TY - JOUR A1 - Brinker, Titus J. A1 - Hekler, Achim A1 - Enk, Alexander H. A1 - Berking, Carola A1 - Haferkamp, Sebastian A1 - Hauschild, Axel A1 - Weichenthal, Michael A1 - Klode, Joachim A1 - Schadendorf, Dirk A1 - Holland-Letz, Tim A1 - von Kalle, Christof A1 - Fröhling, Stefan A1 - Schilling, Bastian A1 - Utikal, Jochen S. T1 - Deep neural networks are superior to dermatologists in melanoma image classification JF - European Journal of Cancer N2 - Background Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date. Methods For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes. Findings The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%–71.7%) and 62.2% (95% CI: 57.6%–66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%–85.7%) and a higher specificity of 77.9% (95% CI: 73.8%–81.8%). The three McNemar's tests in 2 × 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups. Interpretation For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001). KW - deep learning KW - melanoma KW - skin cancer KW - artificial intelligence Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220539 VL - 119 ER - TY - JOUR A1 - Brinker, Titus J. A1 - Hekler, Achim A1 - Hauschild, Axel A1 - Berking, Carola A1 - Schilling, Bastian A1 - Enk, Alexander H. A1 - Haferkamp, Sebastian A1 - Karoglan, Ante A1 - von Kalle, Christof A1 - Weichenthal, Michael A1 - Sattler, Elke A1 - Schadendorf, Dirk A1 - Gaiser, Maria R. A1 - Klode, Joachim A1 - Utikal, Jochen S. T1 - Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark JF - European Journal of Cancer N2 - Background Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field. Methods An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC). Results Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538–0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613–0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark. Conclusions We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification. KW - benchmark KW - artificial intelligence KW - deep learning KW - melanoma Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220569 VL - 111 ER - TY - JOUR A1 - Glutsch, Valerie A1 - Schummer, Patrick A1 - Kneitz, Hermann A1 - Gesierich, Anja A1 - Goebeler, Matthias A1 - Klein, Detlef A1 - Posch, Christian A1 - Gebhardt, Christoffer A1 - Haferkamp, Sebastian A1 - Zimmer, Lisa A1 - Becker, Jürgen C A1 - Leiter, Ulrike A1 - Weichenthal, Michael A1 - Schadendorf, Dirk A1 - Ugurel, Selma A1 - Schilling, Bastian T1 - Ipilimumab plus nivolumab in avelumab-refractory Merkel cell carcinoma: a multicenter study of the prospective skin cancer registry ADOREG JF - Journal for ImmunoTherapy of Cancer N2 - Merkel cell carcinoma is a rare, highly aggressive skin cancer with neuroendocrine differentiation. Immune checkpoint inhibition has significantly improved treatment outcomes in metastatic disease with response rates to programmed cell death protein 1/programmed cell death 1 ligand 1 (PD-1/PD-L1) inhibition of up to 62%. However, primary and secondary resistance to PD-1/PD-L1 inhibition remains a so far unsolved clinical challenge since effective and safe treatment options for these patients are lacking.Fourteen patients with advanced (non-resectable stage III or stage IV, Union international contre le cancer 2017) Merkel cell carcinoma with primary resistance to the PD-L1 inhibitor avelumab receiving subsequent therapy (second or later line) with ipilimumab plus nivolumab (IPI/NIVO) were identified in the prospective multicenter skin cancer registry ADOREG. Five of these 14 patients were reported previously and were included in this analysis with additional follow-up. Overall response rate, progression-free survival (PFS), overall survival (OS) and adverse events were analyzed.All 14 patients received avelumab as first-line treatment. Thereof, 12 patients had shown primary resistance with