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1. Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study

Author

Chanda, Tirtha, Haggenmueller, Sarah, Bucher, Tabea-Clara, Holland-Letz, Tim, Kittler, Harald, Tschandl, Philipp, Heppt, Markus V., Berking, Carola, Utikal, Jochen S., Schilling, Bastian, Buerger, Claudia, Navarrete-Dechent, Cristian, Goebeler, Matthias, Kather, Jakob Nikolas, Schneider, Carolin V., Durani, Benjamin, Durani, Hendrike, Jansen, Martin, Wacker, Juliane, Wacker, Joerg, Consortium, Reader Study, and Brinker, Titus J.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract

Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics.

Published
2024

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

Author

Maron, Roman C, Utikal, Jochen S, Hekler, Achim, Hauschild, Axel, Sattler, Elke, Sondermann, Wiebke, Haferkamp, Sebastian, Schilling, Bastian, Heppt, Markus V, Jansen, Philipp, Reinholz, Markus, Franklin, Cindy, Schmitt, Laurenz, Hartmann, Daniela, Krieghoff-Henning, Eva, Schmitt, Max, Weichenthal, Michael, von Kalle, Christof, Fröhling, Stefan, and Brinker, Titus J

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundEarly 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. ObjectiveThe 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. MethodsTwelve 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. ResultsWhile 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. ConclusionsThe 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.

Published
2020
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3. Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study

Author

Heinlein, Lukas, Maron, Roman C., Hekler, Achim, Haggenmüller, Sarah, Wies, Christoph, Utikal, Jochen S., Meier, Friedegund, Hobelsberger, Sarah, Gellrich, Frank F., Sergon, Mildred, Hauschild, Axel, French, Lars E., Heinzerling, Lucie, Schlager, Justin G., Ghoreschi, Kamran, Schlaak, Max, Hilke, Franz J., Poch, Gabriela, Korsing, Sören, Berking, Carola, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Krieghoff-Henning, Eva, and Brinker, Titus J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Statistics - Applications
Abstract

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. 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. Overall, the AI showed 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<0.001), obtaining a higher sensitivity (0.921, 95% CI 0.900- 0.942 vs. 0.734, 95% CI 0.701-0.770; p<0.001) 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<0.001). As the algorithm exhibited 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.

Published
2024

4. Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care

Author

Heinlein, Lukas, Maron, Roman C., Hekler, Achim, Haggenmüller, Sarah, Wies, Christoph, Utikal, Jochen S., Meier, Friedegund, Hobelsberger, Sarah, Gellrich, Frank F., Sergon, Mildred, Hauschild, Axel, French, Lars E., Heinzerling, Lucie, Schlager, Justin G., Ghoreschi, Kamran, Schlaak, Max, Hilke, Franz J., Poch, Gabriela, Korsing, Sören, Berking, Carola, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Krieghoff-Henning, Eva, and Brinker, Titus J.

Published
2024
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5. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Author

Chanda, Tirtha, Hauser, Katja, Hobelsberger, Sarah, Bucher, Tabea-Clara, Garcia, Carina Nogueira, Wies, Christoph, Kittler, Harald, Tschandl, Philipp, Navarrete-Dechent, Cristian, Podlipnik, Sebastian, Chousakos, Emmanouil, Crnaric, Iva, Majstorovic, Jovana, Alhajwan, Linda, Foreman, Tanya, Peternel, Sandra, Sarap, Sergei, Özdemir, İrem, Barnhill, Raymond L., Llamas-Velasco, Mar, Poch, Gabriela, Korsing, Sören, Sondermann, Wiebke, Gellrich, Frank Friedrich, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Goebeler, Matthias, Schilling, Bastian, Utikal, Jochen S., Ghoreschi, Kamran, Fröhling, Stefan, Krieghoff-Henning, Eva, and Brinker, Titus J.

Published
2024
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7. Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study

Author

Hekler, Achim, Maron, Roman C., Haggenmüller, Sarah, Schmitt, Max, Wies, Christoph, Utikal, Jochen S., Meier, Friedegund, Hobelsberger, Sarah, Gellrich, Frank F., Sergon, Mildred, Hauschild, Axel, French, Lars E., Heinzerling, Lucie, Schlager, Justin G., Ghoreschi, Kamran, Schlaak, Max, Hilke, Franz J., Poch, Gabriela, Korsing, Sören, Berking, Carola, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Kather, Jakob N., Krieghoff-Henning, Eva, and Brinker, Titus J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the impact of multiple real-world dermoscopic views of a single lesion of interest on a CNN-based melanoma classifier. Methods: This study evaluated 656 suspected melanoma lesions. Classifier performance was measured using area under the receiver operating characteristic curve (AUROC), expected calibration error (ECE) and maximum confidence change (MCC) for (I) a single-view scenario, (II) a multiview scenario using multiple artificially modified images per lesion and (III) a multiview scenario with multiple real-world images per lesion. Results: The multiview approach with real-world images significantly increased the AUROC from 0.905 (95% CI, 0.879-0.929) in the single-view approach to 0.930 (95% CI, 0.909-0.951). ECE and MCC also improved significantly from 0.131 (95% CI, 0.105-0.159) to 0.072 (95% CI: 0.052-0.093) and from 0.149 (95% CI, 0.125-0.171) to 0.115 (95% CI: 0.099-0.131), respectively. Comparing multiview real-world to artificially modified images showed comparable diagnostic accuracy and uncertainty estimation, but significantly worse robustness for the latter. Conclusion: Using multiple real-world images is an inexpensive method to positively impact the performance of a CNN-based melanoma classifier.

