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Background: Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. However, there are few prospective studies confirming these promising results. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes, preventing a fair and thorough evaluation of AI and its generalizability, a crucial aspect for its application in the clinical setting., Methods: Therefore, we assessed "All Data are Ext" (ADAE), an established open-source ensemble algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists on a prospectively collected, external, heterogeneous test set comprising eight distinct hospitals, four different camera setups, rare melanoma subtypes, and special anatomical sites. We advanced the algorithm with real test-time augmentation (R-TTA, i.e., providing real photographs of lesions taken from multiple angles and averaging the predictions), and evaluated its generalization capabilities., Results: Overall, the AI shows higher balanced accuracy than dermatologists (0.798, 95% confidence interval (CI) 0.779-0.814 vs. 0.781, 95% CI 0.760-0.802; p = 4.0e-145), obtaining a higher sensitivity (0.921, 95% CI 0.900-0.942 vs. 0.734, 95% CI 0.701-0.770; p = 3.3e-165) at the cost of a lower specificity (0.673, 95% CI 0.641-0.702 vs. 0.828, 95% CI 0.804-0.852; p = 3.3e-165)., Conclusion: As the algorithm exhibits a significant performance advantage on our heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists, particularly in diagnosing challenging cases., (© 2024. The Author(s).)

Competing Interests: Conflicts of interest Dr Utikal is on the advisory board or has received honoraria from Amgen, Bristol Myers Squibb, GSK, Immunocore, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche, and Sanofi outside the submitted work. Dr Meier has received speaker's fees or/and advisor's honoraria from Novartis, Roche, BMS, MSD, and Pierre Fabre. Dr Hobelsberger reports speaker's honoraria from Almirall, UCB, and AbbVie. Dr Gellrich has received speaker's fees or/and advisor's honoraria by Sun Pharma, Sanofi, and Merck. Dr Hauschild reports speaker's honoraria or consultancy fees from the following companies: Agenus, Amgen, BMS, Dermagnostix, Highlight Therapeutics, Immunocore, Incyte, IO Biotech, MerckPfizer, MSD, NercaCare, Novartis, Philogen, Pierre Fabre, Regeneron, Roche, Sanofi-Genzyme, Seagen, Sun Pharma, and Xenthera, outside the submitted work. Dr French is on the advisory board or has received consulting/speaker honoraria from for Galderma, Janssen, Leo Pharma, Eli Lilly, Almirall, Union Therapeutics, Regeneron, Novartis, Amgen, Abbvie, UCB, Biotest, and InflaRx. Dr Schlaak has received consultant or speaker fees or travel grants from BMS, MSD, Roche, Kyowa Kirin, Novartis, Sanofi Genzyme, Pierre Fabre, Sun Pharma, and Immunocore. Dr Erdmann declares honoraria from Bristol-Meyers Squibb, Immunocore, and Novartis outside the submitted work. Dr Haferkamp reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Novartis, Roche, BMS, Amgen, and MSD outside the submitted work. Dr Drexler has received honoraria from Pierre Fabre Pharmaceuticals and Novartis outside the submitted work. Dr Sondermann reports grants, speaker's honoraria, or consultancy fees from medi GmbH Bayreuth, Abbvie, Almirall, Amgen, Bristol-Myers Squibb, Celgene, GSK, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi Genzyme, and UCB outside the submitted work. Dr Schilling reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Incyte, Novartis, Roche, BMS, and MSD. Dr Goebeler has received speaker's honoraria and/or has served as a consultant and/or member of advisory boards for Almirall, Argenx, Biotest, Eli Lilly, Janssen Cilag, Leo Pharma, Novartis, and UCB, outside the submitted work. Dr Kather reports consulting services for Owkin, France, Panakeia, UK, and DoMore Diagnostics, Norway and has received honoraria for lectures by MSD, Eisai, and Fresenius. Dr Brinker reports owning a company that develops mobile apps (Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg). Author Haggenmüller, Author Maron, Author Hekler, Dr Krieghoff-Henning, Dr Gaiser, Dr Müller, Dr Fabian, Dr Sergon, Dr Weichenthal, Dr Heinzerling, Dr Schlager, Dr Ghoreschi, Dr Hilke, Dr Pochi, Dr Korsing, Dr Berking, Dr Heppt, Dr Schadendorf, Dr Fröhling, Author Kaminski, Author Doppler, and Author Bucher have no conflicts of interest to declare.

