NEW YORK, Sept. 17, 2018 /PRNewswire/ -- A new computer program can analyze images of patients' lung tumors, specify cancer types, and even identify altered genes driving abnormal cell growth, a new study shows.

Led by researchers at NYU School of Medicine and published online September 17 in Nature Medicine, the study found that a type of artificial intelligence, or "machine learning," program could distinguish -- with 97 percent accuracy -- between adenocarcinoma and squamous cell carcinoma, two lung cancer types that experienced pathologists at times struggle to parse without confirmatory tests.

In addition, the study's AI was also able to, again from analyzing images, 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. Such genetic changes or mutations often cause the abnormal growth seen in cancer, but can also change a cell's shape and interactions with its surroundings, providing visual clues for automated analysis.
Determining which genes are changed in each tumor has become vital with the increased use of targeted therapies that work only against cancer cells with specific mutations, researchers say. About 20 percent of patients with adenocarcinoma, for instance, are known to have mutations in the gene epidermal growth factor receptor or EGFR, which can now be treated with approved drugs.

But the genetic tests currently used to confirm the presence of mutations can take weeks to return results, say the study authors.

"Delaying the start of cancer treatment is never good," says 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. "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."

Machine Learning

In the current study, the research team designed statistical techniques that gave their program the ability to "learn" how to get better at a task but without being told exactly how. Such programs build rules and mathematical models that enable decision-making based on data examples fed into them, with the program getting "smarter" as the amount of training data grows.

Newer AI approaches, inspired by nerve cell networks in the brain, use increasingly complex circuits to process information in layers, with each step feeding information into the next, and assigning more or less importance to each piece of information along the way. The current team trained a deep convolutional neural network, Google's Inception v3, to analyze slide images obtained from The Cancer Genome Atlas, a database of images where cancer diagnoses had already been determined. That let the researchers to measure how well their program could be trained to accurately and automatically classify normal versus diseased tissue.

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