An extensive new study recently published in Nature Machine Intelligence shows that a prognostic tool, developed by a diverse team of researchers led by Professor Mihaela van der Schaar from The Alan Turing Institute and the University of Cambridge, can recommend therapies for breast cancer patients more reliably than methods that are currently considered international clinical best practice. The study makes use of complex, high-quality cancer datasets from the UK and US to demonstrate the accuracy of “Adjutorium,” a machine learning system for prognostication and treatment benefit prediction.

Adjutorium is a  novel machine learning tool that can be trained to inform treatment decisions for a wide range of different diseases. It predicts an individual’s likely outcomes in the event of treatment or no treatment—with the difference between the two being their individualised “survival benefit” from treatment.

In the case of breast cancer, Adjutorium was tasked with determining whether or not patients would benefit from adjuvant therapies prescribed in addition to surgery, such as chemotherapy and hormone therapy. While such therapies have improved outcomes for early-stage breast cancer patients since their introduction, they bear their own risks, which must be weighed carefully against the expected benefits. Accurately predicting survival benefit is of critical importance in order to prevent a patient from being undertreated or overtreated.

Real-world clinical problems such as these lie at the heart of the van der Schaar Lab’s work. Describing the importance of the Adjutorium project, van der Schaar explained: “Adjutorium exemplifies my lab’s focus on the development of new machine learning tools that can support clinicians by allowing them to make better, more accurate decisions for each patient they treat.”

For more information on this study and the wider work of Professor Mihaela van der Schaar and her Lab please see their website.

Professor Mihaela van der Schaar

Turing Fellow, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and Founder and Director of the Cambridge Centre for AI in Medicine