Exudates of nonhealing wounds contain drivers of pathogenicity. We utilized >800 exudates from nonhealing and healing wounds of diverse etiologies, collected by 3 different methods, to develop a wound-specific, cell-based functional biomarker assay. Human dermal fibroblast proliferation served as readout to (i) differentiate between healing and nonhealing wounds, (ii) follow the healing process of individual patients, and (iii) assess the effects of therapeutics for chronic wounds ex vivo. We observed a strong correlation between wound chronicity and inhibitory effects of individual exudates on fibroblast proliferation, with good diagnostic sensitivity (76-90%, depending on the sample collection method). Transition of a clinically nonhealing to a healing phenotype restored fibroblast proliferation and extracellular matrix formation while reducing inflammatory cytokine production. Transcriptional analysis of fibroblasts exposed to ex vivo nonhealing wound exudates revealed an induction of inflammatory cytokine and chemokine pathways and the unfolded protein response, indicating that these changes may contribute to the pathology of nonhealing wounds. Testing the wound therapeutics, PDGF and silver sulfadiazine, yielded responses in line with clinical experience and indicates the usefulness of the assay to search for and profile new therapeutics., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: Conflicts of interest Jochen S. Utikal is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, Immunocore, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche and Sanofi outside the submitted work. Friedegund Meier has received travel support and/or speaker's fees and/or advisor's honoraria by Novartis, Roche, BMS, MSD and Pierre Fabre and research funding from Novartis and Roche. Sarah Hobelsberger reports clinical trial support from Almirall and speaker's honoraria from Almirall, UCB and AbbVie and has received travel support from the following companies: UCB, Janssen Cilag, Almirall, Novartis, Lilly, LEO Pharma and AbbVie outside the submitted work. Sebastian Haferkamp reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Novartis, Roche, BMS, Amgen and MSD outside the submitted work. Konstantin Drexler has received honoraria from Pierre Fabre Pharmaceuticals and Novartis. Axel Hauschild reports clinical trial support, speaker's honoraria, or consultancy fees from the following companies: Agenus, Amgen, BMS, Dermagnostix, Highlight Therapeutics, Immunocore, Incyte, IO Biotech, MerckPfizer, MSD, NercaCare, Novartis, Philogen, Pierre Fabre, Regeneron, Roche, Sanofi-Genzyme, Seagen, Sun Pharma and Xenthera outside the submitted work. Lars E. French is on the advisory board or has received consulting/speaker honoraria from Galderma, Janssen, Leo Pharma, Eli Lilly, Almirall, Union Therapeutics, Regeneron, Novartis, Amgen, AbbVie, UCB, Biotest and InflaRx. Max Schlaak reports advisory roles for Bristol-Myers Squibb, Novartis, MSD, Roche, Pierre Fabre, Kyowa Kirin, Immunocore and Sanofi-Genzyme. Wiebke Sondermann reports grants, speaker's honoraria, or consultancy fees from medi GmbH Bayreuth, AbbVie, Almirall, Amgen, Bristol-Myers Squibb, Celgene, GSK, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi Genzyme and UCB outside the submitted work. Bastian Schilling reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Incyte, Novartis, Roche, BMS and MSD, research funding from BMS, Pierre Fabre Pharmaceuticals and MSD and travel support from Novartis, Roche, BMS, Pierre Fabre Pharmaceuticals and Amgen outside the submitted work. Matthias Goebeler has received speaker's honoraria and/or has served as a consultant and/or member of advisory boards for Almirall, Argenx, Biotest, Eli Lilly, Janssen Cilag, Leo Pharma, Novartis and UCB outside the submitted work. Michael Erdmann declares honoraria and travel support from Bristol-Meyers Squibb, Immunocore and Novartis outside the submitted work. Jakob N. Kather reports consulting services for Owkin, France, Panakeia, UK and DoMore Diagnostics, Norway and has received honoraria for lectures by MSD, Eisai and Fresenius. Titus J. Brinker reports owning a company that develops mobile apps (Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69,120 Heidelberg). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Importance: The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals., Objective: To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics., Design, Setting, and Participants: This multicentric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using histopathological whole-slide images prospectively acquired at 6 German university hospitals between April 2021 and February 2023 and benchmarked it using both a holdout and an external test dataset. Data analysis was performed from February to April 2023., Exposures: All whole-slide images were retrospectively analyzed by an AI-based classifier without influencing routine clinical care., Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUROC) served as the primary end point for evaluating the diagnostic performance. Secondary end points included balanced accuracy, sensitivity, and specificity., Results: The study included 1025 whole-slide images of clinically melanoma-suspicious skin lesions from 923 patients, consisting of 388 histopathologically confirmed invasive melanomas and 637 nevi. The median (range) age at diagnosis was 58 (18-95) years for the training set, 57 (18-93) years for the holdout test dataset, and 61 (18-95) years for the external test dataset; the median (range) Breslow thickness was 0.70 (0.10-34.00) mm, 0.70 (0.20-14.40) mm, and 0.80 (0.30-20.00) mm, respectively. The federated approach (0.8579; 95% CI, 0.7693-0.9299) performed significantly worse than the classical centralized approach (0.9024; 95% CI, 0.8379-0.9565) in terms of AUROC on a holdout test dataset (pairwise Wilcoxon signed-rank, P < .001) but performed significantly better (0.9126; 95% CI, 0.8810-0.9412) than the classical centralized approach (0.9045; 95% CI, 0.8701-0.9331) on an external test dataset (pairwise Wilcoxon signed-rank, P < .001). Notably, the federated approach performed significantly worse than the ensemble approach on both the holdout (0.8867; 95% CI, 0.8103-0.9481) and external test dataset (0.9227; 95% CI, 0.8941-0.9479)., Conclusions and Relevance: The findings of this diagnostic study suggest that federated learning is a viable approach for the binary classification of invasive melanomas and nevi on a clinically representative distributed dataset. Federated learning can improve privacy protection in AI-based melanoma diagnostics while simultaneously promoting collaboration across institutions and countries. Moreover, it may have the potential to be extended to other image classification tasks in digital cancer histopathology and beyond.

Background: Surgical site infection (SSI) has a significant impact on patients' morbidity and aesthetic results., Objective: To identify risk factors for SSI in dermatologic surgery., Patients and Methods: This prospective, single-centre, observational study was performed between August 2020 and May 2021. Patients that presented for dermatologic surgery were included and monitored for the occurrence of SSI. For statistical analysis, we used a mixed effects logistic regression model., Results: Overall, 767 patients with 1272 surgical wounds were included in the analysis. The incidence of SSI was 6.1%. Significant risk factors for wound infection were defect size over 10cm 2 (OR 3.64, 95% confidence interval [CI] 1.80-7.35), surgery of cutaneous malignancy (OR 2.96, CI 1.41-6.24), postoperative bleeding (OR 4.63, CI 1.58-13.53), delayed defect closure by local skin flap (OR 2.67, CI 1.13-6.34) and localisation of surgery to the ear (OR 7.75, CI 2.07-28.99). Wound localisation in the lower extremities showed a trend towards significance (OR 3.16, CI 0.90-11.09). Patient-related factors, such as gender, age, diabetes, or immunosuppression, did not show a statistically significant association with postoperative infection., Conclusion: Large defects, surgery of cutaneous malignancy, postoperative bleeding, and delayed flap closure increase the risk for SSI. High-risk locations are the ears and lower extremities., (© 2023 The Authors. International Wound Journal published by Medicalhelplines.com Inc and John Wiley & Sons Ltd.)

