Scientists have developed an Artificial Intelligence (AI) screening method that they say could detect signs of lung cancer on CT scans a year faster than existing methods.
The researchers hope that using AI to support lung cancer screening could make the process quicker and more efficient, helping to diagnose more patients at an early stage.
The research has been presented at the European Respiratory Society International Congress by Benoît Audelan, a researcher in the Epione project team of the Inria (France’s National Institute for Research in Digital Science and Technology) centre at Université Côte d’Azur. Audelan worked with colleagues from the Université Côte d’Azur, software company Therapixel, and the University Hospital of Nice.
Currently, computerised tomography (CT) scans are already used to spot signs of lung tumours, followed by a biopsy or surgery to confirm whether the tumour is malignant. However, each scan involves an expert radiologist examining around 300 images to look for signs of cancer that can be very small. Trials using CT scans to screen people with a high risk of lung cancer have shown promise, but screening has some practical limitations. For example, a radiologist must review each image one at a time to decide which patients need further tests.
In the study, researchers trained the AI programme using a set of CT scans from 888 patients that had already been examined by radiologists to identify suspicious growths. They then tested it on a different set of 1179 patients who were part of a lung screening trial with a three-year follow-up, using CT scans that were taken in the last two years of the trial. These included 177 patients who were diagnosed with lung cancer via a biopsy after their final scan in the trial.
97% effective in detecting cancers
The programme identified 172 of the 177 malignant tumours in those CT scans, meaning it was 97% effective in detecting cancers. The five tumours that the programme missed were near the centre of the chest, where tumours are harder to distinguish from healthy parts of the body.
Researchers also tested the programme on scans taken a year prior to the tumours being diagnosed in the same 1179 patients and it was able to identify 152 suspicious areas that were later diagnosed as cancer.
Further studies needed
The team did, however, recognise that the programme identifies many false positives and said that this would need to be significantly improved before the programme can be used in clinics.
Audelan said: “Screening for lung cancer would mean many more CT scans being taken and we do not have enough radiologists to review them all. That’s why we need to develop computer programmes that can help. Our study shows that this programme can find possible signs of lung cancer up to a year earlier.
“The objective of our research is not to replace radiologists but to assist them by giving them a tool that can spot the earliest signs of lung cancer.”
The researchers plan to work on a new system that will be better able to differentiate between malignant and non-malignant tissue to help radiologists decide which patients should be investigated further.
Professor Joanna Chorostowska-Wynimko, who was not involved in the research, is the European Respiratory Society Secretary General and a Consultant in Respiratory Medicine at the National Institute of Tuberculosis and Lung Diseases in Warsaw, Poland. She said: “Diagnosing lung cancer earlier is vital to improving survival rates and screening would be an important step towards that aim. Research shows that screening with CT scans could reduce lung cancer deaths.
“This work is promising because it shows that AI could help us to review lots of scans quickly and even pick up signs of cancer at an earlier stage. However, before this programme can be used, researchers will need to make it better at distinguishing between lung tissue that is abnormal but benign and tissue that is probably cancer.”
