Artificial intelligence approach may help identify melanoma survivors who face a high risk of cancerrecurrence nresearchnews
are necessary to evaluate the performance of a more objective mitotic rate variable in predicting melanoma recurrence. Despite this limitation, mitotic rate was nevertheless consistently selected as a top performing feature in our models. It is also possible that some of the non-recurrent melanomas included in our study experienced recurrence after the end of study follow-up .
In summary, we demonstrate consistent and reliable performance of machine-learning models for predicting melanoma recurrence in the largest cohort of patients diagnosed with early-stage melanomas to date. We delineate the most significant features for recurrence prediction, which include Breslow thickness, mitotic rate, AJCC stage, median income, insurance type, and age at diagnosis.
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