scholarly journals Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Victoriya Andreeva ◽  
Evgeniia Aksamentova ◽  
Andrey Muhachev ◽  
Alexey Solovey ◽  
Igor Litvinov ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Normal Skin ◽  
Invasive Procedure ◽  
Biopsy Material ◽  
Fluorescence Diagnosis ◽  
Non Melanoma Skin Cancer ◽  
Skin Cancers ◽  
Abnormal Cells ◽  
Diagnosis Method ◽  
Melanoma Skin

The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy (FS) has been shown in many studies. This technique is rapidly expanding due to its safety, relative cost-effectiveness, and efficiency. However, skin lesion FS-based diagnosis is challenging due to a number of single overlapping spectra emitted by fluorescent molecules, making it difficult to distinguish changes in the overall spectrum and the molecular basis for it. We applied deep learning (DL) algorithms to quantitatively assess the ability of FS to differentiate between pathologies and normal skin. A total of 137 patients with various forms of primary and recurrent basal cell carcinoma (BCC) were observed by a multispectral laser-based device with a built-in neural network (NN) “DSL-1”. We measured the fluorescence spectra of suspected non-melanoma skin cancers and compared them with “normal” skin spectra. These spectra were input into DL algorithms to determine whether the skin is normal, pigmented normal, benign, or BCC. The preoperative differential AI-driven fluorescence diagnosis method correctly predicted the BCC lesions. We obtained an average sensitivity of 62% and average specificity of 83% in our experiments. Thus, the presented “DSL-1” diagnostic device can be a viable tool for the real-time diagnosis and guidance of non-melanoma skin cancer resection.

Surgical Management of Melanoma and Other Skin Cancers

2015 ◽  
Author(s):  
Jennifer A. Wargo ◽  
Kenneth Tenabe
Keyword(s):  
Lymph Node ◽  
Surgical Treatment ◽  
Skin Cancer ◽  
Cell Carcinoma ◽  
Stage Iv ◽  
Stage Iii ◽  
Non Melanoma Skin Cancer ◽  
Skin Cancers ◽  
Stage Iv Melanoma ◽  
Melanoma Skin

The prevalence of malignant skin cancers has increased significantly over the past several years. Approximately 1.2 million cases of non-melanoma skin cancer are diagnosed per year. More alarming, up to 80,000 cases of melanoma are diagnosed per year, an incidence that has been steadily increasing, with a lifetime risk of 1 in 50 for the development of melanoma. The disturbing increase in the incidence of both non-melanoma skin cancer and melanoma can largely be attributed to the social attitude toward sun exposure. The clinical assessment and management of skin lesions can be challenging. This review describes the assessment process, including thorough history and examination; the need for possible biopsy; and excision criteria. Specific types of skin cancer are distinguished and include basal cell carcinoma; squamous cell carcinoma; and melanoma; and for each type the incidence; epidemiology; histologic subtypes; diagnosis; and both surgical and non-surgical treatments are provided. Stages I-IV of melanoma are detailed, with prognostic factors described. Surgical treatment for stages I and II include description of the margins of excision and sentinel lymph node biopsy. The surgical treatment of Stage III melanoma further includes therapeutic lymph node dissection and isolated limb perfusion. Adjuvant therapies are also presented and include radiotherapy and chemotherapy. The additional treatment of metastasectomy for Stage IV melanoma is described. For both Stage III and IV melanoma, the study of vaccines to host immune cells is reported. For Stage IV melanoma, the text also describes immunotherapy treatment. Operative procedures specific to superficial and deep groin dissections are outlined. This review contains 9 figures, 3 tables, and 96 references.

