Interestingly, the study found that about half of the small percentage of tumor images misclassified by the study AI program were also misclassified by the pathologists, highlighting the difficulty in distinguishing between the two lung cancer types. On the other hand, 45 out of 54 of the images misclassified by at least one of the pathologist in the study were assigned to the correct cancer type by the machine learning program, suggesting that artificial intelligence could offer a useful second opinion.

"In our study, we were excited to improve on pathologist-level accuracies, and to show that AI can discover previously unknown patterns in the visible features of cancer cells and the tissues around them," says co-corresponding author Narges Razavian, PhD, assistant professor in the departments of Radiology and Population Health. "The synergy between data and computational power is creating unprecedented opportunities to improve both the practice and the science of medicine."


Moving forward, the team plans to keep training its AI program with data until it can determine which genes are mutated in a given cancer with more than 90 percent accuracy, at which point they will begin seeking government approval to use the technology clinically, and in the diagnosis of several cancer types.

Along with Tsirigos and Razavian, authors from the NYU School of Medicine were lead investigators Nicolas Coudray of the Applied Bioinformatics Laboratories and Paolo Santiago Ocampo of the Department of Pathology; as well as Navneet Narula, Matija Snuderl, and Andre Moreira in the Department of Pathology, and David Fenyö, in the Department of Biochemistry and Molecular Pharmacology. Also a study author was Theodore Sakellaropoulos in the School of Mechanical Engineering at the National Technical University of Athens in Greece.

The study was supported by Perlmutter Cancer Center support grant, P30CA016087.


SOURCE NYU School of Medicine

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