Research News

Collaborators at Cleveland Clinic and the University of Washington recently published a online risk calculator for recurrence in Merkel cell carcinoma, a rare, aggressive skin cancer that comes back after treatment in 40% of patients. 

UW Merkel cell carcinoma researchers wanted to translate new findings about what puts a patient at high risk to a publicly available tool for patients and clinicians. To do so, they partnered with Cleveland Clinic's Michael Kattan, PhD, the Dr. Keyhan and Dr. Jafar Mobasseri Endowed Chair for Innovations in Cancer Research.

Because Merkel cell carcinoma recurs so frequently, patients and their clinicians need to work together to catch any new malignant cells before the cancer spreads. Predictive models for specific types of cancer can inform proactive care and demonstrate to patients the importance of regular screening. Making risk calculators publicly available, though, requires extra thought to how cancer patients will interpret and navigate their risk, Dr. Kattan says.

"When you're designing tools for a physician, they know what certain terms mean and so it's a bit more straightforward," Dr. Kattan says. "For a tool that is accessible for both physicians and patients, you really need to think about the terms you're using and build something user-friendly. These applications can help patients learn more about their health – but only if they are accessible." 

The most important factor in whether Merkel cell carcinoma will recur is the stage at diagnosis, determined through the size of the tumor and whether cancerous cells have spread to other parts of the body. The UW team, led by researcher and dermatologist Paul Nghiem, MD, PhD, investigated whether demographic or clinical factors outside of the initial stage would help in predicting recurrence. 

First author Aubriana McEvoy, MD, and the UW research team designed and conducted extensive patient data analysis. They identified immunosuppression, male sex and an unknown primary tumor site as the influential factors outside of stage. Researchers then turned to Dr. Kattan to assist in designing the risk calculator, which needed to accurately reflect the clinical influence of individual factors. 

Their models were more accurate in calculating risk levels and predicting recurrence than those solely predicting based on stage, according to results published in the Journal of the Academy of American Dermatology. In addition to presenting an accurate model, the researchers then needed to design a page that clinicians and patients could navigate easily. 

Dr. Kattan's expertise is building predictive models to guide medical decision-making. These models incorporate many factors to personalize predictions, like cancer subtype or treatment type. These models aren't intended as standalone medical advice, but they can give a patient a starting point for understanding their disease, Dr. Kattan says. 

Accessibility includes instructions on how to use the tool and guiding the patient to the information most useful to them. 

"Physicians might look at these models and fixate on what's relevant to them, not necessarily to the patient," says Dr. Kattan, chair of Cleveland Clinic's Quantitative Health Sciences department. "Patients want to know what their options and risks are, but communicating the nuances of these models can be a challenge. You need to build in guidance and opportunities for patients to ask for help."