Skin cancer

2018 ◽  
Author(s):  
Rubeta Matin ◽  
Jane McGregor ◽  
Catherine Harwood
Keyword(s):  
Skin Cancer ◽  
Cell Carcinoma ◽  
Cutaneous Metastasis ◽  
Primary Malignancy ◽  
Non Melanoma Skin Cancer ◽  
Skin Cancers ◽  
Cutaneous Lymphomas ◽  
The Uk ◽  
Late Stages ◽  
Melanoma Skin

Skin cancer is very common in the UK, and its incidence is rising rapidly. There are two broad classes of primary skin cancer: non-melanoma and melanoma. Non-melanoma skin cancer is the commonest form (100 000 cases diagnosed annually in the UK), accounting for nine out of ten skin cancers and includes basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). Cutaneous melanoma is less common (10 000 cases diagnosed in the UK annually) but confers a significantly worse prognosis and accounts for 75% of skin cancer related deaths. There are also a number of other, rarer, non-melanoma skin cancers (e.g. appendageal carcinomas, Merkel cell carcinoma, sarcomas, vascular malignancies, and cutaneous lymphomas); however, these account for less than 1% of all skin cancers in the UK and so will not be specifically discussed in this chapter. Cutaneous metastases can occur secondary to any internal cancer or, indeed, to skin cancer (e.g. melanoma). In most cases, cutaneous metastasis occurs after the diagnosis of a primary cancer and usually in late stages of the disease but, in some cases, it may be the first presentation, in which case it should prompt a thorough investigation for the primary malignancy.

scholarly journals The incidence and clinical analysis of non-melanoma skin cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Magdalena Ciążyńska ◽  
Grażyna Kamińska-Winciorek ◽  
Dariusz Lange ◽  
Bogumił Lewandowski ◽  
Adam Reich ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cell Carcinoma ◽  
Treatment Strategies ◽  
Cancer Registries ◽  
Clinical Analysis ◽  
Tumour Site ◽  
Non Melanoma Skin Cancer ◽  
Skin Cancers ◽  
The Face ◽  
Melanoma Skin

AbstractNon-melanoma skin cancers (NMSCs) are the most common malignancies diagnosed in Caucasian populations. Basal cell carcinoma (BCC) is the most frequent skin cancer, followed by squamous cell carcinoma (SCC). Unfortunately, most European cancer registries do not record individual types of NMSC. To evaluate the incidence of primary BCCs and SCCs regarding age, sex, tumour site and tumour subtype to determine trends in epidemiology of both cancers. Retrospective analysis of BCCs and SCCs diagnosed and treated across seven sites in Poland from 1999 to 2019. We recorded 13,913 NMSCs occurring in 10,083 patients. BCC represented 85.2% of all cases. SCC patients were older than BCC patients (77.1 ± 11.3 years vs. 70.1 ± 12.3 years, p < 0.01). The nodular subtype was the most common subtype of BCC, followed by the superficial and infiltrative subtypes. The superficial BCC subtype was more common on photoprotected areas (p < 0.01), whereas the nodular BCC subtype occurred on the face (p < 0.01). The high-risk SCC subtypes were more common on face compared to low-risk SCC subtypes (p < 0.01). BCC and SCC are common malignancies developing at various ages and anatomical sites. These data underline the need for better registration policies regarding NMSC in order to improve prevention and treatment strategies for these tumours.

scholarly journals RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3631 ◽  
Author(s):  
Van L.T. Hoang ◽  
Lisa N. Tom ◽  
Xiu-Cheng Quek ◽  
Jean-Marie Tan ◽  
Elizabeth J. Payne ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Reference Genes ◽  
Ribosomal Proteins ◽  
Data Interpretation ◽  
Rna Seq ◽  
Non Melanoma Skin Cancer ◽  
Skin Cancers ◽  
Genes Encoding ◽  
Gene Expression Studies ◽  
Melanoma Skin

Identification of appropriate reference genes (RGs) is critical to accurate data interpretation in quantitative real-time PCR (qPCR) experiments. In this study, we have utilised next generation RNA sequencing (RNA-seq) to analyse the transcriptome of a panel of non-melanoma skin cancer lesions, identifying genes that are consistently expressed across all samples. Genes encoding ribosomal proteins were amongst the most stable in this dataset. Validation of this RNA-seq data was examined using qPCR to confirm the suitability of a set of highly stable genes for use as qPCR RGs. These genes will provide a valuable resource for the normalisation of qPCR data for the analysis of non-melanoma skin cancer.

