Artificial intelligence-based method can help predict early-stage melanoma recurrences

Artificial intelligence-based method can help predict early-stage melanoma recurrences

Most deaths from melanoma - the deadliest form of skin cancer - occur in patients who were initially diagnosed with early-stage melanoma and later experienced a recurrence, which is typically not detected until it has spread or metastasized.

A team led by researchers at Massachusetts General Hospital (MGH) recently developed an artificial intelligence-based method to predict which patients are most likely to experience a recurrence and therefore likely 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 cancer cells, but patients with advanced cancer often receive immune checkpoint inhibitors, which are effective in boosting the immune response against tumor cells but also have 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 pathological and potentially fatal adverse immunologic events observed with this class of therapeutics.”

Yevgeniy R. Semenov, MD, senior author, investigator, Department of Dermatology at MGH

“Reliable prediction of melanoma recurrence may enable more accurate treatment selection for immunotherapy, reduce metastatic disease progression, and improve melanoma survival while minimizing exposure to treatment toxicities.”

To achieve this, Semenov and his colleagues evaluated the effectiveness of algorithms based on machine learning, a branch of artificial intelligence that uses data from electronic health records to predict melanoma recurrence.

Specifically, the team collected 1,720 early-stage melanomas - 1,172 from Mass General Brigham Healthcare System (MGB) and 548 from Dana-Farber Cancer Institute (DFCI) - and extracted 36 clinical and pathological features of these cancers from electronic health records to predict the risk of recurrence of patients with mechanical Learning algorithms. Algorithms were developed and validated using different 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, which uses novel machine learning approaches to determine the risk of early-stage melanoma recurrence, achieved a high level of classification and time-to-event prediction accuracy,” says Semenov. “Our results suggest that machine learning algorithms can extract predictive signals from clinicopathological features for the prediction of early-stage melanoma recurrence, which will enable the identification of patients who may benefit from adjuvant immunotherapy.”

Source:

Massachusetts General Hospital

Reference:

Wan, G., et al. (2022) Prediction of early-stage melanoma recurrence based on clinical and histopathological features. npj Precision Oncology. doi.org/10.1038/s41698-022-00321-4.

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