Journal of Cancer Diagnosis
Open Access

Skin cancer; Diagnosis; Dermoscopy; Early detection; Artificial intelligence; Histopathology; Clinical examination; Molecular techniques

Introduction

Skin cancer is a significant and growing global health concern, with its incidence rates steadily increasing over the years. It is the most common type of cancer worldwide, and its early detection plays a crucial role in successful management and treatment outcomes. The timely diagnosis of skin cancer allows for prompt intervention, reducing morbidity and potentially saving lives. Consequently, advancements in skin cancer diagnostic techniques have been a topic of intense research and development. The primary objective of skin cancer diagnosis is to accurately identify malignant lesions while minimizing unnecessary procedures and reducing the risk of missed diagnoses. Traditionally, diagnosis relied heavily on visual inspection and subjective clinical judgment. However, with the advent of technology and medical advancements, several diagnostic tools and methods have emerged to improve accuracy, efficiency, and reproducibility [1].

This comprehensive review aims to provide an overview of the advancements in skin cancer diagnosis. The review will cover various diagnostic techniques, including clinical examination, dermoscopy, histopathology, and emerging technologies such as artificial intelligence (AI) and molecular techniques. By exploring the strengths and limitations of each method, this review will offer insights into the current state-of-the-art diagnostic approaches for skin cancer and shed light on future directions in this field. With a better understanding of the advancements in skin cancer diagnosis, healthcare professionals can make informed decisions regarding patient management, leading to improved outcomes and reduced healthcare burden. Additionally, policymakers and researchers can identify gaps in existing diagnostic approaches and direct efforts toward developing novel techniques that address these limitations. Ultimately, this review aims to contribute to the on-going efforts to enhance early detection and treatment of skin cancer, thereby positively impacting public health [2].

Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. When diagnosed at an early stage, skin cancer is highly curable, with a range of treatment options available that can prevent its progression and potential metastasis. However, delays in diagnosis can result in the disease advancing to more advanced stages, making treatment more challenging and reducing the chances of a positive prognosis. Traditionally, skin cancer diagnosis relied heavily on visual inspection and subjective clinical judgment. Dermatologists and healthcare professionals would evaluate skin lesions using the ABCDE rule (asymmetry, border irregularity, color variation [3], diameter greater than 6 mm, and evolving over time) and other clinical examination tools. While these methods are valuable and form the basis of initial assessments, they have limitations in terms of accuracy and reliability, leading to both false positives and false negatives. In recent years, significant advancements have been made in skin cancer diagnostic techniques, driven by technological advancements and a deeper understanding of the disease. These advancements aim to enhance the accuracy and efficiency of skin cancer diagnosis, enabling healthcare professionals to make more informed decisions and provide timely interventions [4].

This comprehensive review aims to provide an overview of the advancements in skin cancer diagnosis. It will explore various diagnostic techniques, including clinical examination, dermoscopy, histopathology, and emerging technologies such as artificial intelligence (AI) and molecular techniques. By examining the strengths and limitations of each method, this review will highlight their contributions to improving diagnostic accuracy, reducing unnecessary procedures, and guiding treatment decisions. Furthermore, the review will discuss the challenges and limitations associated with current diagnostic approaches, such as false positives, false negatives, and limited access to advanced diagnostic tools [5]. Ethical considerations, cost implications, and resource requirements will also be addressed. By understanding these challenges, researchers and healthcare professionals can identify areas for improvement and direct their efforts towards developing more effective and accessible diagnostic techniques. Lastly, the review will outline future directions in skin cancer diagnosis, including the integration of AI and molecular techniques, the development of noninvasive diagnostic tools, personalized risk assessment approaches, and the potential for telemedicine and remote diagnosis. These future directions hold promise for further enhancing skin cancer diagnosis and improving patient outcomes, advancements in skin cancer diagnosis have revolutionized the field, offering improved accuracy and efficiency in detecting malignant lesions. With a better understanding of the current state-of-the-art diagnostic approaches and future directions, healthcare professionals can employ more effective strategies for early detection and treatment, ultimately reducing the burden of skin cancer on individuals and society as a whole [6-8].

