFR, KRAS and TP53 – were present in cells, with an accuracy that ranged from 73 to 86 percent depending on the gene.
According to researchers, in targeted therapies that work only against cancer cells with specific mutations, it is crucial to determine which genes are changed in each tumor. For example, about 20% of patients with adenocarcinoma are known to have mutations in the gene epidermal growth factor receptor or EGFR, which can be treated with drugs currently available.
However, it may take weeks for the currently used genetic tests to confirm the presence of mutations.
Senior study author Aristotelis Tsirigos, PhD, associate professor in the Department of Pathology at NYU School of Medicine and NYU Langone Health’s Perlmutter Cancer Center said that delaying the start of cancer treatment is always bad. “Our study provides strong evidence that an AI approach will be able to instantly determine cancer subtype and mutational profile to get patients started on targeted therapies sooner, he said.
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