Treatment Motivations and Expectations in Patients with Actinic Keratosis: A German-Wide Multicenter, Cross-Sectional Trial

Patient-centered motives and expectations of the treatment of actinic keratoses (AK) have received little attention until now. Hence, we aimed to profile and cluster treatment motivations and expectations among patients with AK in a nationwide multicenter, cross-sectional study including patients from 14 German skin cancer centers. Patients were asked to complete a self-administered questionnaire. Treatment motives and expectations towards AK management were measured on a visual analogue scale from 1–10. Specific patient profiles were investigated with subgroup and correlation analysis. Overall, 403 patients were included. The highest motivation values were obtained for the items “avoid transition to invasive squamous cell carcinoma” (mean ± standard deviation; 8.98 ± 1.46), “AK are considered precancerous lesions” (8.72 ± 1.34) and “treating physician recommends treatment” (8.10 ± 2.37; p < 0.0001). The highest expectation values were observed for the items “effective lesion clearance” (8.36 ± 1.99), “safety” (8.20 ± 2.03) and “treatment-related costs are covered by health insurance” (8.00 ± 2.41; p < 0.0001). Patients aged ≥77 years and those with ≥7 lesions were identified at high risk of not undergoing any treatment due to intrinsic and extrinsic motivation deficits. Heat mapping of correlation analysis revealed four clusters with distinct motivation and expectation profiles. This study provides a patient-based heuristic tool for a personalized treatment decision in patients with AK

Melanoma microenvironment-impact of modern therapies

Background A considerable proportion of patients with advanced melanoma succumb to metastatic disease despite the initial success of modern therapies. Objectives An overview of the melanoma tumor microenvironment with special focus on approved therapies and new innovative strategies is given. Methods Current clinical trials and scientific insights concerning the impact of the tumor microenvironment on progression and therapy of advanced melanoma are reviewed and discussed. Results The tumor microenvironment with its manifold components and interactions plays a major role in the treatment of malignant melanoma. Conclusion Innovative new strategies that target an immunosuppressive microenvironment may improve the therapeutic efficacy of current treatment of advanced melanoma

Talimogene laherparepvec treatment to overcome loco-regional acquired resistance to immune checkpoint blockade in tumor stage IIIB–IV M1c melanoma patients

Background!#!Resistance to immune checkpoint blockade and targeted therapy in melanoma patients is currently one of the major clinical challenges. With the approval of talimogene laherparepvec (T-VEC), oncolytic viruses are now in clinical practice for locally advanced or non-resectable melanoma. Here, we describe the usage of T-VEC in stage IVM1b-M1c melanoma patients, who achieved complete remission or stable disease upon systemic treatment but suffered from a loco-regional recurrence. To our knowledge, there are no case reports so far describing T-VEC as a means to overcome acquired resistance to immune checkpoint blockade or targeted therapy.!##!Methods!#!All melanoma patients in our department treated with T-VEC in the period of 2016-2018 were evaluated retrospectively. Data on clinicopathological characteristics, treatment response, and toxicity were analyzed.!##!Results!#!Fourteen melanoma patients were treated with T-VEC in our center. Six patients (43%) received T-VEC first-line. In eight patients (57%), T-VEC followed a prior systemic therapy. Three patients with M1b stage and one patient with M1c stage melanoma were treated with T-VEC. These patients suffered from loco-regional progress, whilst distant metastases had regressed during prior systemic treatment. 64% of patients showed a benefit from therapy with T-VEC. The durable response rate was 36%.!##!Conclusion!#!T-VEC represents an effective and tolerable treatment option. This is true not only for loco-regionally advanced melanoma patients, but also for patients with stable or regressive systemic metastases who develop loco-regionally acquired resistance upon treatment with immune checkpoint blockade or targeted therapy. A sensible selection of suitable patients seems to be crucial

Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors

Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy

Immune checkpoint inhibition therapy for advanced skin cancer in patients with concomitant hematological malignancy: a retrospective multicenter DeCOG study of 84 patients

