Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review

The Role of Health Literacy in Skin Cancer Preventative Behavior and Implications for Intervention: A Systematic Review

BACKGROUND

Health literacy is essential for individuals to access, understand, and utilize information and services to inform health related decisions and actions. As one of the most diagnosed and preventable forms of cancer, skin cancer disease risk can be reduced through preventative behavior. Currently, there is no focused study looking specifically at health literacy and skin cancer. An understanding of how health literacy affects skin cancer-related preventive behaviors can improve current practices in skin cancer prevention.

OBJECTIVE

To systematically identify, synthesize, and summarize findings on the role of health literacy in skin cancers (including cutaneous squamous cell carcinoma, basal cell carcinoma, and melanoma), with a focus on preventive behaviors using studies that utilized quantifiable health literacy measurements.

METHODS

A literature search was performed by searching PubMed, Embase, PsycINFO, and CINAHL from inception until September 26, 2023 to identify cross-sectional, case-control, cohort, or randomized controlled studies that investigated the association between health literacy and skin cancer prevention and diagnosis.

RESULTS

Health literacy levels varied across geographic regions, specific populations, and ethnicities. Most of the included studies found a positive association between higher health literacy and better skin cancer preventative behaviors. This included sun-protective behaviors such as: wearing sleeved shirts or shirts with collars, using gloves, covering head and face, limiting sun exposure, more sunscreen use, and less sunbathing or indoor tanning. Higher health literacy was associated with increased likelihood to engage in genetic testing and less family influence on health in one study which assessed determinants of interest in skin cancer genetic testing. Another study investigating family communication about skin cancer found that higher health literacy was associated with increased family communication regarding general cancer risk. One sun protection interventional education program was effective at increasing participants' knowledge, awareness of skin cancer risk, willingness to change sun protection, and use of sun protection, but results varied between ethnic groups.

CONCLUSIONS

Skin cancer-related educational interventions can be effective in improving health literacy and potentially lessen the impact of skin cancer through positive behavior modification, early detection, and disease knowledge and awareness. Interventions need to be tailored to its target population to maximize effectiveness due to the varying baseline of health literacy identified across different geographic and ethnic groups. Protocol Registration PROSPERO CRD42022340826.

Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study

BACKGROUND

The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource-heavy challenge that could potentially benefit from AI-tool assistance, particularly if provided in widely available AI solutions. A novel smartphone application(app)-based AI system, "SCAI," was developed and trained to recognize spots in paired images of skin, pursuing identification of new skin lesions. This pilot study aimed to investigate the feasibility of the SCAI-app to identify simulated skin changes in vivo.

MATERIALS AND METHODS

The study was conducted in a controlled setting with healthy volunteers and standardized, simulated skin changes (test spots), consisting of customized 3-mm adhesive spots in three colors (black, brown, and red). Each volunteer had a total of eight test spots adhered to four areas on back and legs. The SCAI-app collected smartphone- and template-guided standardized images before and after test spot application, using its backend AI algorithms to identify changes between the paired images.

RESULTS

Twenty-four volunteers were included, amounting to a total of 192 test spots. Overall, the detection algorithms identified test spots with a sensitivity of 92.0% (CI: 88.1-95.9) and a specificity of 95.5% (CI: 95.0-96.0). The SCAI-app's positive predictive value was 38.0% (CI: 31.0-44.9), while the negative predictive value was 99.7% (CI: 99.0-100).

CONCLUSION

This pilot study showed that SCAI-app could detect simulated skin changes in a controlled in vivo setting. The app's feasibility in a clinical setting with real-life skin lesions remains to be investigated, where the challenge with false positives in particular needs to be addressed.

Artificial intelligence for nonmelanoma skin cancer

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

Two Cases of Pigmented Basal Cell Carcinoma in African American Patients

Basal cell carcinoma (BCC) is the most common cutaneous malignancy, comprising approximately 80% of non-melanoma skin cancers. There are numerous subtypes, including pigmented basal cell carcinoma (pBCC), a rare clinical and histological variant. Skin cancers in African American patients, although rare, still do occur. BCC is an uncommon neoplasm in this population, but when it does occur, pigmentation is present in more than 50% of tumors compared with only 5% to 6% of BCCs in Caucasians. This report presents two cases of histologically verified pBCC in African American patients from dermatology clinics at the Veterans Affairs Hospital located in the Texas Medical Center. With the population of the United States growing more diverse, these cases emphasize the importance of recognizing the nuanced morphology of BCC in the skin of color compared to lighter-skinned counterparts. This is especially necessary, as early detection and prompt management are key to combating the disproportionately high morbidity and mortality related to skin cancers affecting patients of color.