Skin Cancer Research Articles

A comprehensive review of deep learning for medical image segmentation

Medical image segmentation provides detailed mappings of regions of interest, facilitating precise identification of critical areas and greatly aiding in the diagnosis, treatment, and understanding of diverse medical conditions. However, conventional techniques frequently rely on hand-crafted feature-based approaches, posing challenges when dealing with complex medical images, leading to issues such as low accuracy and sensitivity to noise. Recent years have seen substantial research focused on the effectiveness of deep learning models for segmenting medical images. In this study, we present a comprehensive review of the various deep learning-based approaches for medical image segmentation and provide a detailed analysis of their contributions to the domain. These methods can be broadly categorized into five groups: CNN-based methods, Transformer-based methods, Mamba-based methods, semi-supervised learning methods, and weakly supervised learning methods. Convolutional Neural Networks (CNNs), with their efficient feature self-learning, have driven major advances in medical image segmentation. Subsequently, Transformers, leveraging self-attention mechanisms, have achieved performance on par with or surpassing Convolutional Neural Networks. Mamba-based methods, as a novel selective state-space model, are emerging as a promising direction. Furthermore, due to the limited availability of annotated medical images, research in weakly supervised and semi-supervised learning continues to evolve. This review covers common evaluation methods, datasets, and deep learning applications in diagnosing and treating skin lesions, hippocampus, tumors, and polyps. Finally, we identify key challenges such as limited data, diverse modalities, noise, and clinical applicability, and propose future research in zero-shot segmentation, transfer learning, and multi-modal techniques to advance the development of medical image segmentation technology.

A hybrid feature fusion deep learning framework for multi-source medical image analysis

Despite the widespread adoption of deep learning to enhance image classification, significant obstacles remain. First, multisource data with diverse sizes and formats is a great challenge for most current deep learning models. Second, lacking manual labeled data for model training limits the application of deep learning. Third, the widely used CNN-based methods shows their limitations in extracting global features and yield poor performance for image topology. To address these issues, we propose a Hybrid Feature Fusion Deep Learning (HFFDL) framework for image classification. This framework consists of an automated image segmentation module, a two-stream backbone module, and a classification module. The automatic image segmentation module utilizes the U-Net model and transfer learning to detect region of interest (ROI) in multisource images; the two-stream backbone module integrates the Swin Transformer architecture with the Inception CNN, with the aim of simultaneous extracting local and global features for efficient representation learning. We evaluate the performance of HFFDL framework with two publicly available image datasets: one for identifying COVID-19 through X-ray scans of the chest (30,386 images), and another for multiclass skin cancer screening using dermoscopy images (25,331 images). The HFFDL framework exhibited greater performance in comparison to many cutting-edge models, achieving the AUC score 0.9835 and 0.8789, respectively. Furthermore, a practical application study conducted in a hospital, identifying viable embryos using medical images, revealed the HFFDL framework outperformed embryologists.

The Utilization of PRAME in the Diagnosis, Prognosis, and Treatment of Melanoma.

Melanoma, a deadly form of skin cancer, has seen improved survival rates due to advances in diagnosis and treatment, yet the need for further improvement remains critical. Tumor-associated antigens, such as PRAME (Preferentially Expressed Antigen in Melanoma), offer promising avenues for enhanced diagnostic precision, prognostic assessment, and targeted immunotherapy. PRAME, a cancer testis antigen, is selectively expressed in various cancers, including melanoma, and plays a key role in promoting tumorigenesis through inhibition of retinoic acid signaling, epithelial-to-mesenchymal transition, and immune evasion. This review explores the diagnostic utility of PRAME in distinguishing melanoma from benign nevi, its prognostic value in aggressive melanoma subtypes, and its potential as a therapeutic target in cancer vaccines and adoptive T-cell therapies. While PRAME-targeted therapies face challenges such as tumor heterogeneity and immune suppression, ongoing research aims to overcome these barriers, offering hope for more effective melanoma treatments.

