Raising Awareness for Skin Cancer Prevention, Early Detection, and Healthy Sun Practices
Raising Awareness for Skin Cancer Prevention, Early Detection, and Healthy Sun Practices |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-8 |
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| Year of Publication : 2025 | ||
| Author : Muhammad Akram, Urooj Rehman, Fahad Said Khan, Ho Soonmin | ||
| DOI : 10.14445/22315381/IJETT-V73I8P120 | ||
How to Cite?
Muhammad Akram, Urooj Rehman, Fahad Said Khan, Ho Soonmin,"Raising Awareness for Skin Cancer Prevention, Early Detection, and Healthy Sun Practices", International Journal of Engineering Trends and Technology, vol. 73, no. 8, pp.235-243, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I8P120
Abstract
Skin cancer has developed into one of the most common cancers globally over the past few decades. The sooner researchers can recognize skin cells at risk of damage from harmful UV rays, the better humans can protect against the disease. While it accounts for a smaller percentage of cases, the deadliness of melanoma, the deadliest form of skin cancer, is to blame for most skin cancer-related deaths. The number one risk factor for skin cancer is too much sun, especially sunburns that a person may have experienced in their lifetime. Sunburn, especially in children, significantly increases the risk of skin cancer in later life. An increased risk is also present among those with fair skin, a weakened immune system, and a family history of skin cancer. People who work outdoors or enjoy outdoor activities that expose them to higher amounts of sunlight are also at increased risk. Prevention methods center around sun protection. As it is often curable in its early stages, early detection, self-examinations, and regular visits to the dermatologist are key to better outcomes. You should have an education and awareness campaign when targeting skin cancer now. Humans can save lives by spreading the word about the dangers of UV radiation, the importance of skin cancer prevention, and the need for early detection. Doctors recommend the ABCDE rule when it comes to identifying melanoma: Asymmetry, uneven borders, change in color, diameters greater than the length of a pencil eraser, and evolution in size. With the increasing incidence of skin cancers globally, decreasing mortality is dependent on early detection as well as prevention. Even as public health campaigns have worked to direct attention to prevention and education, research into new treatments, such as immunotherapy and targeted medicines, offers hope for advanced patients. Because early recognition and prevention remain the best weapons against this preventable and common disease, the more the public knows about skin cancer, the healthier the public will be.
Keywords
Medicine, Cancer, Basal Cell Carcinoma, Human and disease, Fair skin, Skin health education.
References
[1] Amrita Muralikrishnan et al., “Chemistry, Pharmacology and Therapeutic Potential of Decursin: A Promising Natural Lead for New Drug Discovery and Development,” Drug Design, Development and Therapy, vol 18, pp. 3741-3763, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Lei Li et al., “Advancements in the Characterization of Tissue Resident Memory T Cells in Skin Disease,” Clinical Immunology, vol. 245, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Yushu Huang et al., “Heat and Outpatient Visits of Skin Diseases - A Multisite Analysis in China, 2014-2018,” Heliyon, vol. 8, no. 10, pp. 1-7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Yassien Badr et al., “Cellular Infiltration, Cytokines, and Histopathology of Skin Lesions Associated with Different Clinical Forms and Stages of Naturally Occurring Lumpy Skin Disease in Cattle,” Comparative Immunology, Microbiology and Infectious Diseases, vol. 90-91, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] June K. Robinson et al., “Real-Time UV Measurement with a Sun Protection System for Warning Young Adults About Sunburn: Prospective Cohort Study,” JMIR mHealth and uHealth, vol. 9, no. 5, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Heidi Li et al., “Sunscreen Application, Safety, and Sun Protection: The Evidence,” Journal