Background: Postoperative wound infections represent the most common complication in dermatosurgery. Given the low incidence and heterogeneous data, no standards for perioperative antibiotic prophylaxis (PAP) have been established in clinical practice., Objectives: To summarize the current evidence on risk factors for postoperative wound infection in dermatosurgery and the new recommendations on PAP., Materials and Methods: Relevant study data and current recommendations were summarized descriptively., Results: Current evidence suggests that the following factors are associated with an increased risk of wound infection after dermatosurgical procedures: surgery to the lower extremity or the ear, postoperative hemorrhage, defect closure by flap or skin graft, large wound defect, immunosuppression, and male sex. Probably not affecting the risk of infection are diabetes, obesity, age, smoking, use of a blood thinner, multiple surgeries, or wound healing by second intention. Not all risk factors affect the risk of infection equally. They must be weighted differently and only in combination do they increase the risk of wound infection in a clinically relevant way. According to a current position paper of the German Society for Dermatosurgery, the indication for PAP should be made individually and only if multiple factors are present. Furthermore, patients with increased risk for bacterial endocarditis or hematogenic endoprosthesis infection should receive PAP prior to septic skin surgery., Conclusion: In dermatologic surgery, PAP should be restricted to patients at high risk for wound infection. Further indications are the prevention of bacterial endocarditis and hematogenic endoprosthesis infection in high-risk individuals prior to septic surgery., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

In addition to prevention of surgical site infections after skin surgery, perioperative antibiotic prophylaxis (PAP) aims to prevent the occurrence of other postoperative infectious complications, especially bacterial endocarditis and hematogenous joint prosthesis infections. This article discusses specific indications for the use of PAP. For example, patients who have undergone any type of heart valve replacement, including transcatheter valve replacement or use of prosthetic material to correct the heart valve, or patients who have experienced bacterial endocarditis, require PAP during skin surgery on mucosal membranes or ulcerated tumors. The use of PAP in special situations such as secondary wound healing, septic dermatosurgery or ulcer surgery is also presented and discussed in detail in this paper based on the current scientific literature. This paper represents the second part of the position paper of the Antibiotic Stewardship Working Group of the German Society for Dermatologic Surgery (DGDC) and summarizes evidence-based recommendations for the administration of PAP during skin surgery for special indications and situations. This is particularly important because, as detailed in Part 1 of this position paper, PAP can and usually should be avoided in skin surgery., (© 2023 The Authors. Journal der Deutschen Dermatologischen Gesellschaft published by John Wiley & Sons Ltd on behalf of Deutsche Dermatologische Gesellschaft.)

The aim of perioperative antibiotic prophylaxis (PAP) is to prevent the occurrence of surgical site infections (SSIs) or other infectious complications (especially bacterial endocarditis or septic arthritis). PAP is effective in surgeries where overall infection rates are high even without considering patient-related risk factors (such as orthopedic surgery or fracture repair). Surgery on airways, gastrointestinal, genital, or urinary tract is also considered to be associated with a risk of infection and may require PAP. Overall, SSIs in skin surgery are relatively rare and vary between 1% and 11% depending on the localization, complexity of the wound closure and patient cohort. Therefore, the general surgical recommendations regarding PAP only partially reflect the needs of dermatologic surgery. In contrast to the USA, where recommendations on the use of PAP in skin surgery already exist, there are currently no guidelines for the use of PAP specifically designed for dermatologic surgery in Germany. In the absence of an evidence-based recommendation, the use of PAP is guided by the experience of the surgeons and leads to a heterogeneous use of antimicrobial substances. In this work, we summarize the current scientific literature on the use of PAP and make a recommendation depending on procedure- and patient-related risk factors., (© 2023 The Authors. Journal der Deutschen Dermatologischen Gesellschaft published by John Wiley & Sons Ltd on behalf of Deutsche Dermatologische Gesellschaft.)

The use of perioperative antibiotic prophylaxis in cutaneous surgery is controversial due to unclear efficacy and, thus, potentially unnecessary side-effects. This prospective observational study analysed the efficacy of oral perioperative antibiotic prophylaxis in preventing surgical site infections. Adult patients undergoing cutaneous surgery between August 2020 and May 2021 at Ludwig-Maximilian University Hospital Munich, Germany, without prior signs of infection were eligible. Propensity score weighting was used for covariate adjustment to account for non-randomized treatment assignment. Of 758 included patients, 23 received perioperative antibiotic prophylaxis (3.0%). In this group, a surgical site infection occurred in 1 of 45 lesions (2.2%) compared with 76 of 1,189 lesions (6.5%) in the group without perioperative antibiotic prophylaxis (735 patients, 97.0%). With covariate adjustment, the odds ratio for the occurrence of a surgical site infection in patients receiving perioperative antibiotic prophylaxis was 0.114 (95% confidence interval 0.073-0.182; p <0.001) on a per lesion level. The number of lesions needed to treat to prevent 1 surgical site infection was 17.6 (95% confidence interval 16.8-19.2). This prospective observational study shows a reduction in the incidence of surgical site infection in cutaneous surgery performed with perioperative antibiotic prophylaxis. The large size difference between the 2 study groups limits the study.

