Mucosal melanoma is an aggressive melanoma with poor prognosis.,We assessed efficacy of pembrolizumab in patients with advanced mucosal melanoma in KEYNOTE-001 (NCT01295827), −002 (NCT01704287), and −006 (NCT01866319).,Patients received pembrolizumab 2 mg/kg every 3 weeks (Q3W) or 10 mg/kg Q2W or Q3W.,Response was assessed by independent central review per RECIST v1.1.,1567 patients were treated and 84 (5%) had mucosal melanoma.,Fifty-one of 84 were ipilimumab-naive.,In patients with mucosal melanoma, the objective response rate (ORR) was 19% (95% CI 11-29%), with median duration of response (DOR) of 27.6 months (range 1.1 + to 27.6).,Median progression-free survival (PFS) was 2.8 months (95% CI 2.7-2.8), with median overall survival (OS) of 11.3 months (7.7-16.6).,ORR was 22% (95% CI 11-35%) and 15% (95% CI 5-32%) in ipilimumab-naive and ipilimumab-treated patients.,Pembrolizumab provides durable antitumour activity in patients with advanced mucosal melanoma regardless of prior ipilimumab.
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.