Published
2023
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9. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Author

Chanda, Tirtha, Hauser, Katja, Hobelsberger, Sarah, Bucher, Tabea-Clara, Garcia, Carina Nogueira, Wies, Christoph, Kittler, Harald, Tschandl, Philipp, Navarrete-Dechent, Cristian, Podlipnik, Sebastian, Chousakos, Emmanouil, Crnaric, Iva, Majstorovic, Jovana, Alhajwan, Linda, Foreman, Tanya, Peternel, Sandra, Sarap, Sergei, Özdemir, İrem, Barnhill, Raymond L., Velasco, Mar Llamas, Poch, Gabriela, Korsing, Sören, Sondermann, Wiebke, Gellrich, Frank Friedrich, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Goebeler, Matthias, Schilling, Bastian, Utikal, Jochen S., Ghoreschi, Kamran, Fröhling, Stefan, Krieghoff-Henning, Eva, and Brinker, Titus J.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XAI methods are unable to produce precisely located domain-specific explanations, making the explanations difficult to interpret. Moreover, the impact of XAI methods on dermatologists has not yet been evaluated. Extending on two existing classifiers, we developed an XAI system that produces text and region based explanations that are easily interpretable by dermatologists alongside its differential diagnoses of melanomas and nevi. To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support. We showed that our XAI's explanations were highly aligned with clinicians' explanations and that both the clinicians' trust in the support system and their confidence in their diagnoses were significantly increased when using our XAI compared to using a conventional AI system. The clinicians' diagnostic accuracy was numerically, albeit not significantly, increased. This work demonstrates that clinicians are willing to adopt such an XAI system, motivating their future use in the clinic.

Published
2023
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10. Improved survival of advanced melanoma patients receiving immunotherapy with concomitant antithrombotic therapy – A multicenter study on 2419 patients from the prospective skin cancer registry ADOReg

Author

Kött, Julian, Zell, Tim, Zimmermann, Noah, Rünger, Alessandra, Smit, Daniel J., Abeck, Finn, Geidel, Glenn, Hansen-Abeck, Inga, Heidrich, Isabel, Weichenthal, Michael, Ugurel, Selma, Leiter, Ulrike, Berking, Carola, Gutzmer, Ralf, Schadendorf, Dirk, Zimmer, Lisa, Livingstone, Elisabeth, Wasielewski, Imke von, Mohr, Peter, Meier, Friedegund, Haferkamp, Sebastian, Drexler, Konstantin, Herbst, Rudolf, Kellner, Ivonne, Utikal, Jochen, Wohlfeil, Sebastian A., Pföhler, Claudia, Adam, Leonie, Terheyden, Patrick, Ulrich, Jens, Meiss, Frank, Möbes, Monica, Welzel, Julia, Schilling, Bastian, Ziller, Fabian, Kaatz, Martin, Kreuter, Alexander, Sindrilaru, Anca, Dippel, Edgar, Sachse, Michael, Weishaupt, Carsten, Hüning, Svea, Heinzerling, Lucie, Loquai, Carmen, Schley, Gaston, Gambichler, Thilo, Löffler, Harald, Grabbe, Stephan, Schultz, Erwin, Devereux, Nina, Hassel, Jesscia C., Simon, Jan-Ch., Raap, Ulrike, Assaf, Chalid, Klemke, Claus-Detlev, Sunderkötter, Cord, Hofmann, Silke C., Wenk, Saskia, Tronnier, Michael, Thies, Silke, Heppt, Markus V., Eggermont, Alexander, Schulze, Hans-Joachim, Zouboulis, Christos C., Tüting, Thomas, Bauer, Alexander T., Schneider, Stefan W., and Gebhardt, Christoffer

Published
2025
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13. Association of antibiotic treatment with survival outcomes in treatment-naïve melanoma patients receiving immune checkpoint blockade

