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This Football Shirt Friday, we pause to reflect on how far cancer research has come and the role technology continues to play in shaping its future.
In memory of Bobby Moore OBE, and in support of the Bobby Moore Fund, this newsletter features the top stories in cancer research, from AI breakthroughs in predicting bowel cancer recurrence to innovations in early detection, diagnostics, and treatment.
Whether you're wearing a shirt in support today or simply staying informed, these stories remind us of the power of progress and the people behind it.
AI helps predict bowel cancer recurrence
A new study using Artificial Intelligence (AI) sheds light on how immune cells, particularly CD3 and CD8 T-cells, can help predict the risk of bowel cancer returning after surgery.
Colorectal cancer, or bowel cancer, is one of the most common cancers worldwide, and being able to predict whether the cancer will come back is crucial for guiding further treatment.
The role of AI in predicting recurrence
The study, part of the larger QUASAR trial, examined tumour samples from 868 patients with stage II and III colorectal cancer. Researchers focused on two types of immune cells – CD3 and CD8 T-cells. These cells are part of the body's natural defence system, attacking cancer cells and helping to control tumour growth. The team used advanced AI algorithms to count the number of these immune cells in different parts of the tumours, making the process much quicker and more precise than manual counting by pathologists.
Using AI, the study found that patients with a high density of CD3 and CD8 T-cells in their tumours were less likely to experience cancer recurrence. Specifically, patients with higher numbers of these immune cells had half the risk of cancer returning compared to those with lower numbers. This was consistent for both stage II and III cancers, and across colon and rectal cancers.
Prognosis vs chemotherapy benefit
The AI test helped predict who was more likely to see their cancer return after surgery. However, while CD3 and CD8 T-cells were good markers for prognosis, they did not predict which patients would benefit most from chemotherapy. Both high-risk and low-risk patients who received chemotherapy saw similar reductions in recurrence rates. This means that, although AI can identify who is at greater risk of cancer coming back, it doesn’t yet help doctors decide whether chemotherapy will be more effective for these patients.
AI trial might help catch breast cancer earlier
Nearly 700,000 women across the country will take part in a trial to test how cutting-edge AI tools can be used to catch breast cancer cases earlier.
As government ramps up the use of new technology across the board, 30 testing sites across the country will be enhanced with the latest digital AI technologies, ready to invite women already booked in for routine screenings on the NHS to take part. The technology will assist radiologists, screening patients to identify changes in breast tissue that show possible signs of cancer and referred for further investigations if required.
Currently two specialists are needed per mammogram screening. This technology enables just one to complete the same mammogram screening process safely and efficiently. If the trial is successful, it could free up hundreds of radiologists and other specialists across the country to see more patients, tackle rising cancer rates, save more lives and cut waiting lists.
The EDITH trial (‘Early Detection using Information Technology in Health’) is backed by £11 million of government support via the National Institute for Health and Care Research (NIHR). It is the latest example of how British scientists are transforming cancer care, building on the promising potential of cutting-edge innovations to tackle one of the UK’s biggest killers.
AI technology to help cut cancer waiting lists
Cancer waiting times are set to fall thanks to new AI technology that locates cancer cells 2.5 times quicker than doctors alone.
Game-changing AI will start being rolled out to every NHS radiotherapy department in England in a matter of weeks – backed by £15.5 million in new Government funding.
It works by automatically reviewing a CT or MRI scan, helping doctors quickly distinguish between cancerous cells and healthy organs and to prevent healthy organs from being damaged during radiation treatment.
Trained health workers will of course review any report before administering any treatment – helping tens of thousands of cancer patients each year get faster treatment.
The UK is at the forefront of embracing and embedding AI into the healthcare system, and with it already being used in 90% of stroke units in England – which is speeding up diagnosis and treatment.
Today’s announcement is another major step to help cut NHS waiting lists, relieve pressure on hospitals, free up staff time, and support people in care settings to live more independently.
LG develop cancer diagnosis AI model on Amazon Web Services
At AWS re:Invent, Amazon announced that LG AI Research, the artificial intelligence (AI) research hub of LG, has used AWS to develop its new pathology foundation model (FM) for earlier cancer diagnosis and treatment.
The histopathology image-specific model, EXAONEPath, can securely analyse microscopic images of tissue samples from cancer patients to reduce genetic testing times from two weeks to less than one minute, helping medical professionals improve the speed and effectiveness of treatments. This isn't the first or last time we will see AI within cancer diagnosis.
EXAONEPath achieves an average accuracy of 86.1% across six benchmarks in correctly classifying cellular-level visual features, which is comparable to other leading pathology FMs trained on far larger data sets. With AWS, LG AI Research transfers terabytes of data to the cloud in less than an hour, shortening model training time from 60 days to one week. This improves EXAONEPath’s performance in diagnosing and detecting cancer, leading to improved clinical outcomes for patients. By running on AWS, LG AI Research can also reduce its data management and infrastructure costs by approximately 35% and cut data preparation time by 95%.
“AWS allows us to accelerate our AI research, bringing accessible and rapid cancer screening closer to reality,” said Hwayoung(Edward) Lee, vice president of LG AI Research. “By leveraging AWS, we can train our pathology model on a vast dataset faster—securely, and cost-effectively. This enhances EXAONEPath’s processing capabilities for delivering personalised, efficient cancer treatments to improve patient outcomes. EXAONEPath has the potential to transform cancer diagnosis and treatment globally.”
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