Epidemiology of Melanoma

Matthews, Natalie H.; Li, Wenqing; Qureshi, Abrar A.; Weinstock, Martin A.; Cho, Eunyoung

doi:10.15586/codon.cutaneousmelanoma.2017.ch1

Cited by 268 publications

(361 citation statements)

References 106 publications

(179 reference statements)

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“…Every year, nearly 17 million people are affected by cancer and about 9.6 million people die due to delayed diagnosis and treatment [3]. This makes cancer the leading causes of death worldwide [4]. In the case of skin cancer, it is one of the most prevalent types of the disease in both adults and children [5] and occurs or originates in the epidermal tissue.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Zafar

Gilani

Waris

et al. 2020

Sensors

102

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Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.

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Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Zafar

Gilani

Waris

et al. 2020

Sensors

102

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“…Cutaneous melanoma is one of the most aggressive types of cancer worldwide, with an incidence that has been increasing over the past 50 years [1].…”

Section: Introductionmentioning

confidence: 99%

miRNAs as Key Players in the Management of Cutaneous Melanoma

Lorusso

Summa

Pinto

et al. 2020

Cells

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The number of treatment options for melanoma patients has grown in the past few years, leading to considerable improvements in both overall and progression-free survival. Targeted therapies and immune checkpoint inhibitors have opened a new era in the management of melanoma patients. Despite the clinical advances, further research efforts are needed to identify other "druggable" targets and new biomarkers to improve the stratification of melanoma patients who could really benefit from targeted and immunotherapies. To this end, many studies have focused on the role of microRNAs (miRNAs) that are small non-coding RNAs (18-25 nucleotides in length), which post-transcriptionally regulate the expression of their targets. In cancer, they can behave either as oncogenes or oncosuppressive genes and play a central role in many intracellular pathways involved in proliferation and invasion. Given their modulating activity on the transcriptional landscape, their biological role is under investigation to study resistance mechanisms. They are able to mediate the communication between tumor cells and their microenvironment and regulate tumor immunity through direct regulation of the genes involved in immune activation or suppression. To date, a very promising miRNA-based strategy is to use them as prognosis and diagnosis biomarkers both as cell-free miRNAs and extracellular-vesicle miRNAs. However, miRNAs have a complex role since they target different genes in different cellular conditions. Thus, the ultimate aim of studies has been to recapitulate their role in melanoma in biological networks that account for miRNA/gene expression and mutational state. In this review, we will provide an overview of current scientific knowledge regarding the oncogenic or oncosuppressive role of miRNAs in melanoma and their use as biomarkers, with respect to approved therapies for melanoma treatment.

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“…In the last 50 years, the incidence of malignant melanoma has increased faster than almost any other cancer and it represents a public health matter in many countries due to its high rate of mortality. 1,2 Although early stage melanoma is curable with surgical resection alone, it is an aggressive malignancy that tends to metastasize beyond its primary site; until the recent introduction of novel therapies the 5-year survival rates of advanced melanoma were very poor (ranging from 5% to 19%). 3 In fact, patients with metastatic melanoma (MM) are highly refractory to conventional chemotherapies and survival improvements have been not relevant with these therapies.…”

Section: Introductionmentioning

confidence: 99%

Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process–A systematic review

et al. 2020

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Advanced malignant melanoma represents a public health matter due to its rising incidence and aggressiveness. Novel therapies such as immunotherapy are showing promising results with improved progression free and overall survival in melanoma patients. However, novel targeted and immunotherapies could generate atypical patterns of response which are nowadays a big challenge since imaging criteria (ie Recist 1.1) have not been proven to be always reliable to assess response. Radiomics and in particular texture analysis (TA) represent new quantitative methodologies which could reduce the impact of these limitations providing most robust data in support of clinical decision process. The aim of this paper was to review the state of the art of radiomics/TA when it is applied to the imaging of metastatic melanoma patients.

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Epidemiology of Melanoma

Cited by 268 publications

References 106 publications

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

miRNAs as Key Players in the Management of Cutaneous Melanoma

Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process–A systematic review

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