AI can help in detecting prostate cancer at an early stage, but because of the vast differences in the way clinics prepare samples, scan images and the diverse patient populations they serve, many algorithms do not have universal application.

A team from Karolinska Institutet in Sweden worked with colleagues from Radboud University Medical Center in the Netherlands, University of Turku in Finland and Google Health in the US to run an AI competition involving nearly 1,300 developers from around the world. The developers created algorithms able to grade prostate cancer tumors and trained them using 10,000 international biopsy images. The top performing algorithms outperformed generalist pathologists and matched the average performance of specialist uropathologists.

Dr Kimmo Kartasalo, said: “Grading prostate cancer is a key step in deciding on appropriate treatment, but it’s a fairly subjective process and differences between pathologists’ assessments can sometimes be large. AI can provide an additional expert opinion, helping to offset the shortage of pathologists and standardize grading. While many algorithms are not widely applicable, those developed in our competition did retain their performance across different patient cohorts”.

Professor Jochen Walz heads the Department of Urology at the Institut Paoli-Calmettes Cancer Center in Marseille, France. He said: “AI is going to become a routine tool, which won’t replace pathologists and urologists but will help them reach more consistent decisions. There is currently a lot of variation in the grading of prostate cancers, particularly outside specialist centers.

This research has used a clever means of crowdsourcing expertise to develop AI to improve tumor grading and took the next step by validating it against a very varied range of images. This shows that it could be used in general clinical practice.

So far, AI has only replicated the grading system used by urologists. But it has the potential to go beyond this – to identify elements within the images that can predict clinical outcomes directly. That is the next challenge for AI.”

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