AI Approach may Help Identify Melanoma Survivors who Face a High Risk of Cancer Recurrence

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."

Wan, G., Nguyen, N., Liu, F. et al.
Prediction of early-stage melanoma recurrence using clinical and histopathologic features.
npj Precis. Onc. 6, 79, 2022. doi: 10.1038/s41698-022-00321-4

Most Popular Now

AI Distinguishes Glioblastoma from Look-…

A Harvard Medical School–led research team has developed an AI tool that can reliably tell apart two look-alike cancers found in the brain but with different origins, behaviors, and treatments. The...

AI Body Composition Measurements can Pre…

Adiposity - or the accumulation of excess fat in the body - is a known driver of cardiometabolic diseases such as heart disease, stroke, type 2 diabetes, and kidney disease...

AI can Strengthen Pandemic Preparedness

How to identify the next dangerous virus before it spreads among people is the central question in a new Comment in The Lancet Infectious Diseases. In it, researchers discuss how...

New AI Tool Scans Social Media for Hidde…

A new artificial intelligence tool can scan social media data to discover adverse events associated with consumer health products, according to a study published September 30th in the open-access journal...

'Future-Guided' AI Improves Se…

In the world around us, many things exist in the context of time: a bird’s path through the sky is understood as different positions over a period of time, and...

Yousif's Story with Sectra and The …

Embarking on healthcare technology career after leaving his home as a refugee during his teenage years, Yousif is passionate about making a difference. He reflects on an apprenticeship in which...

New AI Tools Help Scientists Track How D…

Artificial intelligence (AI) can solve problems at remarkable speed, but it’s the people developing the algorithms who are truly driving discovery. At The University of Texas at Arlington, data scientists...

AI Tool Offers Deep Insight into the Imm…

Researchers explore the human immune system by looking at the active components, namely the various genes and cells involved. But there is a broad range of these, and observations necessarily...

Study Finds One-Year Change on CT Scans …

Researchers at National Jewish Health have shown that subtle increases in lung scarring, detected by an artificial intelligence-based tool on CT scans taken one year apart, are associated with disease...

New Antibiotic Targets IBD - and AI Pred…

Researchers at McMaster University and the Massachusetts Institute of Technology (MIT) have made two scientific breakthroughs at once: they not only discovered a brand-new antibiotic that targets inflammatory bowel diseases...

Highland to Help Companies Seize 'N…

Health tech growth partner Highland has today revealed its new identity - reflecting a sharper focus as it helps health tech companies to find market opportunities, convince target audiences, and...