Professor Eric Aboagye, Professor of Cancer Pharmacology and Molecular Imaging...
Professor Eric Aboagye, Professor of Cancer Pharmacology and Molecular Imaging at Imperial College London.
Source: Imperial College London

AI predicts survival of ovarian cancer patients

The artificial intelligence software, created by researchers at Imperial College London and the University of Melbourne, has been able to predict the prognosis of patients with ovarian cancer more accurately than current methods. It can also predict what treatment would be most effective for patients following diagnosis. The trial took place at Hammersmith Hospital, part of Imperial College Healthcare NHS Trust.

Researchers say that this new technology could help clinicians administer the best treatments to patients more quickly and paves the way for more personalised medicine. They hope that the technology can be used to stratify ovarian cancer patients into groups based on the subtle differences in the texture of their cancer on CT scans rather than classification based on what type of cancer they have, or how advanced it is.

“The long-term survival rates for patients with advanced ovarian cancer are poor despite the advancements made in cancer treatments. There is an urgent need to find new ways to treat the disease, said Professor Eric Aboagye, lead author and Professor of Cancer Pharmacology and Molecular Imaging, at Imperial College London. “Our technology is able to give clinicians more detailed and accurate information on the how patients are likely to respond to different treatments, which could enable them to make better and more targeted treatment decisions.”

Professor Andrea Rockall, co-author and Honorary Consultant Radiologist, at Imperial College Healthcare NHS Trust, added: “Artificial intelligence has the potential to transform the way healthcare is delivered and improve patient outcomes. Our software is an example of this and we hope that it can be used as a tool to help clinicians with how to best manage and treat patients with ovarian cancer.”

Photo
Ovarian cancer is the sixth most common cancer in women and usually affects women after the menopause or those with a family history of the disease.
Source: Imperial College London

Ovarian cancer is the sixth most common cancer in women and usually affects women after the menopause or those with a family history of the disease. There are 6,000 new cases of ovarian cancer a year in the UK but the long-term survival rate is just 35-40 per cent as the disease is often diagnosed at a much later stage once symptoms such as bloating are noticeable. Early detection of the disease could improve survival rates.

Doctors diagnose ovarian cancer in a number of ways including a blood test to look for a substance called CA125 – an indication of cancer – followed by a CT scan that uses x-rays and a computer to create detailed pictures of the ovarian tumour. This helps clinicians know how far the disease has spread and determines the type of treatment patients receive, such as surgery and chemotherapy.

However, the scans can’t give clinicians detailed insight into patients’ likely overall outcomes or on the likely effect of a therapeutic intervention. Researchers used a mathematical software tool called TEXLab to identify the aggressiveness of tumours in CT scans and tissue samples from 364 women with ovarian cancer between 2004 and 2015.

The software examined four biological characteristics of the tumours which significantly influence overall survival – structure, shape, size and genetic makeup – to assess the patients’ prognosis. The patients were then given a score known as Radiomic Prognostic Vector (RPV) which indicates how severe the disease is, ranging from mild to severe.

The researchers compared the results with blood tests and current prognostic scores used by doctors to estimate survival. They found that the software was up to four times more accurate for predicting deaths from ovarian cancer than standard methods. The team also found that five per cent of patients with high RPV scores had a survival rate of less than two years. High RPV was also associated with chemotherapy resistance and poor surgical outcomes, suggesting that RPV can be used as a potential biomarker to predict how patients would respond to treatments.

Professor Aboagye suggests that this technology can be used to identify patients who are unlikely to respond to standard treatments and offer them alternative treatments. The researchers will carry out a larger study to see how accurately the software can predict the outcomes of surgery and/or drug therapies for individual patients.

Subscribe to our newsletter

Related articles

AI predicts effectiveness of immunotherapy

AI predicts effectiveness of immunotherapy

Scientists can determine which lung-cancer patients will benefit from expensive immunotherapy.

AI & MRI look into the genome of brain tumors

AI & MRI look into the genome of brain tumors

Researcher have developed a computer method that uses MRI and machine learning to rapidly forecast genetic mutations in glioma tumors,

ConvPath software uses AI to identify cancer cells

ConvPath software uses AI to identify cancer cells

A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.

Robotic technology enhances spine surgery

Robotic technology enhances spine surgery

Spine surgery: A new robotic technology increases the safety and precision of spinal fusion surgeries while reducing the time needed for the procedure.

Integrating imaging with deep neural networks

Integrating imaging with deep neural networks

Neural network framework may increase radiologist's confidence in assessing the type of lung cancer on CT scans, informing individualized treatment planning.

COVID-19: AI models not yet suitable for clinical use

COVID-19: AI models not yet suitable for clinical use

Researchers have found that out of the more than 300 COVID-19 machine learning models are not suitable for detecting or diagnosing COVID-19 from standard medical imaging.

AI identifies precancerous colon polyps

AI identifies precancerous colon polyps

A machine learning algorithm helps accurately differentiate benign and premalignant colorectal polyps on CT colonography scans.

Deep learning-based image segmentation

Deep learning-based image segmentation

Scientists have presented a new method for configuring self-learning algorithms for a large number of different imaging datasets – without the need for specialist knowledge or very significant computing power.

AI accurately detects COVID-19 on chest x-rays

AI accurately detects COVID-19 on chest x-rays

Researchers have developed a new AI platform that detects COVID-19 by analyzing X-ray images of the lungs.

Popular articles

Subscribe to Newsletter