Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

BACKGROUND: Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS AND METHODS: In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. RESULTS: In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. CONCLUSIONS: AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.

Genetic and methylation profiles distinguish benign, malignant and spitzoid melanocytic tumors.

Accurate classification of melanocytic tumors is important for prognostic evaluation, treatment and follow-up protocols of patients. The majority of melanocytic proliferations can be classified solely based on clinical and pathological criteria, however in select cases a definitive diagnostic assessment remains challenging and additional diagnostic biomarkers would be advantageous. We analyzed melanomas, nevi, Spitz nevi and atypical spitzoid tumors using parallel sequencing (exons of 611 genes and 507 gene translocation analysis) and methylation arrays (850k Illumina EPIC). By combining detailed genetic and epigenetic analysis with reference-based and reference-free DNA methylome deconvolution we compared Spitz nevi to nevi and melanoma and assessed the potential for these methods in classifying challenging spitzoid tumors. Results were correlated with clinical and histologic features. Spitz nevi were found to cluster independently of nevi and melanoma and demonstrated a different mutation profile. Multiple copy number alterations and TERT promoter mutations were identified only in melanomas. Genome-wide methylation in Spitz nevi was comparable to benign nevi while the Leukocytes UnMethylation for Purity (LUMP) algorithm in Spitz nevi was comparable to melanoma. Histologically difficult to classify Spitz tumor cases were assessed which, based on methylation arrays, clustered between Spitz nevi and melanoma and in terms of genetic profile or copy number variations demonstrated worrisome features suggesting a malignant neoplasm. Comprehensive sequencing and methylation analysis verify Spitz nevi as an independent melanocytic entity distinct from both nevi and melanoma. Combined genetic and methylation assays can offer additional insights in diagnosing difficult to classify Spitzoid tumors.

Salivary cortisol levels and anxiety in melanoma patients undergoing sentinel lymph node excision under local anesthesia versus general anesthesia: a prospective study.

BACKGROUND: Sentinel lymph node excision (SLNE) can be performed in tumescent local anesthesia (TLA) or general anesthesia (GA). Perioperative cortisol level changes and anxiety are common in surgical interventions and might be influenced by the type of anesthesia. In this study, we intended to determine whether the type of anesthesia impacts the patients' perioperative levels of salivary cortisol (primary outcome) and the feeling of anxiety evaluated by psychological questionnaires (secondary outcome). METHODS: All melanoma patients of age undergoing SLNE at the University Hospital Essen, Germany, could be included in the study. Exclusion criteria were patients' intake of glucocorticoids or psychotropic medication during the former 6 months, pregnancy, age under 18 years, and BMI ≥ 30 as salivary cortisol levels were reported to be significantly impacted by obesity and might confound results. RESULTS: In total, 111 melanoma patients undergoing SLNE were included in our prospective study between May 2011 and April 2017 and could choose between TLA or GA. Salivary cortisol levels were measured three times intraoperatively, twice on the third and second preoperative day and twice on the second postoperative day. To assess anxiety, patients completed questionnaires (Hospital Anxiety and Depression Scale (HADS), State-Trait Anxiety Inventory (STAI)) perioperatively. Patients of both groups exhibited comparable baseline levels of cortisol and perioperative anxiety levels. Independent of the type of anesthesia, all patients showed significantly increasing salivary cortisol level from baseline to 30 min before surgery (T3) (TLA: t = 5.07, p < 0.001; GA: t = 3.09, p = 0.006). Post hoc independent t tests showed that the TLA group exhibited significantly higher cortisol concentrations at the beginning of surgery (T4; t = 3.29, p = 0.002) as well as 20 min after incision (T5; t = 277, p = 0.008) compared to the GA group. CONCLUSIONS: The type of anesthesia chosen for SLNE surgery significantly affects intraoperative cortisol levels in melanoma patients. Further studies are mandatory to evaluate the relevance of endogenous perioperative cortisol levels on the postoperative clinical course. TRIAL REGISTRATION: German Clinical Trials Register DRKS00003076, registered 1 May 2011.