scholarly journals Vitamin D Receptor Polymorphisms and Non-Melanoma Skin Cancer Risk: A Case-Control Study

2020 ◽  
Vol 9 (12) ◽  
pp. 3819
Author(s):  
Carolina Morgado-Águila ◽  
Purificación Rey-Sánchez ◽  
Guadalupe Gil-Fernández ◽  
María Carmen Costa-Fernández ◽  
Francisco José Rodríguez-Velasco
Keyword(s):  
Vitamin D ◽  
Skin Cancer ◽  
Basal Cell ◽  
Protective Factor ◽  
Case Control Study ◽  
Case Control ◽  
Non Melanoma Skin Cancer ◽  
Skin Cancers ◽  
Control Study ◽  
Melanoma Skin

Exposure to sunlight is the major source of vitamin D and the main environmental cause of non-melanocytic skin cancers. Vitamin D, partly mediated through the vitamin D receptor (VDR), has potential therapeutic applications in skin cancer. The aim of this study was to investigate the association of BsmI and ApaI VDR polymorphisms among patients with non-melanoma cancers and controls. An observational case-control study was conducted in a sample of 154 subjects. We observed no significant effects between these polymorphisms and skin cancer risk. When stratified for gender, GG and AG BsmI polymorphisms significantly increased the risk of basal cell carcinomas in males. In relation to ApaI, all three polymorphisms significantly increased the risk of basal cell carcinoma in males. When stratified for age, we found that being 70 years of age or younger was a protective factor against both skin cancers. Being a female and 70 years old or younger was a protective factor for basal cell carcinoma. A comparison of the frequencies of the VDR genotypes in patients older than 70 years vs. 70 years or younger also revealed age-dependent variations in patients with non-melanoma skin cancer. Our study suggests a role for VDR polymorphisms in non-melanoma skin cancer development.

scholarly journals ANN-based diagnosis method for skin cancers using dermoscopic images

2021 ◽  
pp. bs202108
Author(s):  
Hamidreza Khezri ◽  
Mojtaba Farzaneh ◽  
Zeinab Ghasemishahrestani ◽  
Ali Moghadam
Keyword(s):  
Skin Cancer ◽  
Accurate Result ◽  
Low Cost ◽  
Skin Lesions ◽  
Multi Layer Perceptron ◽  
Skin Cancers ◽  
The World ◽  
Diagnosis Method ◽  
Artificial Neural Network Ann ◽  
Melanoma Skin

Melanoma is one of the most dangerous skin cancers in the world. It accounts for 55% of all deaths associated with skin cancer. Researchers believe that skin cancer increases the risk of other cancers if not diagnosed early. Therefore, prompt and timely diagnosis of this disease is very important for the successful treatment of the patient. This system can detect melanoma lethal carcinoma from other skin lesions without the need for surgery, with a low cost, accuracy of about 98.88% and specificity 99%. In this article, a new, intelligent and accurate software (Delphi) system has been used to diagnose melanoma skin cancer. To detect malignant melanoma, the ABCDT rule, asymmetry (A), boundary (B), color (C), diameter (D) and textural variation (T) of the lesion are calculated and finally, an artificial neural network (ANN) is used to obtain an accurate result. The ANN with Multi-Layer Perceptron (MLP) contains the five extraction Characteristics (ABCDT) of lesions is used as inputs, two hidden layers, and two outputs. Very good results were obtained using this method. It was observed that for a dataset of 180 dermoscopic lesion images including 80 malignant melanomas, 20 benign melanomas and 80 nevus lesions. Due to its automatic recognition and ability to be installed on a computer, this system can be very useful for dermatologists as well as the general public.

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