Search Results (57)

Cutaneous manifestations can serve as early and sometimes the first clinical indicators in various hereditary cancer predisposition syndromes. This review provides a comprehensive overview of the dermatological signs associated with these syndromes, aiming to facilitate their recognition in clinical practice. Hereditary Breast and Ovarian Cancer syndrome is notably linked to an increased risk of melanoma. BAP1 tumor predisposition syndrome is characterized by BAP1-inactivated melanocytic tumors. Muir–Torre syndrome, a variant of Lynch syndrome, presents with distinctive cutaneous neoplasms such as sebaceous carcinomas, sebaceous adenomas, and keratoacanthomas. PTEN hamartoma tumor syndrome commonly features hamartomatous growths, trichilemmomas, acral keratoses, oral papillomas, and genital lentiginosis. Gorlin syndrome is marked by basal cell carcinomas and palmoplantar pits, while Peutz–Jeghers syndrome is identified by mucocutaneous pigmentation. In familial adenomatous polyposis, the cutaneous findings include epidermoid cysts, fibromas, desmoid tumors, and lipomas. Additionally, we examined monogenic disorders associated with cancer risk and skin involvement, such as xeroderma pigmentosum, neurofibromatosis type 1, familial atypical multiple-mole melanoma syndrome, and Fanconi anemia. The early recognition of these dermatologic features is essential for a timely diagnosis and the implementation of appropriate surveillance strategies in individuals with hereditary cancer syndromes. Full article

Background: Cutaneous toxicities associated with CDK4/6 inhibitors are uncommon but may affect treatment adherence. We present the case of a patient with advanced breast cancer who developed vitiligo-like lesions after initiating ribociclib, contributing to the growing evidence of this under-recognized adverse effect. Methods: We present the case of a 72-year-old woman diagnosed in 2007 with early-stage, luminal A, HER2-negative breast cancer, initially treated with surgery and tamoxifen. In 2022, she experienced locoregional recurrence with bone metastases. In January 2023, she began treatment with ribociclib plus letrozole. Two months later, she developed intense pruritus, xerosis, and paresthesia, followed by hypopigmented lesions on her face and upper extremities. Clinical evaluation, supported by photographs and a skin biopsy (led to a diagnosis of ribociclib-induced vitiligo. Management included dose adjustments to the ribociclib and dermatologic treatments, including topical corticosteroids, antihistamines, and short courses of oral prednisone. Results: By September 2024, her skin lesions had stabilized and her pruritus improved with a reduced dose of ribociclib (one tablet per day). However, the hypopigmented patches persisted, mainly on her face and extremities. Despite these cutaneous effects, she maintained an acceptable quality of life and continued effective oncologic treatment. Conclusions: This case highlights the importance of early recognition and management of ribociclib-related cutaneous toxicities. A multidisciplinary approach is essential to minimize adverse effects without compromising therapeutic efficacy. Further research into the dermatologic manifestations of targeted therapies is needed to optimize patient care. Full article

Background/Objectives: We describe a case of intravascular large B-cell lymphoma (IVLBCL) presenting with recurrent cerebral infarctions and review similar reported cases. Our aim is to explore potential early diagnostic markers and discuss their prognostic implications. Methods/Results: A 79-year-old man with a history of hypertension, hyperuricemia, and postoperative bladder cancer presented with five to six cerebral infarctions over an 11-month period, despite successive changes in antiplatelet and anticoagulant medications. Neurological examination revealed decreased pain sensation, bilateral hearing loss, and right thenar atrophy. Laboratory studies showed elevated inflammatory markers and soluble IL-2 receptor. CSF analysis revealed elevated protein, β2-microglobulin, IL-6, and IL-10 levels. A skin biopsy was performed to investigate suspected IVLBCL. Histopathological examination of the skin biopsy revealed large pleomorphic CD20-positive cells within the vasculature, confirming a diagnosis of IVLBCL. The patient was treated with chemotherapy, including dose-adjusted R-CHOP and high-dose methotrexate, and achieved complete remission. No recurrence of cerebral infarction was observed during a two-year follow-up period. Conclusions: This case highlights the importance of considering IVLBCL in patients with recurrent strokes of unknown etiology, especially when laboratory or imaging findings suggest systemic involvement. Early recognition and appropriate tissue diagnosis, such as skin biopsy, are essential for timely treatment and favorable prognosis. Full article