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist's diagnoses. OBJECTIVE: The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image-based discrimination between melanoma and nevus. METHODS: Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. RESULTS: While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists' average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. CONCLUSIONS: The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image-based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.

Patient quality of life after vestibular schwannoma removal: possibilities and limits to measuring different domains of patients' wellbeing.

PURPOSE: Since the 1980s, health-related quality of life (HRQOL) has been recognized in the assessment of medical treatment. To determine the health-related quality of life (HRQOL) of vestibular schwannoma (VS) patients, a specific questionnaire that has been validated in different languages is essential. METHODS: The Short Form-36 Health Survey (SF-36) and PANQOL questionnaires in German were evaluated in patients after removal of a VS via the translabyrinthine approach. Descriptive statistics of a comparison of the SF-36 results to those of a normal sample are illustrated. Criterion validity was investigated using Spearman's rank test to correlate the PANQOL domains with the SF-36 domains. A confirmatory factor analysis of the PANQOL was performed to determine the stability of the factor structure of the PANQOL questionnaire for our cohort. RESULTS: The criterion validity of the German PANQOL questionnaire is comparable to that of the original English version. The SF-36 domains values ranged from 49.31/100 (role physical) to 66.46/100 (physical functioning). Compared to the normal population, patients who underwent surgical removal of a VS showed a significantly reduced quality of life, mainly in domains such as physical and social functioning, as well as psychological wellbeing. CONCLUSION: The German PANQOL has been validated and is now available. Post-surgical treatment should be focused not only on physiological rehabilitation but also on improving the quality of life, especially aspects of psychological and social wellbeing.

A Medical Student-Delivered Smoking Prevention Program, Education Against Tobacco, for Secondary Schools in Germany: Randomized Controlled Trial.

BACKGROUND: More than 8.5 million Germans suffer from chronic diseases attributable to smoking. Education Against Tobacco (EAT) is a multinational network of medical students who volunteer for school-based prevention in the classroom setting, amongst other activities. EAT has been implemented in 28 medical schools in Germany and is present in 13 additional countries around the globe. A recent quasi-experimental study showed significant short-term smoking cessation effects on 11-to-15-year-old adolescents. OBJECTIVE: The aim of this study was to provide the first randomized long-term evaluation of the optimized 2014 EAT curriculum involving a photoaging software for its effectiveness in reducing the smoking prevalence among 11-to-15-year-old pupils in German secondary schools. METHODS: A randomized controlled trial was undertaken with 1504 adolescents from 9 German secondary schools, aged 11-15 years in grades 6-8, of which 718 (47.74%) were identifiable for the prospective sample at the 12-month follow-up. The experimental study design included measurements at baseline (t1), 6 months (t2), and 12 months postintervention (t3), via questionnaire. The study groups consisted of 40 randomized classes that received the standardized EAT intervention (two medical student-led interactive modules taking 120 minutes total) and 34 control classes within the same schools (no intervention). The primary endpoint was the difference in smoking prevalence from t1 to t3 in the control group versus the difference from t1 to t3 in the intervention group. The differences in smoking behavior (smoking onset, quitting) between the two groups, as well as gender-specific effects, were studied as secondary outcomes. RESULTS: None of the effects were significant due to a high loss-to-follow-up effect (52.26%, 786/1504). From baseline to the two follow-up time points, the prevalence of smoking increased from 3.1% to 5.2% to 7.2% in the control group and from 3.0% to 5.4% to 5.8% in the intervention group (number needed to treat [NNT]=68). Notable differences were observed between the groups for the female gender (4.2% to 9.5% for control vs 4.0% to 5.2% for intervention; NNT=24 for females vs NNT=207 for males), low educational background (7.3% to 12% for control vs 6.1% to 8.7% for intervention; NNT=30), and migrational background (students who claimed that at least one parent was not born in Germany) at the 12-month follow-up. The intervention appears to prevent smoking onset (NNT=63) but does not appear to initiate quitting. CONCLUSIONS: The intervention appears to prevent smoking, especially in females and students with a low educational background.

