BACKGROUND: The aim was to identify age‐ related and gender‐related differences in the clinical presentation and outcome of patients with primary cutaneous melanoma (CM).METHODS: A total of 4785 CM patients without clinical evidence of metastasis diagnosed during the period 1976–2001 in southern Germany included in the analysis. Kaplan‐Meier analyses were performed to estimate and to compare disease‐specific survival (DSS) and survival after first recurrence (SAR). The Cox proportional hazards model was used to evaluate the effect of multiple variables on DSS and SAR.RESULTS: Increasing age and male gender were independently associated with thicker tumors (>2 mm) and histologic ulceration (P < .001). Patients older than 65 years had lower 10‐year DSS than younger patients (81.8% vs 88.4%, P < .001) and this difference was more pronounced in women (P < .001) than in men (P = .06). Males had lower 10‐year DSS than females (83.5% vs 88.5%, P < .001) but this difference did not reach statistical significance in patients older than 65 years (P = .162). In multivariate analysis adjusted for tumor thickness, ulceration, anatomic site, histologic subtype, DSS, site of first recurrence, time trend, sentinel lymph node status, age, and gender were independent predictors of DSS and SAR (P < .05).CONCLUSIONS: Older age and male gender are associated with prognostically unfavorable primary CM. Expansion of current preventive strategies to target these subgroups is warranted. Moreover, age and gender are independent predictors of the outcome of CM patients. Females have a better prognosis than males but this difference disappears after the age of 65. Younger patients have a more favorable prognosis than older patients, a difference more pronounced in women. Cancer 2008. © 2008 American Cancer Society.

Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists? Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included. 37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI. XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking.

Until recently, distant metastatic melanoma was considered refractory to systemic therapy. A better understanding of the interactions between tumors and the immune system and the mechanisms of regulation of T-cells led to the development of immune checkpoint inhibitors. This review summarizes the current novel data on the treatment of metastatic melanoma with anti-programmed cell death protein 1 (PD-1) antibodies and anti-PD-1-based combination regimens, including clinical trials presented at major conference meetings. Immune checkpoint inhibitors, in particular anti-PD-1 antibodies such as pembrolizumab and nivolumab and the combination of nivolumab with the anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibody ipilimumab can achieve long-term survival for patients with metastatic melanoma. The anti-PD-1 antibodies nivolumab and pembrolizumab were also approved for adjuvant treatment of patients with resected metastatic melanoma. Anti-PD-1 antibodies appear to be well tolerated, and toxicity is manageable. Nivolumab combined with ipilimumab achieves a 5 year survival rate of more than 50% but at a cost of high toxicity. Ongoing clinical trials investigate novel immunotherapy combinations and strategies (e.g., Talimogene laherparepvec (T-VEC), Bempegaldesleukin (BEMPEG), incorporation or sequencing of targeted therapy, incorporation or sequencing of radiotherapy), and focus on poor prognosis groups (e.g., high tumor burden/LDH levels, anti-PD-1 refractory melanoma, and brain metastases).

Targeted therapy with BRAF inhibitors (BRAFi) and MEK inhibitors (MEKi) provides rapid disease control with high response rates in patients with BRAF-mutant metastatic melanoma. However, the majority of patients develop resistance to therapy during the course of therapy. Immune checkpoint inhibitors show a slower onset of action with lower response rates, with responders showing sustained response. The combination of BRAFi/MEKi and immune checkpoint inhibitors combines the hope for a fast, reliable and lasting response to therapy. Preclinical data supports this hypothesis. With the help of the PubMed database, a comprehensive search and analysis of preclinical and clinical studies on the combination of BRAFi/MEKi with immune checkpoint inhibitors was performed and yielded the following results: 1) In vivo, BRAFi and MEKi have no negative effects on immune cells; BRAFi and MEKi generate 2) an immune stimulating tumor microenvironment, 3) an increased infiltration of immune cells into the tumors, 4) a better recognition of melanoma cells by immune effector cells, and 5) a better functionality of the immune effector cells. In addition, in vivo experiments 6) demonstrated a superiority of the combination treatment compared to the individual strategies in both BRAF-mutant and BRAF wild-type melanomas. In summary, available data show that both BRAFi and MEKi have beneficial effects on the antitumor immunity and the tumor microenvironment as a whole, which is mediated by different mechanisms. Currently, clinical studies are underway to investigate combinations of BRAFi and MEKi with immune checkpoint inhibitors. The results of these studies are eagerly awaited.