Arsenic in Rice: A Call to Change Feeding Substitution Practices for Pediatric Otolaryngology Patients.

Rice products are ubiquitous in the diets of American children and are often utilized to thicken feeds as part of treatments for gastroesophageal reflux disease in infants. However, a 2015 Food and Drug Administration investigation demonstrated that they contain unsafe levels of inorganic arsenic. Inorganic arsenic exposure has been linked to serious health issues including skin, lung, and bladder cancer. Smaller children are put at higher risk due to the increased arsenic-to-weight ratio compared to adults, threatening their development in multiple ways. Other thickeners offer cost-effective, safer alternatives. With this communication, we aim to raise awareness of this issue within the otolaryngology practice thereby bringing us abreast of the best practices of the pediatric and gastrointestinal medical societies who have recommended against the use of rice for thickening feeds, instead advocating for alternate natural thickening agents like wheat or oatmeal.

Basosquamous Carcinoma: A Survey Study on Current Management in the UK.

Basosquamous carcinoma (BSC) is a rare, aggressive non-melanoma skin cancer with overlapping features of basal cell carcinoma and squamous cell carcinoma, often requiring histopathological analysis for accurate diagnosis. Our study surveyed UK dermatologists on their management practices for BSC, revealing significant variability in disease categorisation, treatment approaches, and follow-up protocols, with a noted lack of standardised guidelines. The findings underscore the need for prospective studies and the development of treatment guidelines to improve the management of BSC.

M‐Net: A Skin Cancer Classification With Improved Convolutional Neural Network Based on the Enhanced Gray Wolf Optimization Algorithm

ABSTRACTSkin cancer is a common malignant tumor causing tens of thousands of deaths each year, making early detection essential for better treatment outcomes. However, the similar visual characteristics of skin lesions make it challenging to accurately differentiate between lesion types. With advancements in deep learning, researchers have increasingly turned to convolutional neural networks for skin cancer detection and classification. In this article, an improved skin cancer classification model M‐Net is proposed, and the enhanced gray wolf optimization algorithm is combined to improve the classification performance. The gray wolf optimization algorithm guides the wolf pack to prey through a multileader structure and gradually converges through the encirclement and pursuit mechanism, so as to perform a more detailed search in the later stage. To further improve the performance of the gray wolf optimization, this study introduces the simulated annealing algorithm to avoid falling into the local optimal state and expands the search range by improving the search mechanism, thus enhancing the global optimization ability of the algorithm. The M‐Net model significantly improves the accuracy of classification by extracting features of skin lesions and optimizing parameters with the enhanced gray wolf optimization algorithm. The experimental results based on the ISIC 2018 dataset show that compared with the baseline model, the feature extraction network of the model has achieved a significant improvement in accuracy. The classification performance of M‐Net is excellent in multiple indicators, with accuracy, precision, recall, and F1 score reaching 0.891, 0.857, 0.895, and 0.872, respectively. In addition, the modular design of M‐Net enables it to flexibly adjust feature extraction and classification modules to adapt to different classification tasks, showing great scalability and applicability. In general, the model proposed in this article performs well in the classification of skin lesions, has broad clinical application prospects, and provides strong support for promoting the diagnosis of skin diseases.

Economics of risk stratification for skin cancer screening in solid organ transplant recipients

Background: Solid organ transplant recipients (SOTRs) have different risks of developing skin cancer depending on patient characteristics. However, there is currently no widely used tool to stratify skin cancer risk in these patients. The Skin and Ultraviolet Neoplasia Transplant Risk Assessment Calculator (SUNTRAC) is an attempt to stratify skin cancer risk in SOTRs and guide screening recommendations for those patients. Materials and methods: It was assumed that the 2022 solid organ transplant population in the United States would follow the same distribution of skin cancer risk as the population in the study that led to the development of the SUNTRAC. The total number of skin cancer screening visits and the total cost of those visits over 10 years were determined utilizing the screening recommendations in the SUNTRAC. Results: If all SOTRs received yearly skin cancer screens, 428,870 office visits would be conducted over 10 years for a total cost of $94,780,270. If the SUNTRAC were utilized, 336,666 office visits would be conducted for a 10-year cost of $74,403,186. Conclusions: Utilizing the SUNTRAC to guide skin cancer screening recommendations in SOTRs has the potential to minimize over-screening and lower the total cost of skin cancer screening visits for SOTRs. Initial studies into the applicability of the SUNTRAC have found similar risk distribution in different populations of SOTRs. Utilizing this model to guide skin cancer screening recommendations will better allocate resources to the highest risk patients while also avoiding unnecessary health care costs.

