New AI tool can accurately identify cancer at an early stage

A team of doctors, scientists, and researchers have developed an artificial intelligence (AI) model that can accurately identify cancer, potentially speeding up diagnosis and treatment of the disease.

Cancer is a leading cause of death worldwide, responsible for around 10 million deaths annually or nearly one in six deaths, according to the World Health Organization. However, the disease can be cured if detected early and treated swiftly.

Experts at the Royal Marsden NHS foundation trust, the Institute of Cancer Research, London, and Imperial College London developed an AI tool that can identify whether abnormal growths found on CT scans are cancerous. The algorithm, developed using radiomics, extracts vital information from medical images not easily spotted by the human eye, resulting in more efficient and effective diagnoses than current methods.

The study, whose findings were published in the Lancet’s eBioMedicine journal, used CT scans of approximately 500 patients with large lung nodules to develop the AI algorithm. The model was then tested to determine if it could accurately identify cancerous nodules. An AUC (area under the curve) measure was used to determine the model’s effectiveness in predicting cancer. An AUC of 1 indicates a perfect model, while 0.5 would be expected if the model was randomly guessing.

Results showed that the AI model could identify each nodule’s risk of cancer with an AUC of 0.87. The performance improved on the Brock score, a test currently used in clinic, which scored 0.67. The model also performed comparably with the Herder score – another test – which had an AUC of 0.83.

“According to these initial results, our model appears to identify cancerous large lung nodules accurately,” said Dr. Benjamin Hunter, a clinical oncology registrar at the Royal Marsden and a clinical research fellow at Imperial.

The AI model may also help doctors make quicker decisions about patients with abnormal growths that are currently deemed medium-risk. When combined with Herder, the AI model was able to identify high-risk patients in this group. The study suggested early intervention for 18 out of 22 (82%) of the nodules that went on to be confirmed as cancerous.

The team stressed that the study, backed by the Royal Marsden Cancer Charity, the National Institute for Health and Care Research, RM Partners, and Cancer Research UK, was still at an early stage. More testing will be required before the model can be introduced in healthcare systems.

“Through this work, we hope to push boundaries to speed up the detection of the disease using innovative technologies such as AI,” said the Libra study’s chief investigator, Dr. Richard Lee, consultant physician in respiratory medicine at the Royal Marsden and team leader at the Institute of Cancer Research.

Dr. Lee said that lung cancer was a good example of why new initiatives to speed up detection were urgently needed. It is the biggest worldwide cause of cancer mortality and accounts for a fifth (21%) of cancer deaths in the UK. Those diagnosed early can be treated much more effectively, but more than 60% of lung cancers in England are diagnosed at either stage three or four.

“People diagnosed with lung cancer at the earliest stage are much more likely to survive for five years when compared with those whose cancer is caught late,” said Dr. Lee. “This means it is a priority we find ways to speed up the detection of the disease, and this study – which is the first to develop a radiomics model specifically focused on large lung nodules – could one day support clinicians in identifying high-risk patients.”

Dr. Hunter added: “In the future, we hope it will improve early detection and potentially make cancer treatment more successful by highlighting high-risk patients and fast-tracking them to earlier intervention.”