AI can determine the course and severity of aggressive skin cancers, such as Merkel cell carcinoma (MCC), to help clinical decision making by generating personalised predictions of treatment specific outcomes for patients and their doctors.
An international team, led by Newcastle researchers , combined machine learning with clinical expertise to develop a web-based system called “DeepMerkel” which offers the power to predict MCC treatment specific outcomes based on personal and tumour specific features.
They propose that this system could be applied to other aggressive skin cancers for precision prognostication, the enhancement of informed clinical decision making and improved patient choice.
MCC
MCC is a rare but highly aggressive skin cancer. It can be difficult to treat and usually affects older adults with weakened immune systems. It can spread quickly to other parts of the body and has a poor survival rate.
Mr Tom Andrew, a plastic surgeon and Cancer Research UK-funded PhD student at Newcastle University and first author said; “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.
“Using AI allowed us to understand subtle new patterns and trends in the data which meant on an individual level, we are able to 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 with Penny Lovat, Professor of Dermato-oncology, Newcastle University, and Mr Aidan Rose, Senior Clinical Lecturer, Newcastle University and Consultant Plastic Surgeon at Newcastle Hospitals NHS Foundation Trust.
Mr Rose said; “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 sometime 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 complementary publications in Nature Digital Medicine and the Journal of the American Academy of Dermatology, the team describe how using advanced statistical and machine learning methods they developed the web-based prognostic tool for MCC.
MCC
In Nature Digital Medicine, the team describe how they employed explainability analysis and the data of to reveal new insights into mortality risk factors for the highly aggressive cancer, MCC. They then combined deep learning feature selection with a modified XGBoost framework, to develop a web-based prognostic tool for MCC which they termed DeepMerkel.
Analysing the data from nearly 11,000 patients in two countries, the researchers describe in the Journal of the American Academy of Dermatology how DeepMerkel was able to accurately identify high-risk patients at an earlier stage of the cancer. This allows medics to make more informed decisions about when to use radical treatment options and intensive disease monitoring.
The team hope that DeepMerkel will provide better information for patients to make decisions with their medical teams about the best treatment for them as an individual.
Mr 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 next step is to integrate the DeepMerkel website into routine clinical practice and broaden the scope of its use into other tumour types.
- Newcastle University and Newcastle Hospitals are both part of Newcastle Health Innovation Partners (NHIP). NHIP is one of eight prestigious Academic Health Science Centres (AHSCs) across the UK, bringing together partners to deliver excellence in research, health education and patient care.