Warmer Temperatures Are More Predictive Than Solar Radiation for the Number of Skin Cancer Removal Procedures.

Solar radiation is the primary risk factor for skin cancer, with personal exposure influenced by environmental and behavioral factors. At higher temperatures, behavioral changes increase solar radiation exposure. Examine the relationships between solar radiation, ambient temperature, age, and skin cancer. For the contiguous United States, we obtained the state mean global horizontal irradiance (GHI), daily maximum temperature, and number of skin cancer removals in the Medicare population. For skin cancer removals, we defined more sun-exposed skin as the head, neck, hands, and feet, and less sun-exposed skin as the trunk, arms, and legs. By comparing the temperature thresholds 17°C, 20°C, 24°C, 27°C, 31°C, and 34°C, we found that the annual number of days above 24°C was the strongest temperature-related predictor of skin cancer removals. Multivariable linear regression showed that the number of days above 24°C predicted more skin cancer removals for all body locations and less sun-exposed skin (p = 0.008 and p = 0.003, respectively), while GHI did not (p = 0.1 and p = 0.8, respectively). GHI only predicted more skin cancer removals for more sun-exposed skin (p = 0.02). More days above 24°C was a better predictor of skin cancer removals than GHI for all skin locations and less sun-exposed skin, suggesting that the behavioral changes occurring at warmer temperatures are more predictive of skin cancer removals than solar irradiance. Due to rising global temperatures, skin cancer incidence may further increase. Temperature-related behavioral changes represent a potential target for skin cancer prevention efforts.

Exploring the therapeutic potential of Pongamia pinnata plant extract against skin cancer: In-silico and in-vitro study

Ethnopharmacological relevancePongamia pinnata Linn belonging to the Fabaceae family, holds significance as a crucial remedy in herbal medicine. Aim of the studyThe present study aims to determine the anticancer and antioxidant properties of the plant extract. Material and methodsThe methodology includes extraction of the leaf sample by soxhlet method followed by the phytochemical analysis of leaf extract. Through in-silico approach three anti-cancer receptors were analyzed with 10 ligands which were studied using molecular docking and molecular simulation approach. The prediction outcome of in-silico tests on the petroleum ether plant extract served as a foundation for the subsequent in-vitro studies. Phytochemical profiling of plant extracts using GC-MS analysis, and cytotoxicity testing for A431 skin cell line using MTT Assay. ResultsThe binding affinity of Pongamia pinnata as an anti-cancer agent with respect to the targets EGF, EGFR, ERBB2 was evident. In the in-silico studies, the highest binding affinity with respect to the docked complexes were -8.2 kcal/mol for EGF with Pazopanib, -8.3 kcal/mol for EGFR with pongachromene, -9.5 kcal/mol for ERBB2 with Vitexin. Further these complexes were assessed by molecular simulations and it confirms the stability of these complexes. In in-vitro studies the HPLC results indicated the presence of 0.176 ± 0.001 mg/mL of phenols and 0.159 ± 0.001 mg/mL of flavonoids. The IC50 value was 1.051 which revealed the antioxidant potential. The cytotoxicity studies against the A431 skin cancer cell resulted in an IC50 concentration of 89.59 μg/ml. ConclusionsThese studies show that P. pinnata has the properties to serve as a therapeutic agent for the treatment of skin tumors. The molecular simulation studies approach is a predictor for drug discovery, acting as a basis for testing anti-cancer activity against the skin tumor. As a result, it can be a useful source of crude drug for the treatment of melanomas.