of Cutaneous Medicine and Surgery, vol. 23, no. 4, pp. 357-369, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Elisabeth A. George et al., “Photosensitizing Medications and Skin Cancer: A Comprehensive Review,” Cancers, vol 13, no. 10, pp. 1-20, 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] David B. Buller et al., “Smartphone Mobile Application Delivering Personalized, Real-Time Sun Protection Advice: A Randomized Clinical Trial,” JAMA Dermatology, vol. 151, no. 5, pp. 497-504, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Dora Liu et al., “A Practical Guide to the Monitoring and Management of the Complications of Systemic Corticosteroid Therapy,” Allergy, Asthma & Clinical Immunology, vol. 9, no. 1, pp. 1-25, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Francisco Solano, “Photoprotection and Skin Pigmentation: Melanin-Related Molecules and Some Other New Agents Obtained from Natural Sources,” Molecules, vol. 25, no. 7, pp. 1-18, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Denise K. Woo, and Melody J. Eide, “Tanning Beds, Skin Cancer, and Vitamin D: An Examination of the Scientific Evidence and Public Health Implications,” Dermatologic Therapy, vol. 23, no. 1, pp. 61-71, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Waner Liu, Xu Zhang, and Xiang Chen, “Unraveling the Causal Associations between Systemic Cytokines and Six Inflammatory Skin Diseases,” Cytokine, vol. 185, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jinbo Chen et al., “Pigmented Skin Disease Classification Via Deep Learning with an Attention Mechanism,” Applied Soft Computing, vol. 170, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Dagne Walle Girmaw, “Livestock Animal Skin Disease Detection and Classification Using Deep Learning Approaches,” Biomedical Signal Processing and Control, vol. 102, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Linda Zhou, and Thomas H. Leung, “Detection by Single-Cell RNA Sequencing of Virally Mediated Skin Diseases,” JID Innovations, vol. 5, no. 3, pp. 1-9, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Savita Rathee et al., “Sensitivity Analysis of Fractional Order SVEIR Lumpy Skin Disease Model,” Alexandria Engineering Journal, vol. 119, pp. 609-622, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Antony R. Young et al., “The Similarity of Action Spectra for Thymine Dimers in Human Epidermis and Erythema Suggests That DNA Is the Chromophore for Erythema,” Journal of Investigative Dermatology, vol. 111, no. 6, pp. 982-988, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[18] John A. Parrish, Kurt F. Jaenicke, and R. Rox Anderson, “Erythema and Melanogenesis Action Spectra of Normal Human Skin,” Photochemistry and Photobiology, vol. 36, no. 2, pp. 187-191, 1982.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Theodore Rosen, and Mark G. Lebwohl, “Prevalence and Awareness of Actinic Keratosis: Barriers and Opportunities,” Journal of the American Academy of Dermatology, vol 68, no. 1, pp. S1-S9, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Nathaniel III. Lampley et al., “Field Therapy for Actinic Keratosis: A Structured Review of the Literature on Efficacy, Cost, and Adherence,” Dermatologic Surgery, vol. 49, no. 2, pp. 124-129, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shabnam Madani et al., “Ten-Year Follow-up of Persons with Sun-Damaged Skin Associated with Subsequent Development of Cutaneous Squamous Cell Carcinoma,” JAMA Dermatology, vol. 157, no. 5, pp. 559-565, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] E. Wenande, K. Togsverd, and A. Hastrup, “Skin Cancer Development Is Strongly Associated with Actinic Keratosis in Solid Organ Transplant Recipients: A Danish Cohort Study,” Dermatology, vol. 239, pp. 393-402, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] K. Sanganagouda et al., “Development of a Single-Step SYBR Green-Based Real-Time PCR Assay for Detection and Quantification of Lumpy Skin Disease Virus in Cattle,” Gene Reports, vol. 39, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Hugh M. Gloster Jr, and David G. Brodland, “The Epidemiology of Skin Cancer,” Dermatologic Surgery, vol. 22, no. 3, pp. 