Severe autoimmune hemolytic anemia following immunotherapy with checkpoint inhibitors in two patients with metastatic melanoma: a case report.

Introduction: Over the past decade, immune checkpoint inhibitors such as antibodies against cytotoxicity T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1) have become an important armamentarium against a broad spectrum of malignancies. However, these specific inhibitors can cause adverse autoimmune reactions by impairing self-tolerance. Hematologic side effects of immune checkpoint inhibitors, including autoimmune hemolytic anemia (AIHA), are rare but can be life-threatening. Case report: Herein, we report two patients on immune checkpoint inhibitors for metastatic melanoma who developed AIHA with symptoms of dyspnea and fatigue. In the first patient, symptoms alleviated after discontinuation of combined anti CTLA-4 and anti-PD-1 therapy, initiation of corticosteroids and application of a single red blood cell transfusion. Due to subsequent progress of melanoma, combinational anti-PD-1 and tyrosine kinase inhibitor therapy was initiated based on multidisciplinary tumor board decision. After two months, she again developed the described hematological and clinical signs of AIHA leading to cessation of anti-PD-1 therapy and initiation of corticosteroids, which again resulted in an alleviation of her symptoms. Due to further progression, the patient received dacarbazine for several months before she decided to stop any therapy other than palliative supportive care. In the second patient, discontinuation of anti-PD-1 therapy and initiation of corticosteroids entailed a complete alleviation of his symptoms. After refusing chemotherapy due to subsequent melanoma progression, he received radiotherapy of bone metastases and is currently enrolled in a clinical trial. The patient did not develop AIHA ever since. Conclusion: Hematologic immune-related adverse events due to treatment with immune checkpoint inhibitors are rare but can have life-threatening consequences. If dyspnea and other clinical symptoms are present, AIHA should be considered as a potential cause and treated promptly in a multidisciplinary setting. An expanded comprehension of risk factors and pathogenesis of AIHA is needed to identify high-risk patients beforehand, leading to more effective predictive and reactive treatment approaches.

Deep learning based histological classification of adnex tumors.

BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant entities occur which can metastasize and lead to patients´ death. Correct diagnosis is critical to ensure optimal treatment and best possible patient outcome. Artificial intelligence (AI) in the form of deep neural networks has recently shown enormous potential in the field of medicine including pathology, where we and others have found common cutaneous tumors can be detected with high sensitivity and specificity. To become a widely applied tool, AI approaches will also need to reliably detect and distinguish less common tumor entities including the diverse group of cutaneous adnexal tumors. METHODS: To assess the potential of AI to recognize cutaneous adnexal tumors, we selected a diverse set of these entities from five German centers. The algorithm was trained with samples from four centers and then tested on slides from the fifth center. RESULTS: The neural network was able to differentiate 14 different cutaneous adnexal tumors and distinguish them from more common cutaneous tumors (i.e. basal cell carcinoma and seborrheic keratosis). The total accuracy on the test set for classifying 248 samples into these 16 diagnoses was 89.92 %. Our findings support AI can distinguish rare tumors, for morphologically distinct entities even with very limited case numbers (< 50) for training. CONCLUSION: This study further underlines the enormous potential of AI in pathology which could become a standard tool to aid pathologists in routine diagnostics in the foreseeable future. The final diagnostic responsibility will remain with the pathologist.

Shortened progression free and overall survival to immune-checkpoint inhibitors in BRAF-, RAS- and NF1- ("Triple") wild type melanomas.