Author

Chorti, Eleftheria, Kowall, Bernd, Hassel, Jessica C., Schilling, Bastian, Sachse, Michael, Gutzmer, Ralf, Loquai, Carmen, Kähler, Katharina C., Hüsing, Anika, Gilde, Catharina, Thielmann, Carl M., Zaremba-Montenari, Anne, Placke, Jan-Malte, Gratsias, Emmanouil, Martaki, Anna, Roesch, Alexander, Ugurel, Selma, Schadendorf, Dirk, Livingstone, Elisabeth, Stang, Andreas, and Zimmer, Lisa

Published
2024
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15. Differential effects of PD-1 and CTLA-4 blockade on the melanoma-reactive CD8 T cell response

Author

Gangaev, Anastasia, Rozeman, Elisa A, Rohaan, Maartje W, Isaeva, Olga I, Philips, Daisy, Patiwael, Sanne, van den Berg, Joost H, Ribas, Antoni, Schadendorf, Dirk, Schilling, Bastian, Schumacher, Ton N, Blank, Christian U, Haanen, John BAG, and Kvistborg, Pia

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Health Disparities, Cancer, Clinical Research, 5.1 Pharmaceuticals, Adult, Aged, Aged, 80 and over, CD8-Positive T-Lymphocytes, CTLA-4 Antigen, Cohort Studies, Epitopes, T-Lymphocyte, Female, Hepatitis A Virus Cellular Receptor 2, Humans, Immune Checkpoint Inhibitors, In Vitro Techniques, Kinetics, Lymphocyte Activation, Male, Melanoma, Melanoma-Specific Antigens, Middle Aged, Programmed Cell Death 1 Receptor, Receptors, Antigen, T-Cell, alpha-beta, Receptors, CXCR5, PD-1, CTLA-4, melanoma-reactive CD8 T cells
Abstract

Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4) have revolutionized the treatment of melanoma patients. Based on early studies addressing the mechanism of action, it was assumed that PD-1 blockade mostly influences T cell responses at the tumor site. However, recent work has demonstrated that PD-1 blockade can influence the T cell compartment in peripheral blood. If the activation of circulating, tumor-reactive T cells would form an important mechanism of action of PD-1 blockade, it may be predicted that such blockade would alter either the frequency and/or the breadth of the tumor-reactive CD8 T cell response. To address this question, we analyzed CD8 T cell responses toward 71 melanoma-associated epitopes in peripheral blood of 24 melanoma patients. We show that both the frequency and the breadth of the circulating melanoma-reactive CD8 T cell response was unaltered upon PD-1 blockade. In contrast, a broadening of the circulating melanoma-reactive CD8 T cell response was observed upon CTLA-4 blockade, in concordance with our prior data. Based on these results, we conclude that PD-1 and CTLA-4 blockade have distinct mechanisms of action. In addition, the data provide an argument in favor of the hypothesis that anti-PD-1 therapy may primarily act at the tumor site.

Published
2021

16. Adjuvant immunotherapy with nivolumab versus observation in completely resected Merkel cell carcinoma (ADMEC-O): disease-free survival results from a randomised, open-label, phase 2 trial

Author

van Akkooi, Alexander, van Houdt, Winan, Wilhelm, Tabea, Farmer, Kimberley, Ulrich, Claas, Gambichler, Thilo, Bluhm, Leonie, Schinagl, Heidemarie, Kellner, Ivonne, Herbst, Rudolf, Meiß, Frank, Rafei-Shamsabadi, David, Sell, Sabine, Kaatz, Martin, Wulfken, Lena, Hartmann, Martin, Kähler, Katharina, Ziemer, Mirjana, Simon, Jan, Terheyden, Patrick, Thaci, Diamant, Loquai, Carmen, Mitzel-Rink, Heidrun, Grabbe, Stephan, Stege, Henner, Gaiser, Maria, Utikal, Jochen, Berking, Carola, Heinzerling, Lucie, Schlaak, Max, Tomsitz, Dirk, Dyballa, Jörg, Magnolo, Nina, Weishaupt, Carsten, Berneburg, Mark, Garbe, Claus, Flatz, Lukas, Gesierich, Anja, Schilling, Bastian, Becker, Jürgen C, Ugurel, Selma, Leiter, Ulrike, Meier, Friedegund, Gutzmer, Ralf, Haferkamp, Sebastian, Zimmer, Lisa, Livingstone, Elisabeth, Eigentler, Thomas K, Hauschild, Axel, Kiecker, Felix, Hassel, Jessica C, Mohr, Peter, Fluck, Michael, Thomas, Ioannis, Garzarolli, Marlene, Grimmelmann, Imke, Drexler, Konstantin, Spillner, Alexandra N, Eckhardt, Sebastian, and Schadendorf, Dirk