In vivo CRISPRi screen identified lncRNA portfolio crucial for cutaneous squamous cell carcinoma tumor growth.

Cutaneous squamous cell carcinoma (cSCC) accounts for 20% of all skin cancer deaths globally, making it the second-highest subtype of skin cancer. The prevalence of cSCC in humans, as well as the poor capacity for an efficient prognosis, highlights the need to uncover alternative actors and mechanisms at the foundation of skin cancer development. Significant advances have been made to better understand some key factors in cSCC progression. However, little is known about the role of noncoding RNAs, particularly of a specific category called long noncoding RNA (lncRNA). By performing pseudobulk analysis of single-cell sequencing data from normal and cSCC human skin tissues, we determined a global portfolio of lncRNAs specifically expressed in keratinocyte subpopulations. Integration of CRISPR interference screens in vitro and the xenograft model identified several lncRNAs impacting the growth of cSCC cancer lines both in vitro and in vivo. Among these, we further validated LINC00704 and LINC01116 as proliferation-regulating lncRNAs in cSCC lines and potential biomarkers of cSCC progression. Taken together, our study provides a comprehensive signature of lncRNAs with roles in regulating cSCC progression.

Challenges in Teledermoscopy Diagnostic Outcome Studies: Scoping Review of Heterogeneous Study Characteristics.

Teledermoscopy has demonstrated benefits such as decreased costs and enhanced access to dermatology care for skin cancer detection. However, the heterogeneity among teledermoscopy studies hinders the systematic reviews' synopsis of diagnostic outcomes, impeding trust and adoption in general practice and limiting overall health care benefits. This study aims to improve understanding and standardization of teledermoscopy diagnostic studies, by identifying and categorizing study characteristics contributing to heterogeneity. Subsequently, the variability and consistency of these characteristics were assessed. A review of systematic reviews regarding the diagnostic outcomes of teledermoscopy was performed to discern reported study characteristics contributing to heterogeneity. These characteristics were thematically grouped into 3 domains (population, index test, and reference standard), forming a data extraction framework. A scoping review on teledermoscopy diagnostic outcomes studies was performed, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Data pertaining to study characteristics from included studies were extracted and analyzed through descriptive content analysis. Systematic reviews' reference lists validated the scoping review query. The literature search yielded 4 systematic reviews, revealing 15 heterogeneous studies across the population, index test, and reference standard domains. The scoping review identified 49 studies, with 27 overlapping with the systematic reviews. Population characteristics varied, with one-third (16/49, 33%) of studies reporting fewer than 100 samples; most studies (41/49, 84%) reported on the type of lesion, and most (20/49, 41%) teledermoscopy consultations took place in secondary care. One-fifth (11/49, 22%) did not describe inclusion or exclusion criteria, or the criteria varied highly. Index test characteristics showed differences in clinical expertise, profession, and training in dermatoscopic photography, and 59% (29/49) did not report on 1 or more index test characteristics. Image quality and clinical information reporting likewise varied. Reference standard characteristics involved teledermatologists' assessment, but 16 studies did not report teledermatologists' experience levels. Most studies (26/49, 53%) used histopathology as a gold standard. The heterogeneity in the population, index tests, and reference standard domains across teledermoscopy diagnostic outcome studies underscores the need for standardized reporting. This hinders the synopsis of teledermoscopy diagnostic outcomes in systematic reviews and limits the integration of research results into practice. Adopting a (tailored) STARD (Standards for Reporting Diagnostic Accuracy Studies) checklist for teledermoscopy diagnostic outcome studies is recommended to enhance the consistency and comparability of outcomes. We suggest performing a Delphi study to gather consensus on the tailored STARD guideline.