Background: Artificial intelligence (AI) developed for skin cancer recognition has been shown to have comparable or superior performance to dermatologists. However, it is uncertain if current AI models trained predominantly with lighter Fitzpatrick skin types can be effectively adapted for Asian populations. Objectives: A systematic review was performed to summarize the existing use of artificial intelligence for skin cancer detection in Asian populations. Methods: Systematic search was conducted on PubMed and EMBASE for articles published regarding the use of artificial intelligence for skin cancer detection amongst Asian populations. Information regarding study characteristics, AI model characteristics, and outcomes was collected. Conclusions: Current studies show optimistic results in utilizing AI for skin cancer detection in Asia. However, the comparison of image recognition abilities might not be a true representation of the diagnostic abilities of AI versus dermatologists in the real-world setting. To ensure appropriate implementation, maximize the potential of AI, and improve the transferability of AI models across various Asian genotypes and skin cancers, it is crucial to focus on prospective, real-world-based practice, as well as the expansion and diversification of existing Asian databases used for training and validation. Full article

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model’s resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice. Full article

Skin paraneoplastic syndromes (SPNSs) are a group of disorders that arise as a consequence of cancer but are not directly related to the tumor mass itself. This review aims to provide a comprehensive overview of these syndromes, encompassing their pathophysiology, clinical features, diagnostic approaches, differential diagnosis, and management strategies. These syndromes, which include conditions such as Bazex syndrome, acanthosis nigricans, dermatomyositis, and necrolytic migratory erythema often manifest prior to or concurrently with a cancer diagnosis, serving as potential early warning signs of underlying malignancies. This review delves into the spectrum of SPNSs and their associations with specific cancer types. Special emphasis is placed on the critical role of dermatologists and oncologists in identifying these skin manifestations as potential markers of malignancy. By raising awareness of SPNSs, this paper highlights the pivotal importance of prompt recognition and intervention in reducing cancer-related mortality. Full article

Background: Melanoma is a highly aggressive form of skin cancer, necessitating early and accurate detection for effective treatment. This study aims to develop a novel classification system for melanoma detection that integrates Convolutional Neural Networks (CNNs) for feature extraction and the Aquila Optimizer (AO) for feature dimension reduction, improving both computational efficiency and classification accuracy. Methods: The proposed method utilized CNNs to extract features from melanoma images, while the AO was employed to reduce feature dimensionality, enhancing the performance of the model. The effectiveness of this hybrid approach was evaluated on three publicly available datasets: ISIC 2019, ISBI 2016, and ISBI 2017. Results: For the ISIC 2019 dataset, the model achieved 97.46% sensitivity, 98.89% specificity, 98.42% accuracy, 97.91% precision, 97.68% F1-score, and 99.12% AUC-ROC. On the ISBI 2016 dataset, it reached 98.45% sensitivity, 98.24% specificity, 97.22% accuracy, 97.84% precision, 97.62% F1-score, and 98.97% AUC-ROC. For ISBI 2017, the results were 98.44% sensitivity, 98.86% specificity, 97.96% accuracy, 98.12% precision, 97.88% F1-score, and 99.03% AUC-ROC. The proposed method outperforms existing advanced techniques, with a 4.2% higher accuracy, a 6.2% improvement in sensitivity, and a 5.8% increase in specificity. Additionally, the AO reduced computational complexity by up to 37.5%. Conclusions: The deep learning-Aquila Optimizer (DL-AO) framework offers a highly efficient and accurate approach for melanoma detection, making it suitable for deployment in resource-constrained environments such as mobile and edge computing platforms. The integration of DL with metaheuristic optimization significantly enhances accuracy, robustness, and computational efficiency in melanoma detection. Full article

