Immunotherapy with PD-1 antibodies has greatly increased prognosis of patients with advanced melanoma.,Identifying biomarkers that predict overall survival (OS) and response to immunotherapy is important.,OS and best overall response according to RECIST version 1.1 were analysed, and S100B and lactate dehydrogenase (LDH) serum levels were assessed retrospectively in 152 patients treated with anti-PD-1, and in 86 patients treated with anti-PD-1 plus anti-CTLA-4 antibodies at University Hospital Tuebingen, Germany.,In the pembrolizumab group, patients with elevated baseline S100B or LDH exhibited significantly impaired OS compared with patients with normal S100B (1-year OS: 51.1% vs 83.1%, log-rank P < .0001) and normal LDH (1-year OS: 44.4% vs 80.8%, P = .00022), respectively.,LDH increases of >25% and S100B increases of >145% compared to baseline were significantly associated with impaired OS (both P < .0001).,In patients treated with ipilimumab and nivolumab, baseline S100B and increasing S100B levels of >145% as well as baseline LDH were associated with impaired OS (P < .0001, P = .00060, and P = .0050, respectively), whereas increasing LDH of >25% was not (P = .64).,S100B could serve as a strong baseline marker for OS in melanoma patients receiving anti-PD-1 therapy.,Rising S100B levels during the first weeks of therapy could help guide treatment decisions.
Skin lesions are a severe disease globally.,Early detection of melanoma in dermoscopy images significantly increases the survival rate.,However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc.,Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists.,In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3).,A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result.,A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map.,A straight-forward CNN is proposed for the dermoscopic feature extraction task.,The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset.,Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.