BACKGROUND: Melanomas lacking mutations in BRAF, NRAS and NF1 are frequently referred to as "triple wild-type" (tWT) melanomas. They constitute 5-10 % of all melanomas and remain poorly characterized regarding clinical characteristics and response to therapy. This study investigates the largest multicenter collection of tWT-melanomas to date. METHODS: Targeted next-generation sequencing of the TERT promoter and 29 melanoma-associated genes were performed on 3109 melanoma tissue samples of the prospective multicenter study ADOREG/TRIM of the DeCOG revealing 292 patients suffering from tWT-melanomas. Clinical characteristics and mutational patterns were analyzed. As subgroup analysis, we analyzed 141 tWT-melanoma patients receiving either anti-CTLA4 plus anti-PD1 or anti PD1 monotherapy as first line therapy in AJCC stage IV. RESULTS: 184 patients with cutaneous melanomas, 56 patients with mucosal melanomas, 34 patients with acral melanomas and 18 patients with melanomas of unknown origin (MUP) were included. A TERT promoter mutation could be identified in 33.2 % of all melanomas and 70.5 % of all tWT-melanomas harbored less than three mutations per sample. For the 141 patients with stage IV disease, mPFS independent of melanoma type was 6.2 months (95 % CI: 4-9) and mOS was 24.8 months (95 % CI: 14.2-53.4) after first line anti-CTLA4 plus anti-PD1 therapy. After first-line anti-PD1 monotherapy, mPFS was 4 months (95 %CI: 2.9-8.5) and mOS was 29.18 months (95 % CI: 17.5-46.2). CONCLUSIONS: While known prognostic factors such as TERT promoter mutations and TMB were equally distributed among patients who received either anti-CTLA4 plus anti-PD1 combination therapy or anti-PD1 monotherapy as first line therapy, we did not find a prolonged mPFS or mOS in either of those. For both therapy concepts, mPFS and mOS were considerably shorter than reported for melanomas with known oncogene mutations.

Early versus late response to PD-1-based immunotherapy in metastatic melanoma.

BACKGROUND: Immune checkpoint inhibition (ICI) currently is the most effective treatment to induce durable responses in metastatic melanoma. The aims of this study are the characterization of patients with early, late and non-response to ICI and analysis of survival outcomes in a real-world patient cohort. METHODS: Patients who received PD-1-based immunotherapy for non-resectable stage-IV melanoma in any therapy line were selected from the prospective multicenter real-world DeCOG study ADOREG-TRIM (NCT05750511). Patients showing complete (CR) or partial (PR) response already during the first 3 months of treatment (Early Responders, EarlyR) were compared to patients showing CR/PR at a later time (Late Responders, LateR), a stable disease (SD) and to patients showing progressive disease (Non-Responders, NonR). RESULTS: Of 522 patients, 8.2 % were EarlyR (n = 43), 19.0 % were LateR (n = 99), 37.0 % had a SD (n = 193) and 35.8 % were NonR (n = 187). EarlyR, LateR and SD patients had comparable baseline characteristics. Multivariate logbinomial regression analyses adjusted for age and sex revealed positive tumor PD-L1 (RR=1.99, 95 %-CI=1.14-3.46, p = 0.015), and normal serum CRP (RR=1.59, 95 %-CI=0.93-2.70, p = 0.036) as independently associated with the achievement of an early response compared to NonR. The median progression-free and overall survival was 46.0 months (95 % CI 19.1; NR) and 47.8 months (95 %-CI 36.9; NR) for EarlyR, NR (95 %-CI NR; NR) for LateR, 8.1 months (7.0; 10.4) and 35.4 months (29.2; NR) for SD, and 2.0 months (95 %-CI 1.9; 2.1) and 6.1 months (95 %-CI 4.6; 8.8) for NonR patients. CONCLUSION: Less than 10 % of metastatic melanoma patients achieved an early response during the first 3 months of PD-1-based immunotherapy. Early responders were not superior to late responders in terms of response durability and survival.

Deep learning detection of melanoma metastases in lymph nodes.