The association between psoriasis and increased cancer risk is gaining recognition as studies reveal shared inflammatory and immune pathways. This review examines the relationship between psoriasis and neoplasia, focusing on cancer risk factors in psoriasis patients, the biological pathways underlying this connection, and the impact of various psoriasis treatments on cancer development. Psoriasis patients have a heightened incidence of certain cancers, such as lymphomas, skin cancers, and urological malignancies, potentially linked to immune dysregulation and chronic inflammation. Immunomodulatory treatments for psoriasis, including conventional systemic therapies and biologics, present varied cancer risks, with others, such as phototherapy, associated with an elevated risk of skin cancers. For oncologic patients with psoriasis, management necessitates a tailored approach, balancing effective psoriasis control with minimizing cancer progression risks. The emergence of IL-17 inhibitors, IL-23 inhibitors, and small-molecule therapies offers promising therapeutic alternatives with favorable safety profiles for these patients. This review underscores the need for interdisciplinary collaboration to optimize care for patients managing both psoriasis and malignancy. Full article

Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, showing encouraging outcomes in terms of enhancing diagnostic precision. In this paper, multimodal Explainable Artificial Intelligence (XAI) is presented that offers explanations that (1) address a gap regarding interpretation by identifying specific dermoscopic features, thereby enabling (2) dermatologists to comprehend them during melanoma diagnosis and allowing for an (3) evaluation of the interaction between clinicians and XAI. The specific goal of this article is to create an XAI system that closely aligns with the perspective of dermatologists when it comes to diagnosing melanoma. By building upon previous research on explainability in dermatology, this work introduces a novel soft attention mechanism, called Convolutional Spiking Attention Module (CSAM), to deep neural architectures, which focuses on enhancing critical elements and reducing noise-inducing features. Two instances of the proposed CSAM were placed inside the proposed Spiking Attention Block (SAB). The InceptionResNetV2, DenseNet201, and Xception architectures with and without the proposed SAB mechanism were compared for skin lesion classification. Pretrained networks with SAB outperform state-of-the-art methods on the HAM10000 dataset. The proposed method used the ISIC-2019 dataset for the crossdataset validation process. The proposed model provides attention regarding cancer pixels without using an external explainer, which proves the importance and significance of the SAB module. Full article

Capillary leak syndrome is a rare complication of cancer, particularly of hematologic malignancies. The syndrome was first described as an idiopathic entity; however, increasingly, more cases are being reported in association with cancers and other conditions. Diagnosis stems from the recognition of the double paradox, consisting of severe generalized oedema and hypotension, accompanied by hallmark laboratory modifications. Concurrent conditions in patients with malignancies can alter laboratory findings and make the diagnosis a challenge. This report presents the case of a patient with capillary leak syndrome and an atypical presentation, with generalized skin rash and transcutaneous exudation occurring concurrently with anaplastic large T cell lymphoma, macrophage activation syndrome, and cytopenias. Symptom-specific treatment with diuretics and albumin was ineffective in the case of our patient; however, the CLS remitted promptly with cancer-specific therapy. No treatment has proved to be generally effective against CLS up to date, as is the case for this patient. Thus, the rapid recognition of cancer-associated capillary leak syndrome and the initiation of cancer-specific treatment proves to be the better approach and is key to avoiding unnecessary delays and ineffective treatments targeted specifically at CLS. Full article

Melanoma (malignant melanoma, MM) is an aggressive malignant skin cancer with an increasing incidence rate. The complete pathogenesis of MM in not clear. Due to DNA damage, mutations, dysregulation of growth factors, inactivation of tumor suppressor genes, and activation of oncogenes, excessive uncontrolled growth of abnormal melanocytes occurs in melanomas. Caspases are a group of proteolytic enzymes that participate in several processes important in regulating mechanisms at the cellular level. They play a role in cell homeostasis and programmed cell death (apoptosis) and in the regulation of non-apoptotic cell death processes. Dysregulation of caspase activation plays a role in the etiology of cancers, including melanoma. Caspases can initiate and execute apoptosis and are involved in regulating cell death and controlling tumor growth. These enzymes also inhibit tumor growth by cleaving and inactivating proteins that are involved in cell proliferation and angiogenesis. Moreover, caspases are involved in the activation of immune processes through the processing and presentation of tumor antigens, which facilitates recognition of the tumor by the immune system. The role of caspases in melanoma is complex, and they may inhibit melanoma growth and progression. This work aims to review the current knowledge of the role of individual caspases in melanoma pathogenesis. Full article

