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Million-patient study shows strength of machine learning in recommending breast cancer therapies

13th July 2021 about a 3 minute read
“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.” Professor Mihaela van der Schaar, The Alan Turing Institute

A cutting-edge machine learning tool can recommend therapies for breast cancer patients more reliably than methods that are currently considered international clinical best practice, an extensive new study has found.

The study recently published in Nature Machine Intelligence shows that the 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, makes use of complex, high-quality cancer datasets from the UK and US to demonstrate the accuracy of “Adjutorium.”

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.

In developing and validating Adjutorium, the van der Schaar Lab’s researchers, with guidance from a diverse team of academic and clinical collaborators, conducted one of the largest studies of an AI or machine learning tool for cancer to date.

The process involved the use of data from nationally representative large-scale cohorts of nearly 1 million women within the cancer registries of the U.K. and the U.S.

First, the model was trained and internally validated on roughly 396,000 patients from the U.K. National Cancer Registration and Analysis Service (NCRAS) dataset administered by Public Health England—and, in doing so, became the first AI model to make use of this real-world complex and high-quality dataset.

Adjutorium was then externally validated on 572,000 patients from the U.S. Surveillance, Epidemiology, and End Results (SEER) program.

This makes Adjutorium one of only a handful of AI models to have been validated using datasets from two countries—an especially challenging undertaking given the variations in the datasets between the countries, in addition to their markedly different healthcare systems and populations.

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, say the researchers.

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.”