Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

A Comprehensive Review of Various Therapeutic Strategies for the Management of Skin Cancer

Skin cancer (SC) poses a global threat to the healthcare system and is expected to increase significantly over the next two decades if not diagnosed at an early stage. Early diagnosis is crucial for successful treatment, as the disease becomes more challenging to cure as it progresses. However, identifying new drugs, achieving clinical success, and overcoming drug resistance remain significant challenges. To overcome these obstacles and provide effective treatment, it is crucial to understand the causes of skin cancer, how cells grow and divide, factors that affect cell growth, and how drug resistance occurs. In this review, we have explained various therapeutic approaches for SC treatment via ligands, targeted photosensitizers, natural and synthetic drugs for the treatment of SC, an epigenetic approach for management of melanoma, photodynamic therapy, and targeted therapy for BRAF-mutated melanoma. This article also provides a detailed summary of the various natural drugs that are effective in managing melanoma and reducing the occurrence of skin cancer at early stages and focuses on the current status and future prospects of various therapies available for the management of skin cancer.

Assessment of RAS-RAF-MAPK Pathway Mutation Status in Healthy Skin, Benign Nevi, and Cutaneous Melanomas: Pilot Study Using Droplet Digital PCR

In the present study, we employed the ddPCR and IHC techniques to assess the prevalence and roles of RAS and RAF mutations in a small batch of melanoma (n = 22), benign moles (n = 15), and normal skin samples (n = 15). Mutational screening revealed the coexistence of BRAF and NRAS mutations in melanomas and nevi and the occurrence of NRAS G12/G13 variants in healthy skin. All investigated nevi had driver mutations in the BRAF or NRAS genes and elevated p16 protein expression, indicating cell cycle arrest despite an increased mutational burden. BRAF V600 mutations were identified in 54% of melanomas, and NRAS G12/G13 mutations in 50%. The BRAF mutations were associated with the Breslow index (BI) (p = 0.029) and TIL infiltration (p = 0.027), whereas the NRAS mutations correlated with the BI (p = 0.01) and the mitotic index (p = 0.04). Here, we demonstrate that the "young" ddPCR technology is as effective as a CE-IVD marked real-time PCR method for detecting BRAF V600 hotspot mutations in tumor biopsies and recommend it for extended use in clinical settings. Moreover, ddPCR was able to detect low-frequency hotspot mutations, such as NRAS G12/G13, in our tissue specimens, which makes it a promising tool for investigating the mutational landscape of sun-damaged skin, benign nevi, and melanomas in more extensive clinical studies.

The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

Machine learning (ML) can enhance a dermatologist's work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

Skin Cancer Pathobiology at a Glance: A Focus on Imaging Techniques and Their Potential for Improved Diagnosis and Surveillance in Clinical Cohorts

Early diagnosis is essential for completely eradicating skin cancer and maximizing patients' clinical benefits. Emerging optical imaging modalities such as reflectance confocal microscopy (RCM), optical coherence tomography (OCT), magnetic resonance imaging (MRI), near-infrared (NIR) bioimaging, positron emission tomography (PET), and their combinations provide non-invasive imaging data that may help in the early detection of cutaneous tumors and surgical planning. Hence, they seem appropriate for observing dynamic processes such as blood flow, immune cell activation, and tumor energy metabolism, which may be relevant for disease evolution. This review discusses the latest technological and methodological advances in imaging techniques that may be applied for skin cancer detection and monitoring. In the first instance, we will describe the principle and prospective clinical applications of the most commonly used imaging techniques, highlighting the challenges and opportunities of their implementation in the clinical setting. We will also highlight how imaging techniques may complement the molecular and histological approaches in sharpening the non-invasive skin characterization, laying the ground for more personalized approaches in skin cancer patients.