Postoperative wound infection in dermatologic surgery causes impaired wound healing, poor cosmetic outcome and increased morbidity. Patients with a high-risk profile may benefit from perioperative antibiotic prophylaxis. The objective of this systematic review was to identify risk factors for surgical site infection after dermatologic surgery. In this article, we report findings on patient-dependent risk factors. The literature search included MEDLINE, EMBASE, CENTRAL and trial registers. We performed meta-analysis, if studies reported sufficient data to calculate risk ratios with 95% confidence intervals. Study quality was assessed according to the Newcastle-Ottawa-Scale. Seventeen observational studies that analysed 31213 surgical wounds were eligible for inclusion. Fourteen studies qualified for meta-analysis. Nine studies showed good, three fair and five poor methodological quality. The reported incidence of surgical site infection ranged from 0.96% to 8.70%. Meta-analysis yielded that male gender and immunosuppression were significantly associated with higher infection rates. There was a tendency towards a higher infection risk for patients with diabetes, without statistical significance. Meta-analysis did not show different infection rates after excision of squamous cell carcinoma or basal cell carcinoma, but studies were substantially heterogenous. There was no significant association between risk for wound infection and smoking, age over 60 years, oral anti-aggregation or anti-coagulation or excision of malignant melanoma. In conclusion, the risk for surgical site infection in dermatologic surgery is low. Infection rates were increased significantly in male as well as immunosuppressed patients and non-significantly in diabetics., (© 2022 The Authors. International Wound Journal published by Medicalhelplines.com Inc (3M) and John Wiley & Sons Ltd.)

The COVID-19 pandemic caused collateral damage to patients with acute and chronic conditions. In this mono-centre cross-sectional study, we sought to evaluate the impact of the COVID-19 pandemic on patients with hidradenitis suppurativa (HS). In June 2020, we sent an anonymous survey to 109 patients, who were diagnosed with HS in our outpatient clinic from May 2018 to April 2020. Fifty patients (45.9%) completed and returned the survey. Forty-five participants (90.0%) denied any cancellation of hospitalisation due to the COVID-19 pandemic. Hospitalisation was postponed in 8% of cases and cancelled in 2%. Compared to prior to the pandemic, fewer patients consulted their primary physician for changing wound dressings and more changed the dressings themselves or were assisted by their family members. 13% of patients avoided doctor visits due to fear of COVID-19 and 26.1% minimised doctor visits. The Dermatology Life Quality Index showed a moderate to very severe impact on patients' Quality of Life (mean score = 10.06). Only one patient used telemedicine. Due to limited access to primary care and fear of COVID-19, the pandemic had a detectable impact on the hospital management of patients with HS in our facility. Telemedicine still plays a negligible role in primary wound care., (© 2022 The Authors. International Wound Journal published by Medicalhelplines.com Inc (3M) and John Wiley & Sons Ltd.)

