A meta-analysis of sunburn and basal cell carcinoma risk

A novel hybrid feature fusion approach using handcrafted features with transfer learning model for enhanced skin cancer classification

Skin cancer is a deadly disease and has the highest rising rates globally. It arises from aberrant skin cells, which are often caused by prolonged exposure to ultraviolet rays from sunlight or artificial tanning devices. Dermatologists rely on visual inspection and need to identify suspicious lesions. Prompt and accurate diagnosis is pivotal for effective treatment and enhancing the chances of recovery. Recently, skin cancer prediction has been made utilising machine and deep learning algorithms for early detection. This methodology presents a novel hybrid feature extraction and is fused with a deep learning model for dermoscopic image analysis. Skin lesion images from sources like ISIC were pre-processed. Features were extracted using the Grey-Level Co-Occurrence Matrix (GLCM), Redundant Discrete Wavelet Transform (RDWT) and a various pre-trained model. After evaluating all the combinations, the proposed feature fusion model performed well rather than all other models. This proposed feature fusion model includes GLCM, RDWT, and DenseNet121 features, which were estimated with the various classifiers, among which an impressive accuracy of 93.46 % was obtained with the XGBoost classifier and 94.25 % with the ensemble classifier. This study underscores the efficacy of integrating diverse feature extraction techniques to increase the reliability and effectiveness of skin cancer diagnosis.

Early Detection of Basal Cell Carcinoma of Skin From Medical History

BACKGROUND AND OBJECTIVES

Basal cell carcinoma (BCC) is the most common form of skin cancer, originating from basal cells in the skin's outer layer. It frequently arises from prolonged exposure to ultraviolet (UV) radiation from the sun or tanning beds. Although BCC rarely metastasizes, it can cause significant local tissue damage if left untreated. Early detection is essential to prevent extensive damage and potential disfigurement. The United States Preventive Services Task Force (USPSTF) currently remains uncertain about the benefits and potential harms of routine skin cancer screenings in asymptomatic individuals. This paper evaluates the accuracy of predicting BCC using patients' medical histories to address this uncertainty and support early detection efforts.

METHODS

We analyzed the medical histories of 405,608 patients, including 7733 with BCC. We categorized 25,154 diagnoses into 16 body systems based on the hierarchy in the Systematized Nomenclature of Medicine (SNOMED) ontology. For each body system, we identified the most severe condition present. Logistic Least Absolute Shrinkage and Selection Operator (LASSO) regression was then employed to predict BCC, using demographic information, body systems, and pairwise and triple combinations of body systems, as well as missing value indicators. The dataset was split into 90% for training and 10% for validation. Model performance was evaluated using McFadden's R 2 , Percentage Deviance Explained (PDE), and cross-validated with the area under the receiver operating characteristic curve (AUC).

RESULTS

Diagnoses related to the Integument system showed an 8-fold higher likelihood of being associated with BCC compared to diagnoses related to other systems. Older (age from 60 to 69) white individuals were more likely to receive a BCC diagnosis. After training the model, it achieved a McFadden's R 2 of 0.286, an AUC of 0.912, and a PDE of 28.390%, reflecting a high level of explained variance and prediction accuracy.

CONCLUSIONS

This study underscores the potential of LASSO Regression models to enhance early identification of BCC. Extant medical history of patients, available in electronic health records, can accurately predict the risk of BCC. Integrating such predictive models into clinical practice could significantly improve early detection and intervention.

Effect of Repeated Low-Dose UVR Exposure on Skin Inflammation Threshold, Skin Biomarkers, and Vitamin D in Healthy Adults

UVR can cause photoaging, skin burns, and skin cancer. The skin counters these effects through photoadaptation, which involves melanin production, skin thickening, and immune responses. This study investigated the effects of repeated, suberythemal low-dose UVR on the skin's inflammation threshold, assessed as minimal erythema dose, across different skin phototypes. In addition, epidermal and stratum corneum thickness, melanin index, erythema index, skin and blood levels of immunological biomarkers, and blood vitamin D3 25(OH) were measured. Over 9 weeks, 31 subjects were exposed to UVR (0.8 standard erythema dose) on the lower back 3 times per week. Results showed a 50% increase in minimal erythema dose, with a stronger effect in lighter phototypes. Increase in minimal erythema dose was paralleled by an increase in erythema index. Melanin index and stratum corneum thickness increased significantly by 12 and 34%, respectively, with melanin having a stronger effect on the minimal erythema dose increase. Vitamin D levels rose by 21%, adjusting for seasonal decline. Immunological markers of T helper 1/T helper 2 response and vascular markers declined, indicating local immunosuppression, although no systemic changes in immunological markers were observed. These findings suggest that even low UVR doses are sufficient to induce photoadaptation. Further research is needed to evaluate the long-term benefits and risks of this exposure.

