For many years, the standard therapy for malignant melanoma was based mainly on surgical resection. Unfortunately, this treatment is curative only in the early localized stage of this malignancy. The metastatic stage of malignant melanoma still remains a huge therapeutic challenge. Despite the many new therapeutic options that have become available over the last years, there is a constant need for safer and more effective treatment modalities. There has been a dynamic development of various anti-cancer immunotherapies directed against new molecular targets. A number of clinical trials are currently being conducted to confirm their effectiveness and safety. In this review of the literature, we summarize the contemporary knowledge on promising new immunotherapies beyond the currently available treatment options for malignant melanoma, including oncolytic immunotherapy, selective inhibitors of indoleamine 2,3-dioxygenease, anti-PD-(L)1 (programmed death ligand 1) drugs, immune checkpoint protein LAG-3 antibodies, inhibitors of histone deacetylase (HDAC) and inhibitors of B7-H3.
Introduction
Convolutional neural networks gained popularity due to their ability to detect and classify objects in images and videos. It gives also an opportunity to use them for medical tasks in such specialties like dermatology, radiology or ophthalmology. The aim of this study was to investigate the ability of convolutional neural networks to classify malignant melanoma in dermoscopy images.
Aim
To examine the usefulness of deep learning models in malignant melanoma detection based on dermoscopy images.
Material and methods
Four convolutional neural networks were trained on open source dataset containing dermoscopy images of seven types of skin lesions. To evaluate the performance of artificial neural networks, the precision, sensitivity, F1 score, specificity and area under the receiver operating curve were calculated. In addition, an ensemble of all neural networks’ ability of proper malignant melanoma classification was compared with the results achieved by every single network.
Results
The best convolutional neural network achieved on average 0.88 precision, 0.83 sensitivity, 0.85 F1 score and 0.99 specificity in the classification of all skin lesion types.
Conclusions
Artificial neural networks might be helpful in malignant melanoma detection in dermoscopy images.
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