Researchers have trained a computer program to read slides of tissue samples to diagnose two of the most common types of lung cancer with 97% accuracy. The program also learned to detect cancer-related genetic mutations in the samples just by analyzing the images of cancer tissue.
In a process known as machine learning, the computer program scanned images of tissue slices and developed the ability to differentiate normal lung tissue from the two most common forms of lung cancer, adenocarcinomas, which make up about 40% of lung cancers, and squamous cell carcinomas, which make up about 25% to 30% of lung cancers. Even experienced pathologists can struggle to distinguish these two types of lung cancer, which arise from different types of cells and require very different treatment regimens.
To train the computer program, researchers specializing in machine learning used a deep learning method originally developed and published by Google. The program uses artificial intelligence (AI) to teach itself to get better at a task—in this case, classifying lung cancer specimens—without being told exactly how.