217-226, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Elena Niculet et al., “Basal Cell Carcinoma: Comprehensive Clinical and Histopathological Aspects, Novel Imaging Tools and Therapeutic Approaches (Review),” Experimental and Therapeutic Medicine, vol. 23, no, 1, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Lauren M. Stanoszek, Grace Y. Wang, and Paul W. Harms, “Histologic Mimics of Basal Cell Carcinoma,” Archives of Pathology & Laboratory Medicine, vol. 141, no. 11, pp. 1490-1502, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Manar Al Wohaib et al., “Characteristics and Factors Related to Eyelid Basal Cell Carcinoma in Saudi Arabia,” Middle East African of Ophthalmology, vol. 25, no. 2, pp. 96-102, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Michael C. Cameron et al., “Basal Cell Carcinoma: Epidemiology; Pathophysiology; Clinical and Histological Subtypes; and Disease Associations,” Journal of the American Academy of Dermatology, vol. 80, no. 2, pp. 303-317, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Vincenzo De Giorgi et al., “Advanced Basal Cell Carcinoma: When A Good Drug is Not Enough,” Journal of Dermatological Treatment, vol. 31, no. 6, pp. 552-553, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Jie Dai et al., “Identification of Critically Carcinogenesis-Related Genes in Basal Cell Carcinoma,” Onco Targets and Therapy, vol. 11, pp. 6957-6967, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Caroline Mann et al., “Risk of Developing Sleep Disorders and Psychologic Comorbidity in Children with Inflammatory Skin Diseases-A Population-Based Study,” Journal of the American academy of Dermatology, vol. 92, no. 6, pp. 1261-1268, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Valquiria Pessoa Chinem, and Hélio Amante Miot, “Epidemiology of Basal Cell Carcinoma,” Brazilian Annals of Dermatology, vol 86, no. 2, pp. 292-305, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Ulrike Leiter, Ulrike Keim, and Claus Garbe, Epidemiology of Skin Cancer: Update 2019, Sunlight, Vitamin D and Skin Cancer, Springer, Cham, pp. 123-139, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Pushkar Aggarwal, Peter Knabel, and Jr. Alan B. Fleischer, “United States Burden of Melanoma and Non-Melanoma Skin Cancer From 1990 To 2019,” Journal of the American Academy of Dermatology, vol. 85, no. 2, pp. 388-395, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Maryam M. Asgari et al., “Trends in Basal Cell Carcinoma Incidence and Identification of High-Risk Subgroups, 1998-2012,” JAMA Dermatology, vol. 151, no. 9, pp. 976-981, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Alexander G. Marzuka, and Samuel E. Book, “Basal Cell Carcinoma: Pathogenesis, Epidemiology, Clinical Features, Diagnosis, Histopathology, and Management,” Yale Journal of Biology and Medicine, vol. 88, no. 2, pp. 167-179, 2015.
[Google Scholar] [Publisher Link]
[37] Jean Michel Amici et al., “Defining and Recognising Locally Advanced Basal Cell Carcinoma,” European Journal of Dermatology, vol. 25, no. 6, pp. 586-594, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Susan D. Mathias et al., “Assessing Health-Related Quality of Life for Advanced Basal Cell Carcinoma and Basal Cell Carcinoma Nevus Syndrome: Development of the First Disease-Specific Patient-Reported Outcome Questionnaires,” JAMA Dermatology, vol. 150, no. 2, pp. 169-176, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Erica Leavitt, Gary Lask, and Stephanie Martin, “Sonic Hedgehog Pathway Inhibition in the Treatment of Advanced Basal Cell Carcinoma,” Current Treatment Options in Oncology, vol. 20, no. 11, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Serife Zehra Altunkurek, and Eylül Kaya, “Parents' Knowledge and Understanding of Skin Cancer and Skin Self-Examination and Behaviors to Protect Their Children from the Sun,” European Journal of Oncology Nursing, vol. 50, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[41] James J. Leyden, “Effects of Topical Tretinoin on Non-Sun-Exposed Protected Skin of the Elderly: Over 26 Years Later,” Journal of the American Academy of Dermatology, vol. 82, no. 3, pp. 776-777, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Alyaa Hassan Ali Eissa et