progressive disease in the first tumor assessment, while two patients had initially experienced a short-lived stabilization (stable disease). Six patients had at least one systemic treatment in between avelumab and IPI/NIVO. In total, 7 patients responded to IPI/NIVO (overall response rate 50%), and response was ongoing in 4 responders at last follow-up. After a median follow-up of 18.85 months, median PFS was 5.07 months (95% CI 2.43—not available (NA)), and median OS was not reached. PFS rates at 12 months and 24 months were 42.9% and 26.8 %, respectively. The OS rate at 36 months was 64.3%. Only 3 (21%) patients did not receive all 4 cycles of IPI/NIVO due to immune-related adverse events.In this multicenter evaluation, we observed high response rates, a durable benefit and promising OS rates after treatment with later-line combined IPI/NIVO. In conclusion, our patient cohort supports our prior findings with an encouraging activity of second-line or later-line IPI/NIVO in patients with anti-PD-L1-refractory Merkel cell carcinoma. KW - Skin Neoplasms KW - CTLA-4 Antigen KW - Programmed Cell Death 1 Receptor KW - B7-H1 Antigen KW - Drug Therapy, Combination Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304613 SN - 2051-1426 VL - 10 IS - 11 ER - TY - JOUR A1 - Haist, Maximilian A1 - Stege, Henner A1 - Lang, Berenice Mareen A1 - Tsochataridou, Aikaterini A1 - Salzmann, Martin A1 - Mohr, Peter A1 - Schadendorf, Dirk A1 - Ugurel, Selma A1 - Placke, Jan-Malte A1 - Weichenthal, Michael A1 - Gutzmer, Ralf A1 - Leiter, Ulrike A1 - Kaatz, Martin A1 - Haferkamp, Sebastian A1 - Berking, Carola A1 - Heppt, Markus A1 - Tschechne, Barbara A1 - Schummer, Patrick A1 - Gebhardt, Christoffer A1 - Grabbe, Stephan A1 - Loquai, Carmen T1 - Response to first-line treatment with immune-checkpoint inhibitors in patients with advanced cutaneous squamous cell carcinoma: a multicenter, retrospective analysis from the German ADOReg registry JF - Cancers N2 - Cutaneous squamous cell carcinoma (cSCC) is a common malignancy of the skin and has an overall favorable outcome, except for patients with an advanced stage of the disease. The efficacy of checkpoint inhibitors (CPI) for advanced cSCC has been demonstrated in recent clinical studies, but data from real-world cohorts and trial-ineligible cSCC patients are limited. We retrospectively investigated patients with advanced cSCC who have been treated with CPI in a first-line setting at eight German skin cancer centers registered within the multicenter registry ADOReg. Clinical outcome parameters including response, progression-free (PFS) and overall survival (OS), time-to-next-treatment (TTNT), and toxicity were analyzed and have been stratified by the individual immune status. Among 39 evaluable patients, the tumor response rate (rwTRR) was 48.6%, the median PFS was 29.0 months, and the median OS was not reached. In addition, 9 patients showed an impaired immune status due to immunosuppressive medication or hematological diseases. Our data demonstrated that CPI also evoked tumor responses among immunocompromised patients (rwTRR: 48.1 vs. 50.0%), although these responses less often resulted in durable remissions. In line with this, the median PFS (11 vs. 40 months, p = 0.059), TTNT (12 months vs. NR, p = 0.016), and OS (29 months vs. NR, p < 0.001) were significantly shorter for this patient cohort. CPI therapy was well tolerated in both subcohorts with 15% discontinuing therapy due to toxicity. Our real-world data show that first-line CPI therapy produced strong and durable responses among patients with advanced cSCC. Immunocompromised patients were less likely to achieve long-term benefit from anti-PD1 treatment, despite similar tumor response rates. KW - advanced cutaneous squamous cell carcinoma KW - checkpoint inhibitor therapy KW - cemiplimab KW - immunosuppression KW - response