BACKGROUND: In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissue levels with hematoxylin and eosin and immunohistochemically stained glass slides. Considering the amount of tissue to analyze, the detection of metastasis can be highly time-consuming for pathologists. The application of artificial intelligence in the clinical routine has constantly increased over the past few years. METHODS: In this multi-center study, a deep learning method was established on histological tissue sections of sentinel lymph nodes collected from the clinical routine. The algorithm was trained to highlight potential melanoma metastases for further review by pathologists, without relying on supplementary immunohistochemical stainings (e.g. anti-S100, anti-MelanA). RESULTS: The established method was able to detect the existence of metastasis on individual tissue cuts with an area under the curve of 0.9630 and 0.9856 respectively on two test cohorts from different laboratories. The method was able to accurately identify tumour deposits>0.1 mm and, by automatic tumour diameter measurement, classify these into 0.1 mm to -1.0 mm and>1.0 mm groups, thus identifying and classifying metastasis currently relevant for assessing prognosis and stratifying treatment. CONCLUSIONS: Our results demonstrate that AI-based SLN melanoma metastasis detection has great potential and could become a routinely applied aid for pathologists. Our current study focused on assessing established parameters; however, larger future AI-based studies could identify novel biomarkers potentially further improving SLN-based prognostic and therapeutic predictions for affected patients.

Characterisation and outcome of RAC1 mutated melanoma.

BACKGROUND: Activating hot spot R29S mutations in RAC1, a small GTPase influencing several cellular processes including cell proliferation and cytoskeleton rearrangement, have been reported in up to 9% of sun-exposed melanomas. Clinical characteristics and treatment implications of RAC1 mutations in melanoma remain unclear. METHODS: We investigated the largest set (n = 64) of RAC1 mutated melanoma patients reported to date, including a retrospective single institution cohort (n = 34) from the University Hospital Essen and a prospective multicentre cohort (n = 30) from the translational study Tissue Registry in Melanoma (TRIM; CA209-578), for patient and tumour characteristics as well as therapy outcomes. RESULTS: From 3037 sequenced melanoma samples screened RAC1 mutations occurred in ∼2% of samples (64/3037). The most common RAC1 mutation was P29S (95%, 61/64). The majority of tumours had co-occuring MAP kinase mutations (88%, 56/64); mostly activating NRAS (47%, 30/64) mutations, followed by activating BRAF (28%, 18/64) and NF1 (25%, 16/64) mutations. RAC1 mutated melanomas were almost exclusively of cutaneous origin (84%, 54/64) or of unknown primary (MUP, 14%, 9/64). C > T alterations were the most frequent mutation type identified demonstrating a UV-signature for RAC1 mutated melanoma. Most patients with unresectable disease (39) received immune checkpoint inhibitors (ICI) (77%, 30/39). Objective response rate of first-line treatment in patients with stage III/IV disease was 21%; median overall survival was 47.8 months. CONCLUSIONS: RAC1 mutated melanomas are rare, mostly of cutaneous origin and frequently harbour concomitant MAP kinase mutations, particularly in NRAS. Patients with advanced disease benefit from systemic treatment with ICI.

Clinical and pathological characteristics of familial melanoma with germline TERT promoter variants.

Around 10% of melanoma occurs in patients with a suspected familial predisposition. TERT promoter mutations are the most common somatic hotspot mutations in human cancers. However, only two families with germline mutations have been identified to date. We present detailed histological, clinical, and molecular pathologic analyses of affected patients and details of newly identified individuals in one of these previously reported families. TERT (NM_198253.3) Chr.5:1,295,161T>C (c.-57 T>C) promoter variants were detected in all melanoma-affected (n = 18) and one non-diseased family member. The median age at diagnosis was 30 years (n = 18, range 16-46 years, 2 unknown). While most primary melanomas arose on the upper extremities (n = 7, 21%) and were superficial spreading melanoma (SSM, n = 8, 24%), many primary melanomas also originated from non-UV-exposed mucosal (n = 2, 6%) and acral (n = 4, 12%) locations. One SSM sample harbored a Chr.5:1,295,228C>T TERT promoter mutation in addition to the germline Chr.5:1,295,161T>C variant, arguing additional pathway activation can support tumor pathogenesis. Patients treated with BRAF inhibitor and/or immune checkpoint inhibition (ICI) showed responses, although of limited duration. One mucosal melanoma harbored both a KIT copy number gain and an activating c.1727 p.Leu576Pro mutation. Following the modest response to ICI, subsequent KIT inhibitor (imatinib) therapy demonstrated an ongoing complete pathological response (currently 7 months).