al., “Basal Cell Carcinoma of the Scrotum: A Rare Occurrence in Sun Protected Skin,” African Journal of Urology, vol. 24, no. 3, pp. 160-162, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[43] M.Margarida Galvão et al., “Lymphocyte Subsets and Langerhans Cells in Sun-Protected and Sun-Exposed Skin of Immunosuppressed Renal Allograft Recipients,” Journal of the American Academy of Dermatology, vol. 38, no. 1, pp. 38-44, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Z. Wu et al., “686 Quantification of Perception Toward Skin Complexion on Sun-Exposed vs. Sun-Protected Skin Using an Artificial Neural Network,” Journal of Investigative Dermatology, vol. 143, no. 5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Roman C. Maron et al., “A Benchmark for Neural Network Robustness in Skin Cancer Classification,” European Journal of Cancer, vol. 155, pp. 191-199, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Sarah Haggenmüller et al., “Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study,” JMIR mHealth and uHealth, vol. 9, no. 8, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Suzanne M. Mahon, “Skin Cancer Prevention: Education and Public Health Issues,” Seminars in Oncology Nursing, vol. 19, no. 1, pp. 52-61, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Theresa Garvin, and John Eyles, “Public Health Responses for Skin Cancer Prevention: The Policy Framing of Sun Safety in Australia, Canada and England,” Social Science & Medicine, vol. 53, no. 9, pp. 1175-1189, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Lowell A. Goldsmith et al., “Full Proceedings from the National Conference to Develop a National Skin Cancer Agenda: American Academy of Dermatology and Centers for Disease Control and Prevention, Washington, D.C., April 8-10, 1995,” Journal of the American Academy of Dermatology, vol. 35, no. 5, pp. 748-756, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Joseph S. Rossi et al., “Preventing Skin Cancer Through Behavior Change: Implications for Interventions,” Dermatologic Clinics, vol. 13, no. 3, pp. 613-622, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Carolina Magalhaes et al., “Comparison of Machine Learning Strategies for Infrared Thermography of Skin Cancer,” Biomdeical Signal Processing and Control, vol. 69, pp. 1-23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[52] B. Hughes, D. Altman, and A. Newton, “Melanoma and Skin Cancer: Evaluation of a Health Education Programme for Secondary Schools,” British Journal of Dermatology, vol. 128, no. 4, pp. 412-417, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Achim Hekler et al., “Pathologist-Level Classification of Histopathological Melanoma Images with Deep Neural Networks,” European Journal of Cancer, vol. 115, pp. 79-83, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[54] Francesco De Logu et al., “Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm,” Frontiers in Oncology, vol. 10, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Linyan Wang et al., “Automated Identification of Malignancy in Whole-Slide Pathological Images: Identification of Eyelid Malignant Melanoma in Gigapixel Pathological Slides Using Deep Learning,” The British Journal of Ophthalmology, vol. 104, no. 3, pp. 318-323, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Wei Ba et al., “Diagnostic Assessment of Deep Learning for Melanocytic Lesions Using Whole-Slide Pathological Images,” Translational Oncology, vol. 14, no. 9, pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[57] Rocío del Amor et al., “An Attention-Based Weakly Supervised Framework for Spitzoid Melanocytic Lesion Diagnosis in WSI,” Artificial Intelligence in Medicine, vol. 121, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[58] Henrik Olsson et al., “Estimating Diagnostic Uncertainty in Artificial Intelligence Assisted Pathology Using Conformal Prediction,” Nature Communications, vol. 13, no. 1, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[59] Mike C.M. van Zon et al., “Whole-Slide Margin Control through Deep Learning in Mohs Micrographic Surgery for Basal Cell Carcinoma,” Experimental Dermatology, vol. 30, no. 5, pp. 733-738, 2021.
[CrossRef] [Google Scholar] [Publisher Link]