Published
2023
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17. Adjuvant treatment and outcome of stage III melanoma patients: Results of a multicenter real-world German Dermatologic Cooperative Oncology Group (DeCOG) study

Author

Lodde, Georg C., Hassel, Jessica, Wulfken, Lena M., Meier, Friedegund, Mohr, Peter, Kähler, Katharina, Hauschild, Axel, Schilling, Bastian, Loquai, Carmen, Berking, Carola, Hüning, Svea, Eckardt, Julia, Gutzmer, Ralf, Reinhardt, Lydia, Glutsch, Valerie, Nikfarjam, Ulrike, Erdmann, Michael, Beckmann, Catharina L., Stang, Andreas, Kowall, Bernd, Galetzka, Wolfgang, Roesch, Alexander, Ugurel, Selma, Zimmer, Lisa, Schadendorf, Dirk, Forschner, Andrea, and Livingstone, Elisabeth

Published
2023
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18. Clinical, histopathological and molecular features of dedifferentiated melanomas: An EORTC Melanoma Group Retrospective Analysis

Author

Hench, Juergen, Mihic-Probst, Daniela, Agaimy, Abbas, Frank, Stephan, Meyer, Peter, Hultschig, Claus, Simi, Sara, Alos, Lucia, Balamurugan, Thiagarajah, Blokx, Willeke, Bosisio, Francesca, Cappellesso, Rocco, Griewank, Klaus, Hadaschik, Eva, van Kempen, Leon C., Kempf, Werner, Lentini, Maria, Mazzucchelli, Luca, Rinaldi, Gaetana, Rutkowski, Piotr, Schadendorf, Dirk, Schilling, Bastian, Szumera-Cieckiewicz, Anna, van den Oord, Joost, Mandalà, Mario, and Massi, Daniela

Published
2023
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22. Adjuvant nivolumab plus ipilimumab or nivolumab alone versus placebo in patients with resected stage IV melanoma with no evidence of disease (IMMUNED): final results of a randomised, double-blind, phase 2 trial

Author

Livingstone, Elisabeth, Zimmer, Lisa, Hassel, Jessica C, Fluck, Michael, Eigentler, Thomas K, Loquai, Carmen, Haferkamp, Sebastian, Gutzmer, Ralf, Meier, Friedegund, Mohr, Peter, Hauschild, Axel, Schilling, Bastian, Menzer, Christian, Kiecker, Felix, Dippel, Edgar, Roesch, Alexander, Ziemer, Mirjana, Conrad, Beate, Körner, Silvia, Simon, Jan-Christoph, Herbst, Rudolf A, Berking, Carola, Utikal, Jochen, Sell, Sabine, Martens, Uwe M, Terheyden, Patrick, Stadler, Rudolf, Windemuth-Kieselbach, Christine, Schwarz, Leonora, Garbe, Claus, Becker, Jürgen C, and Schadendorf, Dirk

Published
2022
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23. Model soups improve performance of dermoscopic skin cancer classifiers

Author

Maron, Roman C., Hekler, Achim, Haggenmüller, Sarah, von Kalle, Christof, Utikal, Jochen S., Müller, Verena, Gaiser, Maria, Meier, Friedegund, Hobelsberger, Sarah, Gellrich, Frank F., Sergon, Mildred, Hauschild, Axel, French, Lars E., Heinzerling, Lucie, Schlager, Justin G., Ghoreschi, Kamran, Schlaak, Max, Hilke, Franz J., Poch, Gabriela, Korsing, Sören, Berking, Carola, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Kather, Jakob N., Fröhling, Stefan, Lipka, Daniel B., Krieghoff-Henning, Eva, and Brinker, Titus J.

Published
2022
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24. Re-exposition to ipilimumab plus nivolumab in metastatic Merkel cell carcinoma.

Author

Glutsch, Valerie, Schummer, Patrick, Goebeler, Matthias, Gesierich, Anja, and Schilling, Bastian

Subjects
IMMUNE checkpoint inhibitors, MERKEL cell carcinoma, TREATMENT effectiveness, DISEASE progression, NIVOLUMAB
Abstract

