The use of Artificial intelligence (AI) may boost the outcomes for patients suffering from non-small cell lung cancer (NSCLC), following a comprehensive study.

The OCTAPUS-AI study led by researchers from The Royal Marsden NHS Foundation Trust in collaboration with The Institute of Cancer Research, London, and Imperial College London, which was supported by The Royal Marsden Cancer Charity, compared different machine learning models to determine which could most accurately identify NSCLC patients at risk of recurrence following curative radiotherapy.

“Results from the retrospective, multicentre study suggest that this technology could be used to help personalise and therefore improve the surveillance of patients following treatment based on their risk,” according to the study. “This could lead to recurrence being detected earlier in high-risk patients, ensuring that they receive urgent treatment which could potentially improve their outcomes. For those with a low risk of recurrence, it could result in fewer follow-up scans and hospital visits.”

The researchers used anonymised, routinely available clinical data from 657 NSCLC patients treated at five UK hospitals to compare different machine learning algorithms based on various prognostic factors – used to predict a patient’s chance of recurrence – such as age, gender, and the tumour’s characteristics on scans. They then developed and tested prediction models to categorise patients into low and high risk of recurrence, recurrence-free survival, and overall survival at two years post treatment.

Lung cancer is the leading worldwide cause of cancer death and accounts for just over a fifth (21%) of cancer deaths in the UK. NSCLC makes up nearly five sixths (85%) of lung cancer cases and, when caught early, the disease is often curable. However, over a third (36%) of NSCLC patients experience recurrence in the UK.

To improve the outcomes of lung cancer patients, the National Institute of Healthcare and Clinical Excellence (NICE) has called for further research into using prognostic factors to develop risk-stratification models to inform optimal surveillance. The study was developed in response to this recommendation.

Dr Sumeet Hindocha, study lead and clinical oncology specialist registrar at The Royal Marsden NHS Foundation Trust and Imperial College London explained: “Right now, there is no set framework for the surveillance of non-small cell lung cancer patients following radiotherapy treatment in the UK.

“This means there is variation in the type and frequency of follow-up that patients receive. More research is required to develop personalised follow-up protocols and using AI with healthcare data may be the answer.

“This study shows that machine learning models can predict NSCLC patients’ outcomes following curative radiotherapy using routinely available clinical data. As this type of data can be accessed easily, this methodology could be replicated across different health systems. This study is therefore an exciting first step towards developing a model to help guide the post-treatment surveillance of this patient group based on their individual risk of recurrence.

“The next phase of this study will test machine learning models using imaging data alone and in combination with clinical data. We hope to find out how our model, which is based on patient characteristics and the treatment they received, is influenced by imaging scan data.”

This study was supported by the Early Diagnosis and Detection Centre which aims to accelerate early diagnosis of cancer. The Centre has been established in partnership with The Institute of Cancer Research (ICR), hosted by the National Institute for Health Research Biomedical Research Centre (NIHR BRC) and is supported by funding from The Royal Marsden Cancer Charity.

The Early Diagnosis and Detection Centre brings together early detection research and expertise across multiple tumour groups, with Royal Marden Cancer Charity funding supporting the recruitment of new specialist roles and the setup of a new clinical trials infrastructure. It focuses on identifying higher risk groups, who will then benefit from AI, imaging, and novel liquid biopsy technologies, helping to detect cancers earlier, faster and to give a more accurate diagnosis.

Dr Richard Lee, consultant physician in respiratory medicine and early diagnosis at The Royal Marsden NHS Foundation Trust, who is funded by The Royal Marsden Cancer Charity, and is chief investigator for the OCTAPUS-AI study, said: “This is an important step forward in being able to use AI to understand which patients are at highest risk of cancer recurrence, and to detect this relapse sooner so that re-treatment can be more effective.

“Relapse is also a key source of anxiety for patients. Reducing the number of scans needed in this setting can be helpful, and also reduce radiation exposure, hospital visits, and make more efficient use of valuable NHS resources.

“This study is an example of the vital scientific clinical research we’re undertaking in the Early Diagnosis and Detection centre at The Royal Marsden. Through this work, we hope to push boundaries to improve the care of cancer patients, to help them live longer, and reduce the impact the disease has on their lives. We are grateful to our patients and donors who have made this research possible.”

“In the future, we hope this approach will pave the way for predicting recurrence for all cancer types, not just NSCLC. Our model used features specific to this disease but by refining the algorithm, this technology could have much wider application.”

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