COVID-19 vaccination in advanced skin cancer patients receiving systemic anticancer treatment: A prospective singlecenter study investigating seroconversion rates.

Background: COVID-19 vaccination reduces risk of SARS-CoV-2 infection, COVID-19 severity and death. However, the rate of seroconversion after COVID-19 vaccination in cancer patients requiring systemic anticancer treatment is poorly investigated. The aim of the present study was to determine the rate of seroconversion after COVID-19 vaccination in advanced skin cancer patients under active systemic anticancer treatment. Methods: This prospective single-center study of a consecutive sample of advanced skin cancer patients was performed from May 2020 until October 2021. Inclusion criteria were systemic treatment for advanced skin cancer, known COVID-19 vaccination status, repetitive anti-SARS-CoV-2-S IgG serum quantification and first and second COVID-19 vaccination. Primary outcome was the rate of anti-SARS-CoV-2-S IgG seroconversion after complete COVID-19 vaccination. Results: Of 60 patients with advanced skin cancers, 52 patients (86.7%) received immune checkpoint inhibition (ICI), seven (11.7%) targeted agents (TT), one (1.7%) chemotherapy. Median follow-up time was 12.7 months. During study progress ten patients had died from skin cancer prior to vaccination completion, six patients were lost to follow-up and three patients had refused vaccination. 41 patients completed COVID-19 vaccination with two doses and known serological status. Of those, serum testing revealed n=3 patients (7.3%) as anti-SARS-CoV-2-S IgG positive prior to vaccination, n=32 patients (78.0%) showed a seroconversion, n=6 patients (14.6%) did not achieve a seroconversion. Patients failing serological response were immunocompromised due to concomitant hematological malignancy, previous chemotherapy or autoimmune disease requiring immunosuppressive comedications. Immunosuppressive comedication due to severe adverse events of ICI therapy did not impair seroconversion following COVID-19 vaccination. Of 41 completely vaccinated patients, 35 (85.4%) were under treatment with ICI, five (12.2%) with TT, and one (2.4%) with chemotherapy. 27 patients (65.9%) were treated non adjuvantly. Of these patients, 13 patients had achieved objective response (complete/partial response) as best tumor response (48.2%). Conclusion and relevance: Rate of anti-SARS-CoV-2-S IgG seroconversion in advanced skin cancer patients under systemic anticancer treatment after complete COVID-19 vaccination is comparable to other cancer entities. An impaired serological response was observed in patients who were immunocompromised due to concomitant diseases or previous chemotherapies. Immunosuppressive comedication due to severe adverse events of ICI did not impair the serological response to COVID-19 vaccination.

Evaluation of a Deep Learning Approach to Differentiate Bowen's Disease and Seborrheic Keratosis.