Tethered gold-liposome nanoparticles for iontophoresis-enhanced topical delivery for anti-inflammation

Topical delivery is a widely utilized drug administration route globally due to its convenience, non-invasiveness, and minimal drug metabolism. However, the skin barrier, particularly the stratum corneum (SC), poses a challenge by hindering drug absorption and permeation. To address this issue, green-synthesized gold nanoparticles (AuG) were loaded into liposomes to increase electron density and facilitate cathodal iontophoresis for topical drug delivery enhancement. Ginger rhizome extract served as a model hydrophobic compound encapsulated within the gold-liposomes to evaluate its anti-inflammatory effect. Our study demonstrated that the ginger-gold loaded liposomes (AuGL) enhanced cellular uptake of the hydrophobic payload and improved drug penetration through skin layers compared to conventional liposomes following iontophoresis. We investigated different ratios of gold nanoparticles to liposomes and characterized the nanoparticles using DLS, TEM, and FT-IR. Additionally, AuGL significantly inhibited nitric oxide release and TNF-α in RAW 264.7 cells. Furthermore, we formulated AuGL-loaded topical emulgel, which exhibited good stability under accelerated conditions. Our findings suggest that the AuGL combined with iontophoresis offers superior benefits for topical drug delivery, potentially enabling the treatment of skin inflammations and inflammation-related skin cancers.

Advances in Deep Learning for Skin Cancer Diagnosis

The most prevalent type of cancer worldwide is known as skin cancer. Early detection is critical because if left undiagnosed in the primary stage, it might be fatal. Although there are differences within the class and high inter-class similarities, it is too difficult to distinguish with the naked eye. Owing to the disease's global prevalence, a number of deep learning based automated systems were created thus far to help doctors identify skin lesions early on. Using pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks (CNNs), we trained VGG19 on the HAM10000 dataset. The optimal performance was observed with FT. The model that was created, which yielded an accuracy that was greater overall than the one used in transfer learning, was 82.4±1.9 %. By offering a second opinion and supporting the clinician's diagnosis, this performance could lower morbidity and treatment costs.

Suprathel Versus Hypafix in the Management of Split-Thickness Donor Site Wounds in the Elderly: A Randomised Controlled Trial

(1) Background: Effective wound management aims for expedited healing, improved functional and scar outcomes, and reduced complications including infection. Delayed wound healing remains a prevalent problem in the elderly. Suprathel is a synthetic absorbable skin substitute and an attractive option in partial thickness wounds. The objective of this randomised controlled study was to assess the effect of skin substitute dressings on elderly split-skin graft (STSG) donor sites, evaluating time to heal, pain, itch and scar outcome. (2) Methods: 40 patients over 65 undergoing split-thickness skin grafting for non-melanoma skin cancer excision were randomised to STSG donor site dressings with either Suprathel or Hypafix. Patients were followed up weekly until healed and at 13 weeks post-procedure. (3) Results: There was no significant difference in time to healing, pain, itch, or scar outcome at 13 weeks between the two groups. The mean time to healing was 31.7 days for the skin substitute group and 27.3 days for the adhesive tape control group (p = 0.182). (4) Conclusions: Both dressings are appropriate for STSG donor sites. Hypafix remains a cost-effective dressing of choice for donor sites. Benefits demonstrated in other studies using skin substitutes have not translated into the elderly population. There remains scope in developing dressings that reduce elderly donor site morbidity.

