A computer program based on data from nearly a half-million tissue images and powered by artificial intelligence can accurately diagnose cases of adenocarcinoma, the most common form of lung cancer, a new study shows.
Researchers at NYU Langone Health’s Perlmutter Cancer Center and the University of Glasgow developed and tested the program. They say that because it incorporates structural features of tumors from 452 adenocarcinoma patients, who are among the more than 11,000 patients in the United States National Cancer Institute’s Cancer Genome Atlas, the program offers an unbiased, detailed, and reliable second opinion for patients and oncologists about the presence of the cancer and the likelihood and timing of its return (prognosis).
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 to gauging the severity of disease and had the greatest impact on tumor recurrence.
Publishing in the journal Nature Communications online June 11, the study program, also called an algorithm, or 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‘ tumor images, researchers say.
To develop the HPL program, the researchers first analyzed lung adenocarcinoma tissue slides from the Cancer Genome Atlas. Adenocarcinoma was chosen for the test model because the disease is known for characteristic features. As an example, they note that its tumor cells tend to group in so-called acinar, or saclike patterns and spread predictably along the surface lining of lung cells.
From their analysis of the slides, whose visual images were digitally scanned and broken into 432,231 small quadrants or tiles, researchers found 46 key characteristics, what they term histomorphological phenotype clusters, from both normal and diseased tissue, a subset of which were statistically linked to either cancer’s early return or to long-term survival. The findings were then confirmed by further and separate testing on tissue images from 276 men and women who were treated for adenocarcinoma at NYU Langone from 2006 to 2021.
Researchers say their goal is to use the HPL algorithm to assign to each patient a score between 0 and 1 that reflects their statistical chance of survival and tumor recurrence for up to five years. Because the program is self-learning, they stress HPL 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 upon completion of further testing
https://github.com/AdalbertoCq/Histomorphological-Phenotype-Learning
