AI system learns ‘language of cancer’ to enable improved diagnosis

A computer system which harnesses the power of AI to learn the “language of cancer” could help provide faster diagnosis, its developers have said.

Researchers said the system is capable of spotting the signs of the disease in biological samples with remarkable accuracy, and can also provide reliable predictions of patient outcomes.

Currently, pathologists examine and characterise the features of tissue samples taken from cancer patients on slides under a microscope.

Their observations on the tumour’s type and stage of growth help doctors determine each patient’s course of treatment and their chances of recovery.

An international team of AI specialists and cancer scientists, led by researchers from the University of Glasgow and New York University, have developed a new system, which they call histomorphological phenotype learning (HPL).

They began by collecting thousands of high-resolution images of tissue samples of lung adenocarcinoma taken from 452 patients stored in the United States National Cancer Institute’s Cancer Genome Atlas database.

In many cases, the data is accompanied by additional information on how the patients’ cancers progressed.

Next, they developed an algorithm which used a training process called self-supervised deep learning to analyse the images and spot patterns based solely on the visual data in each slide.

The algorithm broke down the slide images into thousands of tiny tiles, each representing a small amount of human tissue.

A deep neural network scrutinised the tiles, teaching itself in the process to recognise and classify any visual features shared across any of the cells in each tissue sample.

Dr Ke Yuan, of the University of Glasgow’s School of Computing Science, who supervised the research and is the paper’s senior author, said the algorithm learned to spot recurring visual elements in the tiles which correspond to textures, cell properties and tissue architectures called phenotypes.

He said: “By comparing those visual elements across the whole series of images it examined, it recognised phenotypes which often appeared together, independently picking out the architectural patterns that human pathologists had already identified in the samples.”

When the team added analysis of slides from squamous cell lung cancer to the HPL system, it was capable of correctly distinguishing between their features with 99% accuracy.

Once the algorithm had identified patterns in the samples, the researchers used it to analyse links between the phenotypes it had classified and the clinical outcomes stored in the database, including how long patients lived after having cancer surgery.

The predictions made by the HPL system correlated well with the real-life outcomes of the patients stored in the database, correctly assessing the likelihood and timing of cancer’s return 72% of the time.

Human pathologists tasked with the same prediction drew the correct conclusions with 64% accuracy.

When the research was expanded to include analysis of thousands of slides across 10 other types of cancers, the results were similarly accurate.

Professor John Le Quesne, from the University of Glasgow’s School of Cancer Sciences, is one of the co-senior authors of the paper and supervised the research.

He said: “It takes many years to train human pathologists to identify the cancer subtypes they examine under the microscope and draw conclusions about the most likely outcomes for patients.

“It’s a difficult, time-consuming job, and even highly-trained experts can sometimes draw different conclusions from the same slide.

“In a sense, the algorithm at the heart of the HPL system taught itself from first principles to speak the language of cancer – to recognise the extremely complex patterns in the slides and read what they can tell us about both the type of cancer and its potential effect on patients’ long-term health.

“Unlike a human pathologist, it doesn’t understand what it’s looking at, but it can still draw strikingly accurate conclusions based on mathematical analysis.

“It could prove to be an invaluable tool to aid pathologists in the future, augmenting their existing skills with an entirely unbiased second opinion.

“The insight provided by human expertise and AI analysis working together could provide faster, more accurate cancer diagnoses and evaluations of patients’ likely outcomes.

“That, in turn, could help improve monitoring and better-tailored care across each patients’ treatment.”

The research is published in the journal Nature Communications.

Researchers from University College London and the Karolinska Institute in Sweden also contributed to the paper.