Materials and Methods

This section outlines the materials and methods employed in conducting the research review on skin cancer diagnosis. The objective of the study was to comprehensively analyze and present the advancements in diagnostic techniques for skin cancer. The review was conducted through a systematic and comprehensive literature search. Various electronic databases, including PubMed, Google Scholar, and Scopus, were utilized to identify relevant articles published between the years 2000 and 2023 [9]. The search strategy incorporated a combination of keywords such as “skin cancer diagnosis,” “clinical examination,” “dermoscopy,” “histopathology,” “artificial intelligence,” and “molecular techniques.” Inclusion criteria were established to select articles relevant to the scope of the review. Only articles written in English and peer-reviewed were considered. Studies focusing on advancements in skin cancer diagnosis, including clinical examination, dermoscopy, histopathology, and emerging technologies, were included. Exclusion criteria comprised articles unrelated to skin cancer diagnosis, studies conducted on animal models, and non-peer-reviewed publications [10].

The selected articles were carefully reviewed and analyzed to extract pertinent information regarding the advancements in skin cancer diagnostic techniques. Data were collected on the principles, methodologies, and applications of each diagnostic approach. Key findings, including the strengths, limitations, and challenges associated with each technique, were documented. The materials used in the study included a computer with internet access and various scientific databases. Data collection and analysis were performed using reference management software and spread sheet applications [11]. The research team involved in the study comprised experts in dermatology, oncology, and medical research, ensuring a multidisciplinary approach to data interpretation. To ensure the accuracy and reliability of the information presented, the selected articles underwent a rigorous quality assessment. The quality of evidence, study design, sample size, and statistical analysis methods were considered during the evaluation process. Any discrepancies or disagreements among the research team regarding the interpretation of findings were resolved through discussions and consensus [12].

The findings obtained from the review were organized and presented in a coherent and systematic manner. The results were categorized according to the different diagnostic techniques, including clinical examination, dermoscopy, histopathology, and emerging technologies. The strengths, limitations, challenges, and future directions for each technique were discussed and critically analyzed. In summary, the materials and methods employed in this research review involved a comprehensive literature search, systematic article selection based on inclusion criteria, data extraction, quality assessment, and data analysis. The multidisciplinary research team ensured a thorough and balanced analysis of the advancements in skin cancer diagnosis. The findings provide valuable insights into the current state-of-the-art diagnostic techniques and highlight areas for future research and development in this field [13].

Discussion

Clinical examination remains an essential component of skin cancer diagnosis, allowing healthcare professionals to visually assess skin lesions and identify potential indicators of malignancy. The ABCDE rule and other clinical examination tools provide a systematic approach to evaluating skin lesions based on asymmetry, border irregularity, color variation, diameter, and evolution over time. While these tools serve as a valuable initial assessment, they have limitations in terms of subjectivity and variability among observers. Additionally, they may not always be reliable for detecting early-stage or atypical skin cancers. Therefore, clinical examination should be complemented with other diagnostic techniques to improve diagnostic accuracy [14].

Dermoscopy, also known as dermoscopy or epiluminescence microscopy, is a non-invasive technique that allows for magnified examination of skin lesions. It enables healthcare professionals to visualize structures beneath the skin surface and observe characteristic patterns and features associated with different types of skin cancer. Dermoscopy has demonstrated higher sensitivity and specificity compared to naked-eye examination alone. Over the years, new dermoscopic algorithms and automated analysis systems have been developed to assist in lesion evaluation, further enhancing diagnostic accuracy. Dermoscopy plays a crucial role in the early detection and differentiation of melanoma, as well as other non-melanoma skin cancers, improving diagnostic precision and reducing unnecessary excisions [15].

Histopathological examination of skin biopsy samples remains the gold standard for diagnosing skin cancer and determining its subtype and stage. Excisional, incisional, shave, and punch biopsies, along with frozen section analysis, allow for the microscopic evaluation of tissue samples. Histopathology provides detailed information about the cellular characteristics, architecture, and invasion depth of the lesion, aiding in accurate diagnosis and guiding appropriate treatment decisions. Immunohistochemistry techniques can further enhance the diagnostic accuracy by assessing specific molecular markers. However, histopathology is an invasive procedure that requires expertise and time, and it may not always be feasible for every suspicious lesion. There is a need for less invasive and more accessible diagnostic alternatives to complement histopathology [16].