Despite the clear indications and worldwide application of specific guidelines, the recognition of Infective Endocarditis (IE) may be challenging in day-to-day clinical practice. Significant changes in the epidemiological and clinical profile of IE have been observed, including variations in the populations at risk and an increased incidence in subjects without at-risk cardiac disease. Emergent at-risk populations for IE particularly include immunocompromised patients with a comorbidity burden (e.g., cancer, diabetes, dialysis), requiring long-term central venous catheters or recurrent healthcare interventions. In addition, healthy subjects, such as skin-contact athletes or those with piercing implants, may be exposed to the transmission of highly virulent bacteria (through the skin or mucous), determining endothelial lesions and subsequent IE, despite the absence of pre-existing at-risk cardiac disease. Emergent at-risk populations and clinical presentation changes may subvert the conventional paradigm of IE toward an unexpected clinical scenario. Owing to its unusual clinical context, IE might be overlooked, resulting in a challenging diagnosis and delayed treatment. This review, supported by a series of clinical cases, analyzed the subtle and deceptive phenotypes subtending the complex syndrome of unexpected IE. The awareness of an unexpected clinical course should alert clinicians to also consider IE diagnosis in patients with atypical features, enhancing vigilance for preventive measures in an emergent at-risk population untargeted by conventional workflows. Full article

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has recently made great advances in dermatology with respect to the classification and malignancy prediction of skin diseases. In this article, we demonstrate how we have used a similar technique to build a mobile application to classify skin diseases captured by patients with their personal smartphone cameras. We used a CNN classifier to distinguish four subtypes of dermatological diseases the patients might have (“pigmentation changes and superficial infections”, “inflammatory diseases and eczemas”, “benign tumors, cysts, scars and callous”, and “suspected lesions”) and their severity in terms of morbidity and mortality risks, as well as the kind of medical consultation the patient should seek. The dataset used in this research was collected by the Department of Telemedicine of Albert Einstein Hospital in Sao Paulo and consisted of 146.277 skin images. In this paper, we show that our CNN models with an overall average classification accuracy of 79% and a sensibility of above 80% implemented in personal smartphones have the potential to lower the frequency of skin diseases and serve as an advanced tracking tool for a patient’s skin-lesion history. Full article

The occurrence of second primary malignancies is becoming increasingly important among cancer survivors. Melanoma, an aggressive neoplasm originating from the melanocytes, is responsible for most skin cancer-related deaths. This review aims to explore the risk of melanoma occurrence as a second primary cancer after the most common subtypes of hematologic neoplasia, a malignant disease originating from myeloid or lymphocytic cell lineages. Chronic lymphocytic leukemia (CLL) and non-Hodgkin lymphoma (NHL) are among the most associated subtypes with melanoma development. We also discuss the underlying hypotheses that may explain the associations between these malignancies and the impact of melanoma on survival. The review emphasizes the importance of increasing awareness of melanoma risk in hematologic cancer survivors, as it can lead to prompt recognition, improved skin surveillance, and better survival outcomes. Full article

In fair-skinned populations worldwide, skin cancer is a serious public health threat. A significant percentage of all reported occupational diseases fall back on skin cancer. Over the past few decades, there has been a rise in the frequency of skin cancer diagnoses among outdoor workers. The main cause of non-melanoma skin cancer is solar ultraviolet radiation (UVR), which is also the most common occupational carcinogenic exposure in terms of the number of exposed workers (i.e., outdoor workers). Sun protection—and concomitantly the prevention of occupational skin cancer—is a component of workplace safety. The risks of solar UVR exposure at work are often disregarded in practice, despite the recent recognition of the need for measures to support outdoor workers’ sun protection behavior. It is anticipated that occupational dermatology will become increasingly focused on sun safety in the coming decades. To handle current hurdles in a sustainable manner, the full range of preventive measures should be utilized. Existing strategies for the prevention of occupational skin cancer might be evolved and enriched by new (educational) concepts, methods, and/or technologies. In this, not only components of general prevention and individual prevention but also setting-based prevention and behavior-based prevention might be freshly thought through. Full article