If recent research is any indication, artificial intelligence (AI) has a bright future in medicine. Nvidia developed an AI system that can generate synthetic scans of brain cancer. Google subsidiary DeepMind has demonstrated a machine learning algorithm that can recommend treatment for more than 50 eye diseases with 94 percent accuracy. And in newly published research, New York University (NYU) showed how AI might aid in lung cancer diagnosis.
A paper published in the journal Nature Medicine (“Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning”) describes how a team of NYU researchers retrained Google’s Inception v3, an open source convolutional neural network architected for object identification, to detect certain forms of lung cancers with 97 percent accuracy.
The program, the researchers found, performed about as well as experienced pathologists when it was used to distinguish between adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and normal lung tissue. In addition, the program was trained to predict the 10 most commonly mutated genes in LUAD. If the training succeeded, the program would be able to identify mutations instantly, potentially avoiding the delays imposed by genetic tests, which can take weeks to confirm the presence of mutations.
“We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS, and TP53—can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population,” the authors of the Nature Medicine article wrote. “These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations.”
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, the researchers say. About 20% 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, noted the study authors.
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 pathologists in the study were assigned to the correct cancer type by the machine learning program, suggesting that AI could offer a useful second opinion.
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% accuracy, at which point they will begin seeking government approval to use the technology clinically, and in the diagnosis of several cancer types.