A student-community partnership to enhance cancer research training

BackgroundDespite the importance of community involvement in research, little formal training in community outreach and engagement (COE) is offered to cancer research trainees. A collaboration between the Office of COE and the Office of Cancer Research Training and Education Coordination (CRTEC) at the Sidney Kimmel Comprehensive Cancer Center at Jefferson led to the COE-CRTEC Trainee Working Group, a unique program in which trainees in cancer research each created a novel COE initiative.MethodsFour cancer research trainees were selected to serve as COE Program Liaisons (CPLs), each aligned with one of the four cancer center research programs. Each CPL developed, implemented, and evaluated a project that enhanced the bidirectional relationship between their research and the community. Trainees were provided a modest budget, support from the Office of COE, and a requirement to complete the project within one academic year.ResultsProjects included a cancer education seminar for older adults at a senior center, a prostate cancer education and screening event at a predominantly African American church, a video demonstrating a day in the life of a skin cancer researcher, and a podcast that featured SKCCC investigators answering research questions from community members.ConclusionStudents who would not typically be exposed to COE training gained experience developing, implementing, and evaluating community-based initiatives. Projects were diverse in topic and approach, reflecting the diversity of the trainees and the community. Allowing trainees, those who are the next generation of cancer researchers, to design community-based research may lead to more patient-centered research in the future.

Role of spexin and DARS2 as potential biomarkers in basal cell carcinoma and cutaneous malignant melanoma diagnosis, and as therapeutic targets.

Basal cell carcinoma (BCC) is a slowly progressive, locally aggressive and rarely metastasizing cancer, and although its mortality is low, its morbidity and cost of disease are high. While BCC is more common, cutaneous malignant melanoma (CMM) is significant due to its higher mortality rate. These patients can be treated, but recurrence, metastasis and mortality may occur in such patients. Various environmental, phenotypic and genotypic factors, especially ultraviolet (UV) radiations, play a role in the etiology of BCC and CMM. Histopathological examination continues to be the "gold standard" in their diagnosis. Spexin (SPX) and DARS2 are newly discovered proteins linked to many diseases, including cancer. These proteins may have an effect on the development and expression of skin cancers such as BCC and CMM. In this study, we evaluated the potential of SPX and DARS2 expressions as immunohistochemical biomarkers in the differential diagnosis of BCC and CMM. This study was conducted retrospectively using samples taken from the pathology laboratory. A total of 180 patient samples were used. The control group consisted of healthy skin tissues of the patients, and the other groups consisted of BCC and CMM tissues of the same patients. Tissue samples of all three groups were evaluated immunohistochemically with SPX and DARS2. The immunoreactivity of SPX was found to be higher in BCC and CMM tissue samples than in healthy skin tissues in the control group. DARS2 immunoreactivity was found to be higher in CMM tissues compared to the other two groups, and statistically significant in BCC tissues when compared with healthy control group tissues. SPX can be used as an immunohistochemical biomarker in the diagnosis of BCC and CMM. Since DARS2 expression is statistically more significant in CMM tissues than in BCC tissues, it can be used in differential diagnosis.

Expanding the dermatological acronym from BLEND TAN EGG to PEGG: A rare case of painful palmar neurofibroma

Painful skin tumors present a diagnostic challenge due to their diverse presentations. The acronym blue rubber bleb nevus, leiomyoma, eccrine spiradenoma, neuroma, dermatofibroma, tufted angioma, angiolipoma, neurilemmoma, endometrioma, granular cell tumor, and glomus tumor (BLEND TAN EGG) encompasses some of the common causes of painful skin tumors. This case report documents a 24-year-old woman presenting with a painful, solitary palmar nodule confirmed as a neurofibroma after an excisional biopsy. Neurofibromas are frequently asymptomatic, but their location within the tight palmar fascia can lead to significant discomfort. The rarity of this presentation and the difficulty in pre-operative diagnosis highlight the importance of considering palmar neurofibromas as a differential diagnosis for painful skin tumors, particularly in the hand and feet. To enhance the educational value of the existing acronym, we propose its expansion to “BLEND TAN PEGG,” where “P” signifies palmar or plantar neurofibroma.

Surgical Margins of Nonmelanoma Skin Cancers in Mohs Surgery: Dermoscopy Versus Naked Eye.

