Alexis is director of content at AIMed, with responsibility for the research, development and delivery of products across events, digital and publishing. A highly experienced events executive with a career focus on the intersection between healthcare and technology, he is also a school governor leading on teaching, learning, and quality of education.
Artificial intelligence could help guide the post-treatment surveillance of non-small cell lung cancer (NSCLC) patients and improve outcomes as a result, according to a 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.
In a world first, the OCTAPUS-AI study, published in the Lancet’s EbioMedicine journal, 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, multicenter study suggest that this technology could be used to help personalize and therefore improve the surveillance of patients following treatment based on their risk. 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 anonymized, 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 categorize patients into low and high risk of recurrence, recurrence-free survival, and overall survival at two years post treatment.
For example, researchers found that the patient’s tumour size and stage, type, and intensity of radiotherapy, smoking status, BMI and age were the most important factors in the final model’s algorithm for predicting patient outcomes.
This model was also found to be more accurate in predicting outcomes than traditional methods such as the TNM staging system, which describes the amount and spread of cancer in a patient’s body.
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 personalized 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.”
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.”
Dr Merina Ahmed, Consultant Clinical Oncologist at The Royal Marsden and a senior author on the study, said:
“The TNM staging system is currently the best tool at our disposal for predicting a cancer patient’s risk of recurrence. However, we’re always looking for ways to improve our prognostic capabilities and, by including other factors alongside TNM, this study signals a combined approach using AI may work better.
“Right now, the type and frequency of follow-up that patients with non-small cell lung cancer receive following radiotherapy treatment varies significantly, both nationally and internationally, with guidelines varying across the world. For example, some patients are offered a chest x-ray while others may be given a CT scan. Some centres follow-up with patients every three months in the first couple of years, while others may do so less frequently.”