durability KW - real-world data Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297506 SN - 2072-6694 VL - 14 IS - 22 ER - TY - JOUR A1 - Koch, Elias A. T. A1 - Petzold, Anne A1 - Wessely, Anja A1 - Dippel, Edgar A1 - Gesierich, Anja A1 - Gutzmer, Ralf A1 - Hassel, Jessica C. A1 - Haferkamp, Sebastian A1 - Hohberger, Bettina A1 - Kähler, Katharina C. A1 - Knorr, Harald A1 - Kreuzberg, Nicole A1 - Leiter, Ulrike A1 - Loquai, Carmen A1 - Meier, Friedegund A1 - Meissner, Markus A1 - Mohr, Peter A1 - Pföhler, Claudia A1 - Rahimi, Farnaz A1 - Schadendorf, Dirk A1 - Schell, Beatrice A1 - Schlaak, Max A1 - Terheyden, Patrick A1 - Thoms, Kai-Martin A1 - Schuler-Thurner, Beatrice A1 - Ugurel, Selma A1 - Ulrich, Jens A1 - Utikal, Jochen A1 - Weichenthal, Michael A1 - Ziller, Fabian A1 - Berking, Carola A1 - Heppt, Markus T1 - Immune checkpoint blockade for metastatic uveal melanoma: patterns of response and survival according to the presence of hepatic and extrahepatic metastasis JF - Cancers N2 - Background: Since there is no standardized and effective treatment for advanced uveal melanoma (UM), the prognosis is dismal once metastases develop. Due to the availability of immune checkpoint blockade (ICB) in the real-world setting, the prognosis of metastatic UM has improved. However, it is unclear how the presence of hepatic and extrahepatic metastasis impacts the response and survival after ICB. Methods: A total of 178 patients with metastatic UM treated with ICB were included in this analysis. Patients were recruited from German skin cancer centers and the German national skin cancer registry (ADOReg). To investigate the impact of hepatic metastasis, two cohorts were compared: patients with liver metastasis only (cohort A, n = 55) versus those with both liver and extra-hepatic metastasis (cohort B, n = 123). Data were analyzed in both cohorts for response to treatment, progression-free survival (PFS), and overall survival (OS). The survival and progression probabilities were calculated with the Kaplan–Meier method. Log-rank tests, χ\(^2\) tests, and t-tests were performed to detect significant differences between both cohorts. Results: The median OS of the overall population was 16 months (95% CI 13.4–23.7) and the median PFS, 2.8 months (95% CI 2.5–3.0). The median OS was longer in cohort B than in cohort A (18.2 vs. 6.1 months; p = 0.071). The best objective response rate to dual ICB was 13.8% and to anti-PD-1 monotherapy 8.9% in the entire population. Patients with liver metastases only had a lower response to dual ICB, yet without significance (cohort A 8.7% vs. cohort B 16.7%; p = 0.45). Adverse events (AE) occurred in 41.6%. Severe AE were observed in 26.3% and evenly distributed between both cohorts. Conclusion: The survival of this large cohort of patients with advanced UM was more favorable than reported in previous benchmark studies. Patients with both hepatic and extrahepatic metastasis showed more favorable survival and higher response to dual ICB than those with hepatic metastasis only. KW - uveal melanoma KW - immune checkpoint blockade KW - PD-1 KW - CTLA-4 KW - liver metastasis KW - treatment resistance Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242603 SN - 2072-6694 VL - 13 IS - 13 ER - TY - JOUR A1 - Koch, Elias A. T. A1 - Petzold, Anne A1 - Wessely, Anja A1 - Dippel, Edgar A1 - Gesierich, Anja A1 - Gutzmer, Ralf A1 - Hassel, Jessica C. A1 - Haferkamp, Sebastian A1 - Kähler, Katharina C. A1 - Knorr, Harald A1 - Kreuzberg, Nicole A1 - Leiter, Ulrike A1 - Loquai, Carmen A1 - Meier, Friedegund A1 - Meissner, Markus A1 - Mohr, Peter A1 - Pföhler, Claudia A1 - Rahimi, Farnaz A1 - Schadendorf, Dirk A1 - Schell, Beatrice A1 - Schlaak, Max A1 - Terheyden, Patrick A1 - Thoms, Kai-Martin A1 - Schuler-Thurner, Beatrice A1 - Ugurel, Selma A1 - Ulrich, Jens A1 - Utikal, Jochen A1 - Weichenthal, Michael A1 - Ziller, Fabian A1 - Berking, Carola A1 - Heppt, Markus V. T1 - Immune checkpoint blockade for metastatic uveal melanoma: re-induction following resistance or toxicity JF - Cancers N2 - Re-induction with immune checkpoint blockade (ICB) needs to be considered in many patients with uveal melanoma (UM) due to limited systemic treatment options. Here, we provide hitherto the first analysis of ICB re-induction in UM. A total of 177 patients with metastatic UM treated with ICB were included from German skin cancer centers and the German national skin cancer registry (ADOReg). To investigate the impact of ICB re-induction, two cohorts were compared: patients who received at least one ICB re-induction (cohort A, n = 52) versus those who received only one treatment line of ICB (cohort B, n = 125). In cohort A, a transient benefit of overall survival (OS) was observed at 6 and 12 months after the treatment start of ICB. There was no significant difference in OS between both groups (p = 0.1) with a median OS of 16.2 months (cohort A, 95% CI: 11.1–23.8) versus 9.4 months (cohort B, 95% CI: 6.1–14.9). Patients receiving re-induction of ICB (cohort A) had similar response rates compared to those receiving ICB once. Re-induction of ICB may yield a clinical benefit for a small subgroup of patients even after resistance or development of toxicities. KW - uveal melanoma KW - immune checkpoint blockade KW - PD-1 KW - CTLA-4 KW - re-induction KW - treatment resistance KW - toxicity Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-254814 SN - 2072-6694 VL - 14 IS - 3 ER - TY - JOUR A1 - Sondermann, Wiebke A1 - Utikal, Jochen Sven A1 - Enk, Alexander H. A1 - Schadendorf, Dirk A1 - Klode, Joachim A1 - Hauschild, Axel A1 - Weichenthal, Michael A1 - French, Lars E. A1 - Berking, Carola A1 - Schilling, Bastian A1 - Haferkamp, Sebastian A1 - Fröhling, Stefan A1 - von Kalle, Christof A1 - Brinker, Titus J. T1 - Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data JF - European Journal of Cancer N2 - Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30–50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its high relevance for melanoma screening, neither clinicians nor computers are yet able to reliably predict a nevus’ oncologic transformation. The cause of this lies in the static nature of lesion presentation in the current standard of care, both for clinicians and algorithms. The status quo makes it difficult to train algorithms (and clinicians) to precisely assess the likelihood of a benign skin lesion to transform into melanoma. In addition, it inhibits the precision of current algorithms since ‘evolution’ image features may not be part of their decision. The current literature reveals certain types of melanocytic nevi (i.e. ‘spitzoid’ or ‘dysplastic’ nevi) and criteria (i.e. visible vasculature) that, in general, appear to have a higher chance to transform into melanoma. However, owing to the cumulative nature of oncogenic mutations in melanoma, a more fine-grained early morphologic footprint is likely to be detectable by an algorithm. In this perspective article, the concept of melanoma prediction is further explored by the discussion of the evolution of melanoma, the concept for training of such a nevi classifier and the implications of early melanoma prediction for clinical practice. In conclusion, the authors believe that artificial intelligence trained on prospective image data could be transformative for skin cancer diagnostics by (a) predicting melanoma before it occurs (i.e. pre-in situ) and (b) further enhancing the accuracy of current melanoma classifiers. Necessary prospective images for this research are obtained via free mole-monitoring mobile apps. KW - melanoma KW - skin cancer KW - artificial Intelligence KW - deep learning KW - prediction Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-239263 VL - 119 ER -