Mohs micrographic surgery (MMS) offers high cure rates for nonmelanoma skin cancer (NMSC) but relies on precise margin assessment, traditionally initiated by the naked eye. Dermoscopy has emerged as a potential tool to improve margin delineation. To compare the efficacy of dermoscopic versus naked eye evaluation in marking initial MMS margins for NMSC. A multicenter, prospective study on patients with NMSC undergoing MMS was conducted. Patients were randomized into dermoscopy and naked eye groups. Statistical analysis was performed using statistical package for social sciences. A total of 127 patients were enrolled, with 63 in the naked eye group and 64 in the dermoscopy group. No significant differences in age, sex distribution, or histological subtype were found. The average number of MMS stages and accuracy of initial margins were comparable between groups. Despite its potential, the authors' study found no significant reduction in total MMS stages using dermoscopy. Novel imaging modalities or standardized dermoscopic criteria should be explored to enhance margin assessment accuracy and improve outcomes in NMSC treatment. Dermoscopic evaluation of presurgical margins does not effectively assess lateral/deep margins nor contribute to a reduction in total MMS stages. Improving the authors' understanding of presurgical margin assessment techniques can lead to better treatment outcomes for NMSC.

AI in Psoriatic Disease: Scoping Review.

Artificial intelligence (AI) has many applications in numerous medical fields, including dermatology. Although the majority of AI studies in dermatology focus on skin cancer, there is growing interest in the applicability of AI models in inflammatory diseases, such as psoriasis. Psoriatic disease is a chronic, inflammatory, immune-mediated systemic condition with multiple comorbidities and a significant impact on patients' quality of life. Advanced treatments, including biologics and small molecules, have transformed the management of psoriatic disease. Nevertheless, there are still considerable unmet needs. Globally, delays in the diagnosis of the disease and its severity are common due to poor access to health care systems. Moreover, despite the abundance of treatments, we are unable to predict which is the right medication for the right patient, especially in resource-limited settings. AI could be an additional tool to address those needs. In this way, we can improve rates of diagnosis, accurately assess severity, and predict outcomes of treatment. This study aims to provide an up-to-date literature review on the use of AI in psoriatic disease, including diagnostics and clinical management as well as addressing the limitations in applicability. We searched the databases MEDLINE, PubMed, and Embase using the keywords "AI AND psoriasis OR psoriatic arthritis OR psoriatic disease," "machine learning AND psoriasis OR psoriatic arthritis OR psoriatic disease," and "prognostic model AND psoriasis OR psoriatic arthritis OR psoriatic disease" until June 1, 2023. Reference lists of relevant papers were also cross-examined for other papers not detected in the initial search. Our literature search yielded 38 relevant papers. AI has been identified as a key component in digital health technologies. Within this field, there is the potential to apply specific techniques such as machine learning and deep learning to address several aspects of managing psoriatic disease. This includes diagnosis, particularly useful for remote teledermatology via photographs taken by patients as well as monitoring and estimating severity. Similarly, AI can be used to synthesize the vast data sets already in place through patient registries which can help identify appropriate biologic treatments for future cohorts and those individuals most likely to develop complications. There are multiple advantageous uses for AI and digital health technologies in psoriatic disease. With wider implementation of AI, we need to be mindful of potential limitations, such as validation and standardization or generalizability of results in specific populations, such as patients with darker skin phototypes.

Mosaic aneuploidy in a patient with periocular basal cell carcinoma. Report of a case

Basal cell carcinoma constitutes approximately 80% of non-melanocytic skin tumors and originates in the basal layer of the epidermis and its appendages. Genetic alterations that affect neoplastic development have been identified, caused by external physical or chemical agents, hereditary, chromosomal rearrangements, allelic loss and gene amplification. This article presents a white-skinned male individual, 64 years old, with a diagnosis of periocular basal cell carcinoma, who underwent a cytogenetic study of the tumor tissue and a mosaic chromosomal aneuploidy was identified.The presence of mosaic aneuploidy in patients with periocular basal cell carcinoma suggests that chromosomal aberrations may play an important role in tumor progression and aggressiveness. This finding highlights the need to integrate cytogenetic studies in the clinical evaluation of skin tumors, facilitating a better understanding of the underlying biological mechanisms, opening new avenues for therapeutic research.