Dermatopathology

Melanoma is a highly aggressive skin cancer primarily linked to ultraviolet (UV) radiation. However, the potential role of ionizing radiation from radiotherapy in melanoma development remains unclear. This review synthesizes data from epidemiologic studies and case reports on melanoma after radiation exposure. Evidence indicates that childhood radiotherapy, even at low doses, is associated with an increased melanoma risk, plausibly reflecting the heightened radiosensitivity of developing melanocytes. Occupational radiation exposure, particularly in earlier eras with insufficient shielding, also appears to elevate risk. In patients exposed to radiation in adulthood, findings are mixed: large population datasets suggest a modest increase in melanoma following therapeutic radiation, whereas some case–control analyses do not demonstrate a clear dose–response relationship. UV radiation promotes melanomagenesis through direct DNA photoproducts driving characteristic C>T transitions at dipyrimidine sites, alongside oxidative stress and local immune modulation that facilitate malignant transformation. Collectively, individuals with prior radiotherapy, especially those irradiated in childhood, should be considered at increased melanoma risk and may benefit from long-term, targeted surveillance of irradiated fields. Awareness of this association between radiation exposure and melanoma may also support clinicopathologic correlation during the diagnostic evaluation of melanocytic lesions. Future work should define dose–response relationships in contemporary radiotherapy methods, characterize molecular signatures of ionizing radiation-associated melanomas, and establish evidence-based surveillance strategies for high-risk cohorts.

Melanoma subtyping plays a vital role in histopathological diagnosis, informing prognosis and, in some cases, guiding targeted therapy. However, conventional histologic classification is constrained by inter-rater reliability, morphologic overlap, and the underrepresentation of rare subtypes. Deep learning (DL)—particularly convolutional neural networks (CNNs)—presents a compelling opportunity to enhance diagnostic precision and reproducibility through automated analysis of histopathologic slides. This review examines the clinical importance and diagnostic challenges of melanoma subtyping, outlines core DL methodologies in dermatopathology, and synthesizes current advances in applying DL to subtype classification. Pertinent limitations including dataset imbalance, a lack of interpretability, and domain generalizability are discussed. Additionally, emerging directions such as multimodal integration, synthetic data generation, federated learning, and explainable AI are highlighted as potential solutions. As these technologies mature, DL holds considerable promise in advancing melanoma diagnostics and supporting more personalized, accurate, and equitable patient care.

A 56-year-old patient presented to our dermatology clinic with asymmetric hyperpigmentation on her lower lip, which had developed over the previous six to twelve months. Her medical history included kidney and pancreas transplants, requiring chronic immunosuppression, and two lip filler injections with hyaluronic acid (HA). Clinical examination revealed irregular pigmented macules limited strictly to the lower lip. Histological analysis showed epidermal melanosis, pigmentary incontinence, solar elastosis, and amorphous dermal HA deposits, without evidence of melanocytic hyperplasia or granulomatous inflammation.