AI Tool “DeepMerkel” to improve prognosis and treatment predictions for aggressive skin cancers
Newcastle University research team develops AI system to personalise treatment outcomes for Merkel cell carcinoma and other aggressive skin cancers
A team of researchers led by Newcastle University has demonstrated how Artificial Intelligence (AI) can help predict the course and severity of aggressive skin cancers, such as Merkel cell carcinoma (MCC). Their findings, published in two academic papers, show that AI can enhance clinical decision-making by generating personalised predictions of treatment-specific outcomes for both patients and doctors.
The international team combined machine learning with clinical expertise to create a web-based system called “DeepMerkel.” This system predicts MCC treatment-specific outcomes based on individual and tumour-specific features.
According to the statement, the team believes that this AI-driven system could be extended to other aggressive skin cancers to improve precision prognostication, clinical decision-making, and patient choice.
Dr Tom Andrew, a Plastic Surgeon and CRUK-funded PhD student at Newcastle University and first author, explained, “DeepMerkel is allowing us to predict the course and severity of a Merkel cell carcinoma enabling us to personalise treatment so that patients are getting the optimal management.”
He added, “Using AI allowed us to understand subtle new patterns and trends in the data which meant on an individual level, we can provide more accurate predictions for each patient. This is important as in the 20 years up to 2020, the number of people diagnosed with this cancer has doubled and while it is still rare, it is an aggressive skin cancer which is increasingly affecting older people.”
The research was conducted in collaboration with Penny Lovat, Professor of Dermato-oncology at Newcastle University, and Dr Aidan Rose, Senior Clinical Lecturer at Newcastle University and Consultant Plastic Surgeon at Newcastle Hospitals NHS Foundation Trust.
Dr Rose noted, “Being able to accurately predict patient outcomes is critical when guiding clinical decision making. This is particularly important when treating aggressive forms of skin cancer in a complex patient group which typically results in difficult, and sometimes life-changing, choices being made regarding treatment options. The developments we have made using AI allow us to provide personalised survival predictions and inform a patient’s medical team of the optimal treatment.”
In two papers published in Nature Digital Medicine and the Journal of the American Academy of Dermatology, the team described how they used advanced statistical and machine learning methods to develop DeepMerkel.
In Nature Digital Medicine, the team explained how they used explainability analysis to uncover new insights into mortality risk factors for MCC. They combined deep learning feature selection with a modified XGBoost framework to create the web-based tool.
The team then analysed data from nearly 11,000 patients across two countries, as described in the Journal of the American Academy of Dermatology. This analysis showed that DeepMerkel could identify high-risk patients at an earlier stage of cancer, allowing healthcare providers to make more informed decisions regarding the use of radical treatments and intensive disease monitoring.
The team aims for DeepMerkel to offer better information for patients, helping them collaborate with medical teams to make informed decisions about treatment options.
Dr Andrew added, “With further investment, the exciting next step for our team is to further develop DeepMerkel so that the system can present options to help advise clinicians on the best treatment pathway open to them.”
The team’s next step is to integrate DeepMerkel into routine clinical practice and expand its use to other tumour types.
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