AI has made significant strides in improving skin cancer diagnosis. Deep learning algorithms have demonstrated remarkable performance in classifying skin lesions and distinguishing between benign and malignant cases. Computer-aided diagnosis (CAD) systems have been developed to assist healthcare professionals in analyzing dermoscopic images or histopathological slides, offering a second opinion and reducing inter observer variability. AI-based systems have shown promise in achieving high accuracy and efficiency, potentially aiding in early detection and reducing unnecessary biopsies [17]. Molecular techniques have emerged as promising tools for skin cancer diagnosis. Gene expression profiling and next-generation sequencing enable the identification of genetic alterations and molecular signatures associated with different types and stages of skin cancer. These techniques offer insights into the biological behavior of skin lesions, aiding in prognosis prediction, treatment selection, and monitoring of treatment response. Furthermore, circulating tumor DNA analysis shows potential for non-invasive detection and monitoring of skin cancer, providing a less invasive alternative to tissue biopsies. However, molecular techniques are still under development and require further validation before widespread clinical implementation [18].

Skin cancer diagnosis still faces several challenges and limitations. False positives and false negatives can occur with various diagnostic techniques, leading to unnecessary procedures or missed diagnoses. Access to advanced diagnostic tools, such as dermoscopy devices or molecular testing platforms, may be limited in certain regions, impacting timely and accurate diagnosis. Cost implications and resource requirements for implementing advanced diagnostic techniques can be substantial, posing barriers to widespread adoption. Ethical considerations, such as patient privacy and data security, must be addressed when using emerging technologies like AI. Further research and development are needed to address these challenges and ensure equitable access to accurate and efficient skin cancer diagnosis [19].

The future of skin cancer diagnosis lies in the integration of different diagnostic techniques and the development of non-invasive and personalized approaches. Integrating AI algorithms with dermoscopy and histopathology can further improve diagnostic accuracy and streamline the decision-making process. Non-invasive diagnostic tools, such as optical imaging and spectroscopy, hold promise for assessing skin lesions without the need for tissue biopsy. Personalized risk assessment models, incorporating clinical, dermoscopic, and molecular data, can enhance early detection and individualized treatment strategies. Telemedicine and remote diagnosis using digital imaging and AI-based systems can extend skin cancer diagnostic capabilities to underserved areas and improve access to expert opinions [20].

Conclusion

In conclusion, advancements in skin cancer diagnosis have significantly improved the ability to detect and diagnose skin cancer at an early stage. The integration of various diagnostic techniques, along with the development of non-invasive and personalized approaches, holds promise for further enhancing diagnostic accuracy, reducing unnecessary procedures, and improving patient outcomes. Continued research, collaboration, and innovation in this field are essential to addressing the remaining challenges and ensuring equitable access to accurate and efficient skin cancer diagnosis. By doing so, we can make significant strides in reducing the burden of skin cancer and improving public health.

Acknowledgement

None

Conflict of Interest

None

References

1. Ray JG, Wyatt PR, Thompson MD (2007) Vitamin B12 and the risk of neural tube defects in a folic-acid-fortified population. Epidemiology 18: 362-366.

Google Scholar, Crossref, Indexed at

2. Siddiqi A, Given CW, Given B, Sikorskii A (2009) Quality of life among patients with primary, metastatic and recurrent cancer. Eur J Cancer Care 18: 84-96.

Google Scholar, Crossref, Indexed at

3. Aboelnaga EM, Ahmed RA (2015) Difference between papillary and follicular thyroid carcinoma outcomes: an experience from Egyptian institution. Cancer Biol Med 12: 53-59.

Google Scholar, Crossref, Indexed at

4. Tiangui H, Jian Z, Wenyong WZ (2013) Sensitive detection of BRAF V600E mutation by Amplification Refractory Mutation System (ARMS)-PCR. Biomark Res 1: 1-6.

Google Scholar, Crossref, Indexed at

5. Melck A L, Yip L (2012) Predicting malignancy in thyroid nodules: molecular advances. Head Neck 34: 1355-1361.

Google Scholar, Crossref, Indexed at

6. Bhaijee F, Nikiforov YE (2011) Molecular analysis of thyroid tumors. Endocr Pathol 22: 126-133.

Google Scholar, Crossref, Indexed at

7. Rashid FA, Fukuoka J, Bychkov A (2020) Prevalence of BRAFV600E mutation in Asian series of papillary thyroid carcinoma-a contemporary systematic review. Gland Surg 9: 1878-1900.

