Researchers in the UK are using machine intelligence to predict how patients will respond to cancer treatment before they receive it.
Game-changing technology is part of a wave of Cambridge research harnessing AI’s power in the fight against disease.
The tool focuses on ovarian cancer but has the potential to help treat other cancers, too.
The AI model uses machine learning to digitally integrate a wealth of patient data – including CT images, blood tests, genomic results and electronic health records – to help clinicians make quicker, highly personalised and better-informed decisions in treating ovarian cancer – often diagnosed at an advanced stage when immediate intervention is critical.
Most patients are given chemotherapy to shrink tumours ahead of primary surgery to remove them.
However, figures show that 39% of women do not respond to the treatment, thereby delaying surgical intervention.
This new AI tool – IRON (Integrated Radiogenomics for Ovarian Neoadjuvant therapy) – enabled researchers to predict whether chemotherapy would be effective during a study involving 134 women at two centres.
Dr Mireia Crispin-Ortuzar, who, along with Professor James Brenton, co-leads the Cancer Research UK Cambridge Centre's Ovarian Cancer programme, said: ‘Essentially, it means we can estimate who might benefit from these treatments and who might need alternatives so we can make changes to the treatment journey sooner.
‘It also allows us to discover why patients do not respond to chemotherapy.’
Alongside clinical data and patients’ general characteristics – including age and disease status – IRON uses augmented radionics to scan CT images, measure lesions, and reveal patterns in tumours and tissues that the human eye cannot detect on a scale that would be prohibitively expensive to do “by hand”.’
All of this information is then analysed using the AI model to predict the likely success of chemotherapy.
Dr Crispin-Ortuzar added: ‘Measuring the volume of the disease is crucial in understanding and treating it. Measuring the disease helps greatly if you know the boundary of the lesions. This is extremely time-consuming for radiologists and takes hours out of their day. Now we have developed automatic AI tools to do this, which coupled with IRON could be game-changing.’
Professor Brenton, co-leader of Cambridge University’s Mark Foundation Institute for Integrated Cancer Medicine and based at the Cancer Research UK Cambridge Institute, said the research was hugely significant and that AI offered clinicians ‘new thinking” on targeting cancer.
‘AI gives us the ability to follow these structures in time and space and to correlate the image with the genomic features. And that is a really major step forward, and is what a complex disease like ovarian cancer really needs. Outcomes for patients with advanced stages of ovarian cancer haven’t changed dramatically in the past 20 years, because research hasn’t made a “move the needle” difference. It needs new thinking that can sometimes be lost in more pragmatic kinds of clinical trials, which haven’t learned enough about why the cancer does or does not respond. We need completely new tools, and these AI approaches are ground-breaking in that regard.’
Photo caption Dr Mireia Crispin-Ortuzar