S2k guideline basal cell carcinoma of the skin (update 2023)

Basal cell carcinoma is the most common malignant tumor in the fair-skinned population and its incidence continues to rise. An update of the S2k guideline with the participation of all specialist societies familiar with the clinical picture and previous literature research is of great importance for the quality of care for affected patients. In addition to epidemiology, diagnostics and histology are discussed. After risk stratification, therapy is divided into topical, systemic and radiation therapy. Surgical removal remains the treatment of first choice in most cases. The approval of anti-PD1 inhibitors for locally advanced and metastatic tumors has opened up a new option in second-line therapy (after hedgehog inhibitors).

Risk Factors and Innovations in Risk Assessment for Melanoma, Basal Cell Carcinoma, and Squamous Cell Carcinoma

Skin cancer is the most frequently diagnosed cancer globally and is preventable. Various risk factors contribute to different types of skin cancer, including melanoma, basal cell carcinoma, and squamous cell carcinoma. These risk factors encompass both extrinsic, such as UV exposure and behavioral components, and intrinsic factors, especially involving genetic predisposition. However, the specific risk factors vary among the skin cancer types, highlighting the importance of precise knowledge to facilitate appropriate early diagnosis and treatment for at-risk individuals. Better understanding of the individual risk factors has led to the development of risk scores, allowing the identification of individuals at particularly high risk. These advances contribute to improved prevention strategies, emphasizing the commitment to mitigating the impact of skin cancer.

Multimodal Method for Differentiating Various Clinical Forms of Basal Cell Carcinoma and Benign Neoplasms In Vivo

Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy, the coefficients of melanin pigmentation, erythema, hemoglobin content, and the slope coefficient of diffuse reflectance spectroscopy in the wavelength range 650-800 nm were determined. Statistical texture analysis of optical coherence tomography images was used to calculate first- and second-order statistical parameters. The analysis of ultrasound images assessed the shape of the tumor according to parameters such as area, perimeter, roundness and other characteristics. Based on the calculated parameters, a machine learning algorithm was developed to differentiate the various clinical forms of basal cell carcinoma. The proposed algorithm for classifying various forms of basal cell carcinoma and benign neoplasms provided a sensitivity of 70.6 ± 17.3%, specificity of 95.9 ± 2.5%, precision of 72.6 ± 14.2%, F1 score of 71.5 ± 15.6% and mean intersection over union of 57.6 ± 20.1%. Moreover, for differentiating basal cell carcinoma and benign neoplasms without taking into account the clinical form, the method achieved a sensitivity of 89.1 ± 8.0%, specificity of 95.1 ± 0.7%, F1 score of 89.3 ± 3.4% and mean intersection over union of 82.6 ± 10.8%.

Association Between Sports Team Participation and Sunburn Among U.S. High School Students, National Youth Risk Behavior Survey, 2021

Sunburn during adolescence increases a person's lifetime skin cancer risk. This study examines the association between sports team participation and sunburn prevalence among U.S. high school students. Using cross-sectional data from the 2021 National Youth Risk Behavior Survey, we compared sunburn prevalence among students who participated in one or more sports teams during the past 12 months to those who did not. Nationwide, 64.4% of high school students experienced one or more sunburns during the past 12 months, and 49.1% had played on one or more sports teams during the past 12 months. Sports team participation was associated with higher sunburn prevalence overall (adjusted prevalence ratio [aPR], 1.2; 95% CI [1.2, 1.3]) and among female (aPR, 1.2; 95% CI [1.2, 1.3]) and male (aPR, 1.2; 95% CI [1.1, 1.3]) students. Sunburn prevalence is higher among student athletes than those who do not participate on sports teams. Evidence-based organizational policies and practices are available to help keep student athletes sun-safe while allowing them to enjoy the benefits of sports participation. Future research could examine the contexts in which high school students experience sunburn and why student athletes are at an increased risk.