Background: Merkel cell carcinoma (MCC) is a rare but highly aggressive cutaneous malignancy. Immune checkpoint inhibition (ICI) with PD-(L)1 blockade has significantly improved treatment outcomes in metastatic disease. In patients with primary resistance to PD-(L)1 inhibition, a high overall response rate (ORR) of 50% to later-line ipilimumab plus nivolumab (IPI/NIVO) has been demonstrated. However, clinical data on patients with progression after an initial response to IPI/NIVO are still lacking. Methods: Clinical data of three metastatic MCC patients who were re-exposed to IPI/NIVO after progression were retrospectively evaluated. Results: Two of the three patients showed primary resistance to avelumab with progressive disease, while one patient showed complete response (according to RECIST V.1.1). All three patients received combined ICI with IPI/NIVO as subsequent therapy, resulting in an ORR of ∼ 67%. However, all three patients progressed during follow-up and were re-exposed to IPI/NIVO. With a follow-up period ranging from 6.5 to 37.1 months, no PFS event has been detected. ORR for IPI/NIVO re-exposition was equal to that of initial IPI/NIVO treatment. Conclusion: In this retrospective follow-up analysis, we observed a response rate of 67% and long-lasting responses after re-exposition to combined ICI in metastatic MCC patients with progression after initial response or disease control upon their first IPI/NIVO treatment. An important observation from this small analysis is that primary resistance to PD-L1 inhibition may result in a better response to IPI/NIVO. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Age Correlates with Response to Anti-PD1, Reflecting Age-Related Differences in Intratumoral Effector and Regulatory T-Cell Populations

Author

Kugel, Curtis H, Douglass, Stephen M, Webster, Marie R, Kaur, Amanpreet, Liu, Qin, Yin, Xiangfan, Weiss, Sarah A, Darvishian, Farbod, Al-Rohil, Rami N, Ndoye, Abibatou, Behera, Reeti, Alicea, Gretchen M, Ecker, Brett L, Fane, Mitchell, Allegrezza, Michael J, Svoronos, Nikolaos, Kumar, Vinit, Wang, Daniel Y, Somasundaram, Rajasekharan, Hu-Lieskovan, Siwen, Ozgun, Alpaslan, Herlyn, Meenhard, Conejo-Garcia, Jose R, Gabrilovich, Dmitry, Stone, Erica L, Nowicki, Theodore S, Sosman, Jeffrey, Rai, Rajat, Carlino, Matteo S, Long, Georgina V, Marais, Richard, Ribas, Antoni, Eroglu, Zeynep, Davies, Michael A, Schilling, Bastian, Schadendorf, Dirk, Xu, Wei, Amaravadi, Ravi K, Menzies, Alexander M, McQuade, Jennifer L, Johnson, Douglas B, Osman, Iman, and Weeraratna, Ashani T

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Cancer, Vaccine Related, Immunization, Aging, 2.1 Biological and endogenous factors, Aetiology, Age Factors, Animals, Antineoplastic Agents, Immunological, Biomarkers, Tumor, CD8-Positive T-Lymphocytes, Cell Line, Tumor, Disease Models, Animal, Female, Humans, Immunomodulation, Melanoma, Mice, Mice, Transgenic, Neoplasms, Programmed Cell Death 1 Receptor, T-Lymphocytes, Regulatory, Tumor Microenvironment, Xenograft Model Antitumor Assays, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

Purpose: We have shown that the aged microenvironment increases melanoma metastasis, and decreases response to targeted therapy, and here we queried response to anti-PD1.Experimental Design: We analyzed the relationship between age, response to anti-PD1, and prior therapy in 538 patients. We used mouse models of melanoma, to analyze the intratumoral immune microenvironment in young versus aged mice and confirmed our findings in human melanoma biopsies.Results: Patients over the age of 60 responded more efficiently to anti-PD-1, and likelihood of response to anti-PD-1 increased with age, even when we controlled for prior MAPKi therapy. Placing genetically identical tumors in aged mice (52 weeks) significantly increased their response to anti-PD1 as compared with the same tumors in young mice (8 weeks). These data suggest that this increased response in aged patients occurs even in the absence of a more complex mutational landscape. Next, we found that young mice had a significantly higher population of regulatory T cells (Tregs), skewing the CD8+:Treg ratio. FOXP3 staining of human melanoma biopsies revealed similar increases in Tregs in young patients. Depletion of Tregs using anti-CD25 increased the response to anti-PD1 in young mice.Conclusions: While there are obvious limitations to our study, including our inability to conduct a meta-analysis due to a lack of available data, and our inability to control for mutational burden, there is a remarkable consistency in these data from over 500 patients across 8 different institutes worldwide. These results stress the importance of considering age as a factor for immunotherapy response. Clin Cancer Res; 24(21); 5347-56. ©2018 AACR See related commentary by Pawelec, p. 5193.

