AI helps to predict lung cancer severity

A computer program based on data from nearly a half-million tissue images and powered by artificial intelligence (AI) can accurately diagnose in cases of adenocarcinoma, the most common form of lung cancer.

Researchers at New York University Langone Health’s Perlmutter Cancer Center and the University of Glasgow, in Scotland, developed and tested the program. 

The algorithm, or more specifically histomorphological phenotype learning (HPL), was found to accurately distinguish between similar lung cancers – adenocarcinoma and squamous cell cancers – 99% of the time. The HPL program was also found to be 72% accurate at predicting the likelihood and timing of cancer’s return after therapy, bettering the 64% accuracy in the predictions made by pathologists who directly examined the same patients’ tumour images, researchers said.

The research team also points out that the program is independent and “self-taught,” meaning that it determined on its own which structural features were statistically most significant for gauging the severity of disease and had the greatest impact on tumour recurrence.

The team stress that because HPL is self-learning, the program will become increasingly more accurate as more data is added over time. To build public trust, researchers have posted their programming code online and have plans to make the new HPL tool freely available on completion of further testing.

The study was published recently in Nature Communication.