Machine learning tools can improve the personalised prognostication of aggressive skin cancers, such as Merkel cell carcinoma (MCC), according to a new study.
MCC is the most aggressive form of skin cancer, often presenting in advanced stages. Currently, no personalised prognostication tools exist, and survival rates are poor. As such, artificial intelligence (AI), including machine learning, is being utilised to address and improve the clinical management of skin cancers such as MCC.
In this study, researchers employed a two-step approach, using advanced machine learning techniques, to develop a diagnostic tool called DeepMerkel. The data came from two sources: the SEER database – a large cancer database maintained by the US National Cancer Institute (NCI), and a UK dataset. Together, they involved over 10,000 patients, all of whom had histologically confirmed MCC during the study period.
Firstly, using explainability techniques, they were able to determine patterns in clinical data such as tumour size, age and immune status. This revealed which factors were most influential on survival in MCC patients.
The second step used combined deep learning feature selection, which meant the machine learnt to automatically select factors that were most important for predicting survival. It was combined with a modified XGBoost framework – an algorithm to structure the data – allowing time-dependent predictions to be made.
The machine learning tool was tested on an international clinical cohort and it made accurate, personalised, time-dependent survival predictions for MCC from readily available clinical information. Retaining accuracy across a wide range of patient groups highlights the broad-reach potential of the new tool.
It outperformed current population-based prognostic staging systems, such as the American Joint Committee on Cancer (AJCC) staging system. Considered the clinical gold standard, the AJCC predicts outcomes using general population data rather than personalised risk factors.
The new machine learning tool also accurately predicted disease-specific survival (DSS) at five years. Additionally, it was able to differentiate time-to-death, providing greater clinical insight and allowing clinicians to personalise treatments more effectively. They could also adjust patient care to improve outcomes, manage symptoms and, where appropriate, make decisions about end-of-life care.
The researchers described MCC and DeepMerkel as ‘the exemplar model’ of personalised machine learning prognostic tools in aggressive skin cancers, highlighting the potential for AI-driven approaches in other areas of oncology.
Reference
Andrew, T et al. A hybrid machine learning approach for the personalized prognostication of aggressive skin cancers. npj Digit. Med. 2025, Jan. 08: DOI: 10.1038/s41746-024-01329-9.