Published
2018

27. Loss of p14 diminishes immunogenicity in melanoma via non‐canonical Wnt signaling by reducing the peptide surface density

Author

Wohlfarth, Jonas, primary, Kosnopfel, Corinna, additional, Faber, Dominic, additional, Berthold, Marion, additional, Siedel, Claudia, additional, Bernhardt, Melissa, additional, Schlosser, Andreas, additional, Aprati, Tyler, additional, Liu, David, additional, Schrama, David, additional, Houben, Roland, additional, Schadendorf, Dirk, additional, Goebeler, Matthias, additional, Meierjohann, Svenja, additional, and Schilling, Bastian, additional

Published
2024
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28. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Author

Haggenmüller, Sarah, Maron, Roman C., Hekler, Achim, Utikal, Jochen S., Barata, Catarina, Barnhill, Raymond L., Beltraminelli, Helmut, Berking, Carola, Betz-Stablein, Brigid, Blum, Andreas, Braun, Stephan A., Carr, Richard, Combalia, Marc, Fernandez-Figueras, Maria-Teresa, Ferrara, Gerardo, Fraitag, Sylvie, French, Lars E., Gellrich, Frank F., Ghoreschi, Kamran, Goebeler, Matthias, Guitera, Pascale, Haenssle, Holger A., Haferkamp, Sebastian, Heinzerling, Lucie, Heppt, Markus V., Hilke, Franz J., Hobelsberger, Sarah, Krahl, Dieter, Kutzner, Heinz, Lallas, Aimilios, Liopyris, Konstantinos, Llamas-Velasco, Mar, Malvehy, Josep, Meier, Friedegund, Müller, Cornelia S.L., Navarini, Alexander A., Navarrete-Dechent, Cristián, Perasole, Antonio, Poch, Gabriela, Podlipnik, Sebastian, Requena, Luis, Rotemberg, Veronica M., Saggini, Andrea, Sangueza, Omar P., Santonja, Carlos, Schadendorf, Dirk, Schilling, Bastian, Schlaak, Max, Schlager, Justin G., Sergon, Mildred, Sondermann, Wiebke, Soyer, H. Peter, Starz, Hans, Stolz, Wilhelm, Vale, Esmeralda, Weyers, Wolfgang, Zink, Alexander, Krieghoff-Henning, Eva, Kather, Jakob N., von Kalle, Christof, Lipka, Daniel B., Fröhling, Stefan, Hauschild, Axel, Kittler, Harald, and Brinker, Titus J.

Published
2021
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29. A benchmark for neural network robustness in skin cancer classification

Author

Maron, Roman C., Schlager, Justin G., Haggenmüller, Sarah, von Kalle, Christof, Utikal, Jochen S., Meier, Friedegund, Gellrich, Frank F., Hobelsberger, Sarah, Hauschild, Axel, French, Lars, Heinzerling, Lucie, Schlaak, Max, Ghoreschi, Kamran, Hilke, Franz J., Poch, Gabriela, Heppt, Markus V., Berking, Carola, Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Goebeler, Matthias, Krieghoff-Henning, Eva, Hekler, Achim, Fröhling, Stefan, Lipka, Daniel B., Kather, Jakob N., and Brinker, Titus J.

Published
2021
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30. Clinical characteristics and therapy response in unresectable melanoma patients stage IIIB-IIID with in-transit and satellite metastases

Author

Zaremba, Anne, Philip, Manuel, Hassel, Jessica C., Glutsch, Valerie, Fiocco, Zeno, Loquai, Carmen, Rafei-Shamsabadi, David, Gutzmer, Ralf, Utikal, Jochen, Haferkamp, Sebastian, Reinhardt, Lydia, Kähler, Katharina C., Weishaupt, Carsten, Moreira, Alvaro, Thoms, Kai-Martin, Wilhelm, Tabea, Pföhler, Claudia, Roesch, Alexander, Ugurel, Selma, Zimmer, Lisa, Stadtler, Nadine, Sucker, Antje, Kiecker, Felix, Heinzerling, Lucie, Meier, Friedegund, Meiss, Frank, Schlaak, Max, Schilling, Bastian, Horn, Susanne, Schadendorf, Dirk, and Livingstone, Elisabeth

Published
2021
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31. Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification

Author

Höhn, Julia, Krieghoff-Henning, Eva, Jutzi, Tanja B., von Kalle, Christof, Utikal, Jochen S., Meier, Friedegund, Gellrich, Frank F., Hobelsberger, Sarah, Hauschild, Axel, Schlager, Justin G., French, Lars, Heinzerling, Lucie, Schlaak, Max, Ghoreschi, Kamran, Hilke, Franz J., Poch, Gabriela, Kutzner, Heinz, Heppt, Markus V., Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Goebeler, Matthias, Hekler, Achim, Fröhling, Stefan, Lipka, Daniel B., Kather, Jakob N., Krahl, Dieter, Ferrara, Gerardo, Haggenmüller, Sarah, and Brinker, Titus J.

