Key Takeaways
- Researchers have developed and validated a machine learning–based method to predict which patients with early-stage melanoma are most likely to experience a cancer recurrence
- The tool may help identify patients who would benefit from aggressive treatment even at early stages of melanoma
BOSTON – Most deaths from melanoma—the most lethal form of skin cancer—occur in patients who were initially diagnosed with early-stage melanoma and then later experienced a recurrence that is typically not detected until it has spread or metastasized.
A team led by investigators at Massachusetts General Hospital (MGH) recently developed an artificial intelligence–based method to predict which patients are most likely to experience a recurrence and are therefore expected to benefit from aggressive treatment. The method was validated in a study published in npj Precision Oncology.
Most patients with early-stage melanoma are treated with surgery to remove cancerous cells, but patients with more advanced cancer often receive immune checkpoint inhibitors, which effectively strengthen the immune response against tumor cells but also carry significant side effects.
"There is an urgent need to develop predictive tools to assist in the selection of high-risk patients for whom the benefits of immune checkpoint inhibitors would justify the high rate of morbid and potentially fatal immunologic adverse events observed with this therapeutic class," says senior author Yevgeniy R. Semenov, MD, an investigator in the Department of Dermatology at MGH.
"Reliable prediction of melanoma recurrence can enable more precise treatment selection for immunotherapy, reduce progression to metastatic disease and improve melanoma survival while minimizing exposure to treatment toxicities."
To help achieve this, Semenov and his colleagues assessed the effectiveness of algorithms based on machine learning, a branch of artificial intelligence, that used data from patient electronic health records to predict melanoma recurrence.
Specifically, the team collected 1,720 early-stage melanomas—1,172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI)—and extracted 36 clinical and pathologic features of these cancers from electronic health records to predict patients' recurrence risk with machine learning algorithms. Algorithms were developed and validated with various MGB and DFCI patient sets, and tumor thickness and rate of cancer cell division were identified as the most predictive features.
"Our comprehensive risk prediction platform using novel machine learning approaches to determine the risk of early-stage melanoma recurrence reached high levels of classification and time to event prediction accuracy," says Semenov. "Our results suggest that machine learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients who may benefit from adjuvant immunotherapy."
Additional Mass General co-authors include Ahmad Rajeh, Michael R. Collier, Min Seok Choi, Munachimso Amadife, Kimberly Tang, Shijia Zhang, Jordan Phillips, Nora A. Alexander, Yining Hua, Wenxin Chen, Diane, Ho, Stacey Duey, and Genevieve M. Boland.
This work was supported by the Melanoma Research Alliance, the National Institutes of Health, the Department of Defense, and the Dermatology Foundation.
About the Massachusetts General Hospital
Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The Mass General Research Institute conducts the largest hospital-based research program in the nation, with annual research operations of more than $1 billion and comprises more than 9,500 researchers working across more than 30 institutes, centers and departments. In August 2021, Mass General was named #5 in the U.S. News & World Report list of "America's Best Hospitals." MGH is a founding member of the Mass General Brigham healthcare system.
