Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300
Approximately 50% of all melanomas harbor an activating BRAF mutation. In patients suffering from an advanced melanoma with such a somatic alteration, combined targeted therapy with a BRAF and MEK inhibitor can be applied to significantly increase the survival probability. Nevertheless, resistance mechanisms, as well as negative predictive biomarkers (elevated lactate dehydrogenase levels, high number of metastatic organ disease sites, brain metastasis), remain a major problem in treating melanoma patients. Recently, a landmark overall survival (OS) rate of 34% after 5 years of combined targeted therapy in treatment-naïve patients was reported. On the other hand, patients harboring a BRAF mutation and receiving first-line immune checkpoint blockade with ipilimumab plus nivolumab showed a 5-year OS rate of 60%. As indicated by these data, long-term survival can be reached in melanoma patients but it remains unclear if this is equivalent to reaching a true cure for metastatic melanoma. In this review, we summarize the recent results for combined targeted therapy and immunotherapy in advanced melanoma harboring an activating BRAF mutation and discuss the impact of baseline characteristics on longterm outcome.
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