Background: Identifying risk factors is essential for preventing surgical site infections (SSIs) in dermatologic surgery., Objective: To analyze whether specific procedure-related factors are associated with SSI., Methods: This systematic review of the literature included MEDLINE, EMBASE, CENTRAL, and trial registers. The Newcastle-Ottawa Scale was used for risk bias assessment. If suitable, the authors calculated risk factors and performed meta-analysis using random effects models. Otherwise, data were summarized narratively., Results: Fifteen observational studies assessing 25,928 surgical procedures were included. Seven showed good, 2 fair, and 6 poor study quality. Local flaps (risk ratio [RR] 3.26, 95% confidence intervall [CI] 1.92-5.53) and skin grafting (RR 2.95, 95% CI 1.37-6.34) were associated with higher SSI rates. Simple wound closure had a significantly lower infection risk (RR 0.34, 95% CI 0.25-0.46). Second intention healing showed no association with SSI (RR 1.82, 95% CI 0.40-8.35). Delayed wound closure may not affect the SSI rate. The risk for infection may increase with the degree of preoperative contamination. There is limited evidence whether excisions >20 mm or surgical drains are linked to SSI., Conclusion: Local flaps, skin grafting, and severely contaminated surgical sites have a higher risk for SSI. Second intention healing and probably delayed wound closure are not associated with postoperative wound infection., (Copyright © 2022 by the American Society for Dermatologic Surgery, Inc. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Background: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance., Objective: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties., Methods: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components., Results: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par., Conclusions: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: JSU is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, Immunocore, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche, outside the submitted work. FM has received travel support or/and speaker's fees or/and advisor's honoraria by Novartis, Roche, BMS, MSD and Pierre Fabre and research funding from Novartis and Roche. SH reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Novartis, Roche, BMS, Amgen and MSD outside the submitted work. Axel H. reports clinical trial support, speaker's honoraria, or consultancy fees from the following companies: Amgen, BMS, Merck Serono, MSD, Novartis, Oncosec, Philogen, Pierre Fabre, Provectus, Regeneron, Roche, OncoSec, Sanofi-Genzyme, and Sun Pharma, outside, the submitted work. LF is on the advisory board or has received consulting/speaker honoraria from for Galderma, Janssen, Leo Pharma, Eli Lilly, Almirall, Union Therapeutics, Regeneron, Novartis, Amgen, Abbvie, UCB, Biotest, and InflaRx. MS reports advisory roles for Bristol-Myers Squibb, Novartis, MSD, Roche, Pierre Fabre, Kyowa Kirin, Immunocore and Sanofi-Genzyme. WS reports grants, speaker's honoraria or consultancy fees from medi GmbH Bayreuth, Abbvie, Almirall, Amgen, Bristol-Myers Squibb, Celgene, GSK, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi Genzyme and UCB outside the submitted work. BS reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Incyte, Novartis, Roche, BMS and MSD, research funding from BMS, Pierre Fabre Pharmaceuticals and MSD, and travel support from Novartis, Roche, BMS, Pierre Fabre Pharmaceuticals and Amgen; outside the submitted work. MG has received speaker's honoraria and/or has served as a consultant and/or member of advisory boards for Almirall, Argenx, Biotest, Eli Lilly, Janssen Cilag, Leo Pharma, Novartis and UCB, outside the submitted work. TJB is the owner of Smart Health Heidelberg GmbH (Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, Germany, https://smarthealth.de) which develops telemedicine mobile apps (such as AppDoc; https://online-hautarzt.net and Intimarzt; https://intimarzt.de), outside of the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: 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?, Methods: 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., Results: 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., Conclusion: 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., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Identifying risk factors for wound infection may guide clinical practice for optimal use of perioperative antibiotic prophylaxis in dermatologic surgery., Objective: To summarize the current evidence whether specific body sites have higher risks for surgical site infections (SSI)., Methods: The systematic literature search included MEDLINE, Embase, CENTRAL, and trial registers. Only observational studies qualified for inclusion and meta-analysis. We assessed the risk of bias according to the Newcastle-Ottawa Scale., Results: Eighteen studies with 33,086 surgical wounds were eligible. Eight studies were of good, 4 of fair, and 6 of poor quality. The mean infection rate was 4.08%. Meta-analysis showed that the lips had significantly higher infection rates. The lower extremity and ears had or tended toward a higher risk for infection, but studies were clinically heterogeneous. A large prospective trial found that surgical wounds on the hands were at higher risk for infection. The trunk showed the lowest infection rate. The risk for SSI in other body locations was not different or remained uncertain because of substantial heterogeneity among studies., Conclusion: Lips, lower extremities, and probably ears and hands may have a higher risk for wound infection after skin surgery. The trunk showed the lowest infection rate., (Copyright © 2022 by the American Society for Dermatologic Surgery, Inc. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Background: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice., Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians., Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included., Results: A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images., Conclusions: All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.S.U. is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, LEO Pharma, Merck Sharp and Dohme, Novartis, Pierre Fabre and Roche, outside the submitted work. M.G. has received speaker's honoraria and/or has served as a consultant and/or member of advisory boards for Almirall, Argenx, Biotest, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis and UCB, outside the submitted work. H.A.H. worked as a consultant or received honoraria and travel support from Heine Optotechnik GmbH, JenLab GmbH, FotoFinder Systems GmbH, Magnosco GmbH, SciBase AB, Beiersdorf AG, Almirall Hermal GmbH and Galderma Laboratorium GmbH. V.M.R. is on the advisory board or has received honoraria or ownership in Inhabit Brands, Inc. unrelated to this work. Sondermann W. reports grants from medi GmbH Bayreuth, personal fees from Janssen, grants and personal fees from Novartis, personal fees from Lilly, personal fees from UCB, personal fees from Almirall, personal fees from LEO Pharma and personal fees from Sanofi Genzyme, outside the submitted work. H.P.S. is a shareholder of MoleMap NZ Limited and e-derm consult GmbH and undertakes regular tele-dermatological reporting for both companies. H.P.S. is a medical consultant for Canfield Scientific, Inc., MoleMap Australia Pty Ltd and Revenio Research Oy and a medical advisor for First Derm. M.L-V. has received speaker's honoraria and/or received grants and/or participated in clinical trials of AbbVie, Almirall, Amgen, Celgene, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis and UCB, outside the submitted work. A.Z. has been an advisor and/or received speaker's honoraria and/or received grants and/or participated in clinical trials of AbbVie, Almirall, Amgen, Beiersdorf Dermo Medical, Bencard Allergy, Celgene, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis, Sanofi-Aventis and UCB Pharma, outside the submitted work. Kittler H. received speaker's honoraria from FotoFinder Systems GmbH and received non-financial support from Heine Optotechnik GmbH, Derma Medical and 3Gen. T.J.B. reports owning a company that develops mobile apps, including the teledermatology services AppDoc (https://online-hautarzt.de) and Intimarzt (https://Intimarzt.de); Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, https://smarthealth.de. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data., Objective: The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured., Methods: Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it., Results: The benchmark contains three data sets-Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations., Conclusions: This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.S.U. is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, Leo Pharma, Merck Sharp and Dohme, Novartis, Pierre Fabre and Roche, outside the submitted work. F.M. has received travel support or/and speaker's fees or/and advisor's honoraria from Novartis, Roche, BMS, MSD and Pierre Fabre and research funding from Novartis and Roche. S.H. reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Novartis, Roche, BMS, Amgen and MSD outside the submitted work. A.H. reports clinical trial support, speaker's honoraria or consultancy fees from the following companies: Amgen, BMS, Merck Serono, MSD, Novartis, OncoSec, Philogen, Pierre Fabre, Provectus, Regeneron, Roche, OncoSec, Sanofi Genzyme and Sun Pharma, outside the submitted work. W.S. reports grants from medi GmbH Bayreuth, personal fees from Janssen, grants and personal fees from Novartis, personal fees from Lilly, personal fees from UCB, personal fees from Almirall, personal fees from Leo Pharma and personal fees from Sanofi Genzyme, outside the submitted work. B.S. reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Incyte, Novartis, Roche, BMS and MSD, research funding from BMS, Pierre Fabre Pharmaceuticals and MSD and travel support from Novartis, Roche, BMS, Pierre Fabre Pharmaceuticals and Amgen, outside the submitted work. M.G. has received speaker's honoraria and/or has served as a consultant and/or member of advisory boards for Almirall, argenx, Biotest, Eli Lilly, Janssen Cilag, Leo Pharma, Novartis and UCB, outside the submitted work. T.J.B. reports owning a company that develops mobile apps including the teledermatology services AppDoc (https://online-hautarzt.net) and Intimarzt (https://intimarzt.de): Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, https://smarthealth.de. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Recent studies showed that the COVID-19 pandemic caused collateral damage in health care in terms of reduced hospital submissions or postponed treatment of other acute or chronic ill patients. An anonymous survey was sent out by mail to patients with chronic wounds in order to evaluate the impact of the pandemic on wound care. Sixty-three patients returned the survey. In 14%, diagnostic workup or hospitalisation was cancelled or postponed. Thirty-six percent could not seek consultation by their primary care physician as usual. The use of public transport or long travel time was not related to limited access to medical service (P = .583). In ambulatory care, there was neither a significant difference in the frequency of changing wound dressings (P = .67), nor in the person, who performed wound care (P = .39). There were no significant changes in wound-specific quality of life (P = .505). No patient used telemedicine in order to avoid face-to-face contact or anticipate to pandemic-related restrictions. The COVID-19 pandemic impaired access to clinical management of chronic wounds in Germany. It had no significant impact on ambulatory care or wound-related quality of life. Telemedicine still plays a negligible role in wound care., (© 2021 The Authors. International Wound Journal published by Medicalhelplines.com Inc (3M) and John Wiley & Sons Ltd.)