Background: Some of the most common cutaneous neoplasms are Bowen's disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists' workload, and in some cases, histological differentiation may be challenging. The potential of deep learning networks to distinguish these diseases is assessed. Methods: In total, 1935 whole-slide images from three institutions were scanned on two different slide scanners. A U-Net-based segmentation deep learning algorithm was trained on data from one of the centers to differentiate Bowen's disease, seborrheic keratosis, and normal tissue, learning from annotations performed by dermatopathologists. Optimal thresholds for the class distinction of diagnoses were extracted and assessed on a test set with data from all three institutions. Results: We aimed to diagnose Bowen's diseases with the highest sensitivity. A good performance was observed across all three centers, underlining the model's robustness. In one of the centers, the distinction between Bowen's disease and all other diagnoses was achieved with an AUC of 0.9858 and a sensitivity of 0.9511. Seborrheic keratosis was detected with an AUC of 0.9764 and a sensitivity of 0.9394. Nevertheless, distinguishing irritated seborrheic keratosis from Bowen's disease remained challenging. Conclusions: Bowen's disease and seborrheic keratosis could be correctly identified by the evaluated deep learning model on test sets from three different centers, two of which were not involved in training, and AUC scores > 0.97 were obtained. The method proved robust to changes in the staining solution and scanner model. We believe this demonstrates that deep learning algorithms can aid in clinical routine; however, the results should be confirmed by qualified histopathologists.

Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images.

BACKGROUND: Onychomycosis numbers among the most common fungal infections in humans affecting finger- or toenails. Histology remains a frequently applied screening technique to diagnose onychomycosis. Screening slides for fungal elements can be time-consuming for pathologists, and sensitivity in cases with low amounts of fungi remains a concern. Convolutional neural networks (CNNs) have revolutionized image classification in recent years. The goal of our project was to evaluate if a U-NET-based segmentation approach as a subcategory of CNNs can be applied to detect fungal elements on digitized histologic sections of human nail specimens and to compare it with the performance of 11 board-certified dermatopathologists. METHODS: In total, 664 corresponding H&E- and PAS-stained histologic whole-slide images (WSIs) of human nail plates from four different laboratories were digitized. Histologic structures were manually annotated. A U-NET image segmentation model was trained for binary segmentation on the dataset generated by annotated slides. RESULTS: The U-NET algorithm detected 90.5% of WSIs with fungi, demonstrating a comparable sensitivity with that of the 11 board-certified dermatopathologists (sensitivity of 89.2%). CONCLUSIONS: Our results demonstrate that machine-learning-based algorithms applied to real-world clinical cases can produce comparable sensitivities to human pathologists. Our established U-NET may be used as a supportive diagnostic tool to preselect possible slides with fungal elements. Slides where fungal elements are indicated by our U-NET should be reevaluated by the pathologist to confirm or refute the diagnosis of onychomycosis.

Genetic and Clinical Characteristics of ARID1A Mutated Melanoma Reveal High Tumor Mutational Load without Implications on Patient Survival.

(1) Background: Melanoma has the highest mortality of all cutaneous tumors, despite recent treatment advances. Many relevant genetic events have been identified in the last decade, including recurrent ARID1A mutations, which in various tumors have been associated with improved outcomes to immunotherapy. (2) Methods: Retrospective analysis of 116 melanoma samples harboring ARID1A mutations. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome applying Kaplan-Meier (log-rank test), Fisher's exact and Chi-squared tests. (3) Results: The majority of ARID1A mutations were in cutaneous and occult melanoma. ARID1A mutated samples had a higher number of mutations than ARID1A wild-type samples and harbored UV-mutations. A male predominance was observed. Many samples also harbored NF1 mutations. No apparent differences were noted between samples harboring genetically inactivating (frame-shift or nonsense) mutations and samples with other mutations. No differences in survival or response to immunotherapy of patients with ARID1A mutant melanoma were observed. (4) Conclusions: ARID1A mutations primarily occur in cutaneous melanomas with a higher mutation burden. In contrast to findings in other tumors, our data does not support ARID1A mutations being a biomarker of favorable response to immunotherapies in melanoma. Larger prospective studies would still be warranted.

TERT promoter mutations are associated with longer progression-free and overall survival in patients with BRAF-mutant melanoma receiving BRAF and MEK inhibitor therapy.