Published
2021
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33. Outcome of melanoma patients with elevated LDH treated with first-line targeted therapy or PD-1-based immune checkpoint inhibition

Author

Knispel, Sarah, Gassenmaier, Maximilian, Menzies, Alexander M., Loquai, Carmen, Johnson, Douglas B., Franklin, Cindy, Gutzmer, Ralf, Hassel, Jessica C., Weishaupt, Carsten, Eigentler, Thomas, Schilling, Bastian, Schummer, Patrick, Sirokay, Judith, Kiecker, Felix, Owen, Carina N., Fleischer, Maria I., Cann, Christopher, Kähler, Katharina C., Mohr, Peter, Bluhm, Leonie, Niebel, Dennis, Thoms, Kai-Martin, Goldinger, Simone M., Reinhardt, Lydia, Meier, Friedegund, Berking, Carola, Reinhard, Raphael, Susok, Laura, Ascierto, Paolo A., Drexler, Konstantin, Pföhler, Claudia, Tietze, Julia, Heinzerling, Lucie, Livingstone, Elisabeth, Ugurel, Selma, Long, Georgina V., Stang, Andreas, Schadendorf, Dirk, and Zimmer, Lisa

Published
2021
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34. Robustness of convolutional neural networks in recognition of pigmented skin lesions

Author

Maron, Roman C., Haggenmüller, Sarah, von Kalle, Christof, Utikal, Jochen S., Meier, Friedegund, Gellrich, Frank F., Hauschild, Axel, French, Lars E., Schlaak, Max, Ghoreschi, Kamran, Kutzner, Heinz, Heppt, Markus V., Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Hekler, Achim, Krieghoff-Henning, Eva, Kather, Jakob N., Fröhling, Stefan, Lipka, Daniel B., and Brinker, Titus J.

Published
2021
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36. Exploring the Thoughts, Needs and Fears of Chemotherapy Patients—An Analysis Based on Google Search Behavior.

Author

Özistanbullu, Deniz, Weber, Ronja, Schröder, Maria, Kippenberger, Stefan, Kleemann, Johannes, Stege, Henner, Kaufmann, Roland, Schilling, Bastian, Grabbe, Stephan, and Wilhelm, Raphael

Subjects
FEAR, HEALTH services accessibility, SKIN diseases, IMMUNOTHERAPY, BREAST tumors, HEALTH, CANCER patients, INFORMATION resources, TREATMENT effectiveness, DESCRIPTIVE statistics, CANCER chemotherapy, ADJUVANT chemotherapy, PATIENT-centered care, SEARCH engines, COMBINED modality therapy, COGNITION disorders, MEDICAL needs assessment, PUBLIC health, DATA analysis software, INFORMATION-seeking behavior, THOUGHT & thinking
Abstract

Chemotherapy poses both physical and psychological challenges for patients, prompting many to seek answers independently through online resources. This study investigates German Google search behavior regarding chemotherapy-related terms using Google AdWords data from September 2018 to September 2022 to gain insights into patient concerns and needs. A total of 1461 search terms associated with "chemotherapy" were identified, representing 1,749,312 to 28,958,400 search queries. These terms were categorized into four groups based on frequency and analyzed. Queries related to "adjuvant" and "neoadjuvant" chemotherapy, as well as "immunotherapy", suggest potential confusion among patients. Breast cancer emerged as the most searched tumor type, with hair loss, its management, and dermatological issues being the most searched side effects. These findings underscore the role of search engines such as Google in facilitating access to healthcare information and provide valuable insights into patient thoughts and needs. Healthcare providers can leverage this information to deliver patient-centric care and optimize treatment outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Conceptual framework for precision cancer medicine in Germany: Consensus statement of the Deutsche Krebshilfe working group ‘Molecular Diagnostics and Therapy’

Author

Bargou (Würzburg), Ralf, Bläker (Leipzig), Hendrik, Börries (Freiburg), Melanie, Brandts (Frankfurt), Christian, von Bubnoff (Lübeck), Nikolas, Demes (Frankfurt), Melanie, Desuki (Mainz), Alexander, Döhner (Ulm), Hartmut, Duyster (Freiburg), Justus, Gaisa (Aachen), Nadine, Heine (Bonn), Annkristin, Heining (Dresden), Christoph, Horak (Heidelberg), Peter, Jelas (Berlin), Ivan, Jost (München), Philipp J., Jung (München), Andreas, Kirchner (München), Thomas, Klauschen (Berlin), Frederick, Kreutzfeldt (Heidelberg), Simon, Kumbrink (München), Jörg, Kunzmann (Würzburg), Volker, Lassmann (Freiburg), Silke, Metzeler (München), Klaus, Möller (Ulm), Peter, Ortiz-Brüchle (Aachen), Nadina, Paret (Mainz), Claudia, Pelusi (Bonn), Natalie, Peters (Freiburg), Christoph, Pfarr (München), Nicole, Richter (Dresden), Daniela, Riedmann (München), Kristina, Rieke (Berlin), Damian, Ritzel (Mainz), Christoph, Schadendorf (Essen), Dirk, Schildhaus (Essen), Hans-Ulrich, Schorle (Bonn), Hubert, Seufferlein (Ulm), Thomas, Simon (Hamburg), Ronald, Stenzinger (Heidelberg), Albrecht, Tabatabai (Tübingen), Ghazaleh, Velthaus (Hamburg), Janna-Lisa, Werner (Freiburg), Martin, Wild (Frankfurt), Peter J., Wolf (Köln), Jürgen, Westphalen, C. Benedikt, Bokemeyer, Carsten, Büttner, Reinhard, Fröhling, Stefan, Gaidzik, Verena I., Glimm, Hanno, Hacker, Ulrich T., Heinemann, Volker, Illert, Anna L., Keilholz, Ulrich, Kindler, Thomas, Kirschner, Martin, Schilling, Bastian, Siveke, Jens T., Schroeder, Thomas, Tischler, Verena, Wagner, Sebastian, Weichert, Wilko, Zips, Daniel, and Loges, Sonja