Background: Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers., Objective: This systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance., Methods: Google Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined., Results: A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier., Conclusions: This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients' benefits., (©Julia Höhn, Achim Hekler, Eva Krieghoff-Henning, Jakob Nikolas Kather, Jochen Sven Utikal, Friedegund Meier, Frank Friedrich Gellrich, Axel Hauschild, Lars French, Justin Gabriel Schlager, Kamran Ghoreschi, Tabea Wilhelm, Heinz Kutzner, Markus Heppt, Sebastian Haferkamp, Wiebke Sondermann, Dirk Schadendorf, Bastian Schilling, Roman C Maron, Max Schmitt, Tanja Jutzi, Stefan Fröhling, Daniel B Lipka, Titus Josef Brinker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.07.2021.)

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses., Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone., Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier., Results: The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%., Conclusion: In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Haferkamp S. reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Novartis, Roche, BMS, Amgen and MSD outside the submitted work. Hauschild A. reports clinical trial support, speaker's honoraria or consultancy fees from the following companies: Amgen, BMS, Merck Serono, MSD, Novartis, Oncosec, Philogen, Pierre Fabre, Provectus, Regeneron, Roche, OncoSec, Sanofi-Genzyme and Sun Pharma, outside the submitted work. BS reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Incyte, Novartis, Roche, BMS and MSD, research funding from BMS, Pierre Fabre Pharmaceuticals and MSD, and travel support from Novartis, Roche, BMS, Pierre Fabre Pharmaceuticals and Amgen, outside the submitted work. JSU is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche, outside the submitted work. WS received travel expenses for attending meetings and/or (speaker) honoraria from Abbvie, Almirall, Bristol-Myers Squibb, Celgene, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi Genzyme and UCB outside the submitted work. FM has received travel support or/and speaker's fees or/and advisor's honoraria by Novartis, Roche, BMS, MSD and Pierre Fabre and research funding from Novartis and Roche. TJB reports owning a company that develops mobile applications (Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, https://smarthealth.de). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Studies have shown that artificial intelligence achieves similar or better performance than dermatologists in specific dermoscopic image classification tasks. However, artificial intelligence is susceptible to the influence of confounding factors within images (eg, skin markings), which can lead to false diagnoses of cancerous skin lesions. Image segmentation can remove lesion-adjacent confounding factors but greatly change the image representation., Objective: The aim of this study was to compare the performance of 2 image classification workflows where images were either segmented or left unprocessed before the subsequent training and evaluation of a binary skin lesion classifier., Methods: Separate binary skin lesion classifiers (nevus vs melanoma) were trained and evaluated on segmented and unsegmented dermoscopic images. For a more informative result, separate classifiers were trained on 2 distinct training data sets (human against machine [HAM] and International Skin Imaging Collaboration [ISIC]). Each training run was repeated 5 times. The mean performance of the 5 runs was evaluated on a multi-source test set (n=688) consisting of a holdout and an external component., Results: Our findings showed that when trained on HAM, the segmented classifiers showed a higher overall balanced accuracy (75.6% [SD 1.1%]) than the unsegmented classifiers (66.7% [SD 3.2%]), which was significant in 4 out of 5 runs (P<.001). The overall balanced accuracy was numerically higher for the unsegmented ISIC classifiers (78.3% [SD 1.8%]) than for the segmented ISIC classifiers (77.4% [SD 1.5%]), which was significantly different in 1 out of 5 runs (P=.004)., Conclusions: Image segmentation does not result in overall performance decrease but it causes the beneficial removal of lesion-adjacent confounding factors. Thus, it is a viable option to address the negative impact that confounding factors have on deep learning models in dermatology. However, the segmentation step might introduce new pitfalls, which require further investigations., (©Roman C Maron, Achim Hekler, Eva Krieghoff-Henning, Max Schmitt, Justin G Schlager, Jochen S Utikal, Titus J Brinker. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.03.2021.)

Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39-75.66%) for dermatological and 73.80% (95% CI: 73.10-74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12-65.94%, p < 0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66-65.83%, p < 0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem., (Copyright © 2020 Hekler, Kather, Krieghoff-Henning, Utikal, Meier, Gellrich, Upmeier zu Belzen, French, Schlager, Ghoreschi, Wilhelm, Kutzner, Berking, Heppt, Haferkamp, Sondermann, Schadendorf, Schilling, Izar, Maron, Schmitt, Fröhling, Lipka and Brinker.)

