A new study led by researchers at The Royal Marsden NHS Foundation Trust has found that artificial intelligence (AI) could help shape the post-treatment surveillance on non-small cell lung cancer (NSCLC) patients and improve their outcomes as a result.
In a first of its kind, the OCTAPUS-AI study compared different machine learning models to determine which could most accurately identify NSCLC patients at risk of recurrence following curative radiotherapy. Machine learning (ML) is a type of AI that allows software to automatically predict outcomes. ML algorithms build a model based on sample data to make predictions or decisions without being explicitly programmed to do so.
The study showed that AI could be used to personalise and thus improve the surveillance of patients following treatment based on their risk. This could lead to recurrence being detected earlier in high-risk patients, meaning 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.
Dr Richard Lee, Consultant Physician in Respiratory Medicine and Early Diagnosis at The Royal Marsden NHS Foundation Trust, said: “This is an important step forward in being able to use AI to understand which patients are at a highest risk of cancer recurrence, and to detect relapse sooner so that re-treatment can be more effective.”
He added: “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. 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 a much wider application.”
Lung cancer is the leading worldwide cause of cancer death and accounts for 21% of cancer deaths in the UK. NSCLC makes up 85% of lung cancer cases and, when caught early, the disease is curable. However, 36% of NSCLC patients experience recurrence in the UK.
Because of this the National Institute of Healthcare and Clinical Excellence called for more research into using prognostic factors to develop risk-stratification models to inform optimal surveillance, ultimately leading to this study.
Study lead Dr Sumeet Hindocha, Clinical Oncology Specialist Registrar at The Royal Marsden NHS Foundation Trust and Imperial College London, said: “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.”
He added: “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.”
He concluded: “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.”
The research was published in Lancet’s EbioMedicine journal and was in collaboration with The Institute of Cancer Research, Imperial College London and The Royal Marsden Cancer Charity.
For more information on the study, click here.
