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.
Belgique Dernières Nouvelles, Belgique Actualités
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