Background: Photodynamic therapy (PDT) is an effective intervention for actinic keratosis and field cancerization. Ablative fractional lasers may facilitate the delivery of photosensitizers and thereby improve the effects of PDT., Objective: To summarize the current evidence on the efficacy and safety of laser-assisted PDT., Methods: We performed a systematic literature research in Medline, Embase, and the Cochrane Central Register of Controlled Trials and hand-searched pertinent trial registers for eligible randomized controlled trials. Results from individual studies were pooled by using a random-effects model. The risk of bias was estimated with the Cochrane Risk of Bias Tool, and the quality of evidence of the outcomes was assessed with the Grading of Recommendations, Assessment, Development, and Evaluation approach., Results: Of 817 records initially identified, 7 randomized controlled trials were included in the qualitative analysis and 4 were included in the meta-analysis. Laser-assisted PDT showed significantly higher clearance rates than did PDT monotherapy (risk ratio, 1.33; 95% confidence interval, 1.24-1.42; I 2  = 25%; P < .01). There was no difference in pain intensity between laser-assisted PDT and other interventions (mean difference, 0.31; 95% confidence interval, -0.12 to 0.74; I 2  = 0%; P = .16). The included studies showed a high risk of bias., Limitations: The clinical heterogeneity of included studies., Conclusion: Laser-assisted PDT is more efficient but not more painful than PDT or laser treatment only., (Copyright © 2018 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: The use of immune checkpoint blockade (ICB) for uveal melanoma (UM) is little established. The aim of this review was to provide a comprehensive overview on the efficacy, safety, and tolerability of ICB in patients with UM., Methods: We performed a systematic literature research covering MEDLINE, Embase and CENTRAL. Abstracts of pertinent conferences and trial registers were handsearched for relevant studies., Results: Out of 1327 records initially identified, 12 eligible studies were included in the qualitative synthesis. They comprised 7 expanded access or named patient programs (n=162), 4 phase II trials (n=171), and 1 phase Ib trial (sample size unknown), while no randomized controlled trial was found. Ipilimumab monotherapy was assessed at 3mg/kg in 5 trials (n=186) with a response rate of 0 to 5%. Two reports investigated ipilimumab at 10mg/kg (n=45) with radiological responses observed in 0 to 6.5%. The median progression-free survival (PFS) was below 3months and the median overall survival was 5.2-9.8months for ipilimumab monotherapy. Severe immune-related adverse events occurred at a frequency comparable to cutaneous melanoma (6 to 36%). Two studies investigated pembrolizumab (2mg/kg) and nivolumab (3mg/kg) with overall response rates of 30% and 6%, respectively. Data on combined ipilimumab and programmed cell death protein 1 inhibition were available from one expanded access program, but no response was observed with a median PFS of 2.9months., Conclusions: UM is little responsive to ipilimumab regardless of dosage schemes. Sound randomized clinical trials are needed to evaluate the efficacy of combined ICB in patients with UM., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

People with chronic plaque psoriasis often have lesions on the scalp that are difficult to treat. This report is a summary of a Cochrane review on the efficacy and safety of topical treatments for scalp psoriasis. For quality-of-evidence assessment, we used the Grading of Recommendations Assessment, Development and Evaluation Working Group approach. Only randomized controlled trials (RCTs) were eligible for inclusion. We searched the Cochrane Skin Group Specialised Register, CENTRAL, MEDLINE, Embase and LILACS; ongoing trials; indexes of included studies and screened abstracts of six psoriasis-specific conferences up to August 2015. We included 59 RCTs, with 11 561 participants overall. Most findings were limited to short-term treatments (< 6 months). According to the clinician and patients' self-assessment, a corticosteroid-vitamin D combination (e.g. betamethasone dipropionate plus calcipotriol) and corticosteroids of high and very high potency were better than vitamin D. The two-compound combination was superior to the corticosteroid alone, but the additional benefit was small. Reporting of quality-of-life data was insufficient. The two-compound combination and corticosteroids caused fewer withdrawals due to adverse events than vitamin D. There was no difference between the two-compound combination and corticosteroid monotherapy concerning this outcome. Overall the evidence was of moderate quality. Evaluation of other topical treatments was limited. Given the comparable safety profile and only slim benefit of the two-compound combination over the corticosteroid alone, monotherapy with generic topical corticosteroids of high and very high potency may be fully acceptable for short-term therapy. More quality-of-life data and long-term assessments are needed., (© 2016 British Association of Dermatologists.)