Google Scholar, Crossref, Indexed at

8. Pyo JS, Kim DH, Yang J (2018) Diagnostic value of CD56 immunohistochemistry in thyroid lesions. Int J Biol Markers 33: 161-167.

Google Scholar, Crossref, Indexed at

9. Ahn D, Park JS, Sohn JH, Kim JH, Park SK, et al. (2012) BRAFV600E mutation does not serve as a prognostic factor in Korean patients with papillary thyroid carcinoma. Auris Nasus Larynx 39: 198-203.

Google Scholar, Crossref, Indexed at

10. Mond M, Alexiadis M, Fuller P J, Gilfillan C (2014) Mutation profile of differentiated thyroid tumours in an Australian urban population. Intern Med J 44: 727-734.

Google Scholar, Crossref, Indexed at

11. Nasirden A, Saito T, Fukumura Y, Hara K, Akaike K, et al. (2016) In Japanese patients with papillary thyroid carcinoma, TERT promoter mutation is associated with poor prognosis, in contrast to BRAF (V600E) mutation. Virchows Arch 469: 687-696.

Google Scholar, Crossref, Indexed at

12. Elisei R, Ugolini C, Viola D, Lupi C, Biagini A, et al. (2008) BRAF (V600E) mutation and outcome of patients with papillary thyroid carcinoma: a 15-year median follow-up study. J Clin Endocrinol Metab 93: 3943-3949.

Google Scholar, Crossref, Indexed at

13. Lupi C, Giannini R, Ugolini C, Proietti A, Berti P, et al. (2007) Association of BRAF V600E mutation with poor clinicopathological outcomes in 500 consecutive cases of papillary thyroid carcinoma. J Clin Endocrinol Metab 92: 4085-4090.

Google Scholar, Crossref, Indexed at

14. Jo YS, Li S, Song JH, Kwon KH, Lee JC, et al. (2006) Influence of the BRAF V600E Mutation on Expression of Vascular Endothelial Growth Factor in Papillary Thyroid Cancer. J Clin Endocrinol Metab 91: 3667-3670.

Google Scholar, Crossref, Indexed at

15. Abrosimov A, Saenko V, Rogounovitch T, Namba H, Lushnikov E, et al. (2007) Different structural components of conventional papillary thyroid carcinoma display mostly identical BRAF status. Int J Cancer 120: 196-200.

Google Scholar, Crossref, Indexed at

16. Durante C, Puxeddu E, Ferretti E, Morisi R, Moretti S, et al. (2007) BRAF mutations in papillary thyroid carcinomas inhibit genes involved in iodine metabolism. J Clin Endocrinol Metab 92: 2840-2843.

Google Scholar, Crossref, Indexed at

17. Fugazzola L, Puxeddu E, Avenia N, Romei C, Cirello V, et al. (2006) Correlation between B-RAFV600E mutation and clinico-pathologic parameters in papillary thyroid carcinoma: data from a multicentric Italian study and review of the literature. Endocr Relat Cancer 13: 455-464.

Google Scholar, Crossref, Indexed at

18. Sapio MR, Posca D, Troncone G, Pettinato G, Palombini L, et al. (2006) Detection of BRAF mutation in thyroid papillary carcinomas by mutant allele-specific PCR amplification (MASA). Eur J Endocrinol 154: 341-348.

Google Scholar, Crossref, Indexed at

19. Zou M, Baitei EY, Alzahrani AS, BinHumaid FS, Alkhafaji D, et al. (2014) Concomitant RAS, RET/PTC, or BRAF mutations in advanced stage of papillary thyroid carcinoma. Thyroid 24: 1256-1266.

Google Scholar, Crossref, Indexed at

20. Khan U, Warriach SA (2020) paraneoplastic syndrome affecting peripheral nerves, associated with anti-collapsin-response mediator protein-5 (anti-CRMP5) antibodies, as early manifestation of small cell lung cancer confined to a solitary lymph node without evidence of lung mass on routine CT thorax. BMJ Case Rep. 13:232-256.

Google Scholar, Crossref, Indexed at