BACKGROUND: Around 50% of cutaneous melanomas harbour therapeutically targetable BRAF V600 mutations. Reliable clinical biomarkers predicting duration of response to BRAF-targeted therapies are still lacking. Recent in vitro studies demonstrated that BRAF-MEK inhibitor therapy response is associated with tumour TERT promoter mutation status. We assessed this potential association in a clinical setting. METHODS: The study cohort comprised 232 patients with metastatic or unresectable BRAF V600-mutated melanoma receiving combined BRAF/MEK inhibitor treatment, including a single-centre retrospective discovery cohort (N = 120) and a prospectively collected multicenter validation cohort (N = 112). Patients were excluded if they received BRAF or MEK inhibitors in an adjuvant setting, as monotherapy, or in combination with immunotherapy. Kaplan-Meier and univariate/multivariate Cox regression analyses were performed as appropriate. RESULTS: median age at first diagnosis was 54 years (range 16-84 years). The majority of patients were men 147/232 (63.4%). Most tumours harboured TERT promoter mutations (72%, N = 167). A survival advantage was observed in both progression-free survival (PFS) and overall survival (OS) for patients with TERT promoter-mutant versus wild-type tumours in both the discovery cohort (mPFS of 9.6 months [N = 87] vs 5.0 months [N = 33]; hazard ratio [HR] = 0.56 [95% confidence interval {CI} 0.33-0.96] and mOS of 33.6 months vs 15.0 months; HR = 0.47 [95%CI 0.32-0.70]) as well as the validation cohort (mPFS of 7.3 months [N = 80] vs 5.8 months [N = 32]; HR = 0.67 [95%CI 0.41-1.10] and mOS of 51.1 months vs 15.0 months; HR = 0.33 [95%CI 0.18-0.63]). In the pooled cohort of TERT promoter-mutant (N = 167) versus wild-type (N = 65) tumours, respectively, PFS was 8.9 versus 5.5 months, (HR = 0.62; 95%CI 0.45-0.87; P = 0.004), and OS was 33.6 versus 17.0 months, (HR = 0.51; 95%CI 0.35-0.75, P = 0.0001). CONCLUSIONS: In patients with melanoma receiving BRAF/MEK-targeted therapies, TERT promoter mutations are associated with longer survival. If validated in larger studies, TERT promoter mutation status should be included as a predictive biomarker in treatment algorithms for advanced melanoma.

Genetic and Methylation Analysis of CTNNB1 in Benign and Malignant Melanocytic Lesions.

Melanocytic neoplasms have been genetically characterized in detail during the last decade. Recurrent CTNNB1 exon 3 mutations have been recognized in the distinct group of melanocytic tumors showing deep penetrating nevus-like morphology. In addition, they have been identified in 1-2% of advanced melanoma. Performing a detailed genetic analysis of difficult-to-classify nevi and melanomas with CTNNB1 mutations, we found that benign tumors (nevi) show characteristic morphological, genetic and epigenetic traits, which distinguish them from other nevi and melanoma. Malignant CTNNB1-mutant tumors (melanomas) demonstrated a different genetic profile, instead grouping clearly with other non-CTNNB1 melanomas in methylation assays. To further evaluate the role of CTNNB1 mutations in melanoma, we assessed a large cohort of clinically sequenced melanomas, identifying 38 tumors with CTNNB1 exon 3 mutations, including recurrent S45 (n = 13, 34%), G34 (n = 5, 13%), and S27 (n = 5, 13%) mutations. Locations and histological subtype of CTNNB1-mutated melanoma varied; none were reported as showing deep penetrating nevus-like morphology. The most frequent concurrent activating mutations were BRAF V600 (n = 21, 55%) and NRAS Q61 (n = 13, 34%). In our cohort, four of seven (58%) and one of nine (11%) patients treated with targeted therapy (BRAF and MEK Inhibitors) or immune-checkpoint therapy, respectively, showed disease control (partial response or stable disease). In summary, CTNNB1 mutations are associated with a unique melanocytic tumor type in benign tumors (nevi), which can be applied in a diagnostic setting. In advanced disease, no clear characteristics distinguishing CTNNB1-mutant from other melanomas were observed; however, studies of larger, optimally prospective, cohorts are warranted.