Background: People with chronic plaque psoriasis often have lesions on the scalp. Hair makes the scalp difficult to treat and the adjacent facial skin is particularly sensitive to topical treatments., Objectives: To assess the efficacy and safety of topical treatments for scalp psoriasis., Search Methods: We searched the following databases up to August 2015: the Cochrane Skin Group Specialised Register, CENTRAL (2015, Issue 7), MEDLINE (from 1946), EMBASE (from 1974) and LILACS (from 1982). We also searched five trials registers, screened abstracts of six psoriasis-specific conferences and checked the reference lists of included studies for further references to relevant randomised controlled trials., Selection Criteria: Randomised controlled trials (RCTs) with a parallel-group, cross-over or within-patient design of topical treatments for people of all ages with scalp psoriasis., Data Collection and Analysis: Two authors independently carried out study selection, data extraction and 'Risk of bias' assessment. Disagreements were settled by reference to a third author.To assess the quality of evidence, we focused on the following outcomes: 'clearance' or 'response' as assessed by the investigator global assessment (IGA), improvement in quality of life, adverse events requiring withdrawal of treatment and 'response' as assessed by the patient global assessment (PGA).We expressed the results of the single studies as risk ratios (RR) with 95% confidence intervals (CI) for dichotomous outcomes, and mean differences (MD) with 95% CI for continuous outcomes. If studies were sufficiently homogeneous, we meta-analysed the data by using the random-effects model. Where it was not possible to calculate a point estimate for a single study, we described the data qualitatively. We also presented the number needed to treat to benefit (NNTB).We categorised topical corticosteroids according to the German classification of corticosteroid potency as mild, moderate, high and very high., Main Results: We included 59 RCTs with a total of 11,561 participants. Thirty studies were either conducted or sponsored by the manufacturer of the study medication. The risk of bias varied considerably among the included studies. For instance, most authors did not state the randomisation method and few addressed allocation concealment. Most findings were limited to short-term treatments, since most studies were conducted for less than six months. Only one trial investigated long-term therapy (12 months). Although we found a wide variety of different interventions, we limited the grading of the quality of evidence to three major comparisons: steroid versus vitamin D, two-compound combination of steroid and vitamin D versus steroid monotherapy and versus vitamin D.In terms of clearance, as assessed by the IGA, steroids were better than vitamin D (RR 1.82; 95% CI 1.52 to 2.18; four studies, 2180 participants, NNTB = 8; 95% CI 7 to 11; moderate quality evidence). Statistically, the two-compound combination was superior to steroid monotherapy, however the additional benefit was small (RR 1.22; 95% CI 1.08 to 1.36; four studies, 2474 participants, NNTB = 17; 95% CI 11 to 41; moderate quality evidence). The two-compound combination was more effective than vitamin D alone (RR 2.28; 95% CI 1.87 to 2.78; four studies, 2008 participants, NNTB = 6; 95% CI 5 to 7; high quality evidence).In terms of treatment response, as assessed by the IGA, corticosteroids were more effective than vitamin D (RR 2.09; 95% CI 1.80 to 2.41; three studies, 1827 participants; NNTB = 4; 95% CI 4 to 5; high quality evidence). The two-compound combination was better than steroid monotherapy, but the additional benefit was small (RR 1.15; 95% CI 1.06 to 1.25; three studies, 2444 participants, NNTB = 13; 95% CI 9 to 24; moderate quality evidence). It was also more effective than vitamin D alone (RR 2.31; 95% CI 1.75 to 3.04; four studies, 2222 participants, NNTB = 3; 95% CI 3 to 4; moderate quality evidence).Reporting of quality of life data was poor and data were insufficient to be included for meta-analysis.Steroids caused fewer withdrawals due to adverse events than vitamin D (RR 0.22; 95% CI 0.11 to 0.42; four studies, 2291 participants; moderate quality evidence). The two-compound combination and steroid monotherapy did not differ in the number of adverse events leading withdrawal (RR 0.88; 95% CI 0.42 to 1.88; three studies, 2433 participants; moderate quality evidence). The two-compound combination led to fewer withdrawals due to adverse events than vitamin D (RR 0.19; 95% CI 0.11 to 0.36; three studies, 1970 participants; high quality evidence). No study reported the type of adverse event requiring withdrawal.In terms of treatment response, as assessed by the PGA, steroids were more effective than vitamin D (RR 1.48; 95% CI 1.28 to 1.72; three studies, 1827 participants; NNTB = 5; 95% CI 5 to 7; moderate quality evidence). Statistically, the two-compound combination was better than steroid monotherapy, however the benefit was not clinically important (RR 1.13; 95% CI 1.06 to 1.20; two studies, 2226 participants; NNTB = 13; 95% CI 9 to 26; high quality evidence). The two-compound combination was more effective than vitamin D (RR 1.76; 95% CI 1.46 to 2.12; four studies, 2222 participants; NNTB = 4; 95% CI 3 to 6; moderate quality evidence).Common adverse events with these three interventions were local irritation, skin pain and folliculitis. Systemic adverse events were rare and probably not drug-related.In addition to the results of the major three comparisons we found that the two-compound combination, steroids and vitamin D monotherapy were more effective than the vehicle. Steroids of moderate, high and very high potency tended to be similarly effective and well tolerated. There are inherent limitations in this review concerning the evaluation of salicylic acid, tar, dithranol or other topical treatments., Authors' Conclusions: The two-compound combination as well as corticosteroid monotherapy were more effective and safer than vitamin D monotherapy. Given the similar safety profile and only slim benefit of the two-compound combination over the steroid alone, monotherapy with generic topical steroids may be fully acceptable for short-term therapy.Future RCTs should investigate how specific therapies improve the participants' quality of life. Long-term assessments are needed (i.e. 6 to 12 months).

A major obstacle of evidence-based clinical decision making is the use of nonstandardized, partly untested outcome measurement instruments. Core Outcome Sets (COSs) are currently developed in different medical fields to standardize and improve the selection of outcomes and outcome measurement instruments in clinical trials, in order to pool results of trials or to allow indirect comparison between interventions. A COS is an agreed minimum set of outcomes that should be measured and reported in all clinical trials of a specific disease or trial population. The international, multidisciplinary Cochrane Skin Group Core Outcome Set Initiative (CSG-COUSIN) aims to develop and implement COSs in dermatology, thus making trial evidence comparable and, herewith, more useful for clinical decision making. The inaugural meeting of CSG-COUSIN was held on 17-18 March 2015 in Dresden, Germany, as the exclusive theme of the Annual Cochrane Skin Group Meeting. In total, 29 individuals representing a broad mix of different stakeholder groups, professions, skills and perspectives attended. This report provides a description of existing COS initiatives in dermatology, highlights current methodological challenges in COS development, and presents the concept, aims and structure of CSG-COUSIN., (© 2016 British Association of Dermatologists.)