Background Skin cancers are known for their strong immunogenicity, which may contribute to a high treatment efficacy of immune checkpoint inhibition (ICI). However, a considerable proportion of patients with skin cancer is immuno-compromised by concomitant diseases. Due to their previous exclusion from clinical trials, the ICI treatment efficacy is poorly investigated in these patients. The present study analyzed the ICI treatment outcome in advanced patients with skin cancer with a concomitant hematological malignancy. Methods This retrospective multicenter study included patients who were treated with ICI for locally advanced or metastatic melanoma (MM), cutaneous squamous cell carcinoma (cSCC), or Merkel cell carcinoma (MCC), and had a previous diagnosis of a hematological malignancy irrespective of disease activity or need of therapy at ICI treatment start. Comparator patient cohorts without concomitant hematological malignancy were extracted from the prospective multicenter skin cancer registry ADOREG. Treatment outcome was measured as best overall response, progression-free (PFS), and overall survival (OS). Results 84 patients (MM, n=52; cSCC, n=15; MCC, n=17) with concomitant hematological malignancy were identified at 20 skin cancer centers. The most frequent concomitant hematological malignancies were non-Hodgkin's lymphoma (n=70), with chronic lymphocytic leukemia (n=32) being the largest entity. While 9 patients received ICI in an adjuvant setting, 75 patients were treated for advanced non-resectable disease (55 anti-PD-1; 8 anti-PD-L1; 5 anti-CTLA-4; 7 combinations). In the latter 75 patients, best objective response (complete response+partial response) was 28.0%, disease stabilization was 25.3%, and 38.6% showed progressive disease (PD). Subdivided by skin cancer entity, best objective response was 31.1% (MM), 26.7% (cSCC), and 18.8% (MCC). Median PFS was 8.4 months (MM), 4.0 months (cSCC), and 5.7 months (MCC). 1-year OS rates were 78.4% (MM), 65.8% (cSCC), and 47.4% (MCC). Comparison with respective ADOREG patient cohorts without hematological malignancy (n=392) revealed no relevant differences in ICI therapy outcome for MM and MCC, but a significantly reduced PFS for cSCC (p=0.002). Conclusions ICI therapy showed efficacy in advanced patients with skin cancer with a concomitant hematological malignancy. Compared with patients without hematological malignancy, the observed ICI therapy outcome was impaired in cSCC, but not in MM or MCC patients

Outcome of melanoma patients with elevated LDH treated with first-line targeted therapy or PD-1-based immune checkpoint inhibition

Background: Elevated lactate dehydrogenase (LDH) is a known predictive and prognostic factor for a poor outcome in patients with metastatic melanoma. It is unclear whether first-line targeted therapy (TT) or immune checkpoint inhibition (ICI) is more beneficial in melanoma patients with elevated LDH because prospective studies in this area are lacking. Methods: This multicentre retrospective cohort study was conducted at 25 melanoma centres worldwide to analyse progression-free survival (PFS) and overall survival (OS) among melanoma patients with elevated LDH. The role of confounders was addressed by using inverse probability of treatment weighting. Results: Among 173 BRAFV600-mutant patients, PFS at 12 months in the TT group was 22% compared with 52% in the combined anti-PD-1 and anti-CTLA-4 group (HR 0.6, 95% CI 0.4-1.0, p = 0.07) and 18% in the anti-PD-1 monotherapy group (HR 1.8, 95% CI 1.2-2.8, p = 0.003). Twelve months' OS was 48% in the TT group compared with 83% in the combined anti-PD-1 and anti-CTLA-4 group (HR 0.5, 95% CI 0.3-1.0, p = 0.03) and 50% in the anti-PD-1 monotherapy group (HR 1.2, 95% CI 0.8-2.0, p = 0.37). The ORR in the TT group was 63%, compared with 55% and 20% in the combined anti-PD-1 and anti-CTLA-4 and anti-PD-1 monotherapy group, respectively. Among 314 patients receiving ICI first-line, PFS at 12 months was 33% in the anti-PD-1 group versus 38% in the combined anti-PD-1 and anti-CTLA-4 group (HR 0.8, 95% CI 0.6-1.0; p = 0.07). OS at 12 months was 54% in the anti-PD-1 group versus 66% in the combined ICI group (HR 0.7, 95% CI 0.5-1.0; p = 0.03). The ORR was 30% in the anti-PD-1 monotherapy group and 43% in the combined anti-PD-1 and anti-CTLA-4 group. Results from multivariate analysis confirmed the absence of qualitative confounding. Conclusions: Among BRAF-mutant patients with elevated LDH, combined anti-PD-1 and anti-CTLA-4 blockade seems to be associated with prolonged OS compared with first-line TT. Among patients receiving ICI as a first-line treatment, OS appears to be longer for the combination of anti-PD-1 and anti-CTLA-4 than for anti-PD-1 alone. (C) 2021 Elsevier Ltd. All rights reserved

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)