Published
2020
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38. Adjuvant nivolumab plus ipilimumab or nivolumab monotherapy versus placebo in patients with resected stage IV melanoma with no evidence of disease (IMMUNED): a randomised, double-blind, placebo-controlled, phase 2 trial

Author

Berking, Carola, Herbst, Rudolf A, Martens, Uwe M, Sell, Sabine, Stadler, Rudolf, Terheyden, Patrick, Utikal, Jochen, Zimmer, Lisa, Livingstone, Elisabeth, Hassel, Jessica C, Fluck, Michael, Eigentler, Thomas, Loquai, Carmen, Haferkamp, Sebastian, Gutzmer, Ralf, Meier, Friedegund, Mohr, Peter, Hauschild, Axel, Schilling, Bastian, Menzer, Christian, Kieker, Felix, Dippel, Edgar, Rösch, Alexander, Simon, Jan-Christoph, Conrad, Beate, Körner, Silvia, Windemuth-Kieselbach, Christine, Schwarz, Leonora, Garbe, Claus, Becker, Jürgen C, and Schadendorf, Dirk

Published
2020
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39. Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics

Author

Haggenmüller, Sarah, primary, Schmitt, Max, additional, Krieghoff-Henning, Eva, additional, Hekler, Achim, additional, Maron, Roman C., additional, Wies, Christoph, additional, Utikal, Jochen S., additional, Meier, Friedegund, additional, Hobelsberger, Sarah, additional, Gellrich, Frank F., additional, Sergon, Mildred, additional, Hauschild, Axel, additional, French, Lars E., additional, Heinzerling, Lucie, additional, Schlager, Justin G., additional, Ghoreschi, Kamran, additional, Schlaak, Max, additional, Hilke, Franz J., additional, Poch, Gabriela, additional, Korsing, Sören, additional, Berking, Carola, additional, Heppt, Markus V., additional, Erdmann, Michael, additional, Haferkamp, Sebastian, additional, Drexler, Konstantin, additional, Schadendorf, Dirk, additional, Sondermann, Wiebke, additional, Goebeler, Matthias, additional, Schilling, Bastian, additional, Kather, Jakob N., additional, Fröhling, Stefan, additional, and Brinker, Titus J., additional

Published
2024
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40. sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance

Author

Kaur, Amanpreet, Webster, Marie R, Marchbank, Katie, Behera, Reeti, Ndoye, Abibatou, Kugel, Curtis H, Dang, Vanessa M, Appleton, Jessica, O’Connell, Michael P, Cheng, Phil, Valiga, Alexander A, Morissette, Rachel, McDonnell, Nazli B, Ferrucci, Luigi, Kossenkov, Andrew V, Meeth, Katrina, Tang, Hsin-Yao, Yin, Xiangfan, Wood, William H, Lehrmann, Elin, Becker, Kevin G, Flaherty, Keith T, Frederick, Dennie T, Wargo, Jennifer A, Cooper, Zachary A, Tetzlaff, Michael T, Hudgens, Courtney, Aird, Katherine M, Zhang, Rugang, Xu, Xiaowei, Liu, Qin, Bartlett, Edmund, Karakousis, Giorgos, Eroglu, Zeynep, Lo, Roger S, Chan, Matthew, Menzies, Alexander M, Long, Georgina V, Johnson, Douglas B, Sosman, Jeffrey, Schilling, Bastian, Schadendorf, Dirk, Speicher, David W, Bosenberg, Marcus, Ribas, Antoni, and Weeraratna, Ashani T

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Adult, Aging, Animals, Cell Line, Tumor, Culture Media, Conditioned, DNA Damage, DNA-(Apurinic or Apyrimidinic Site) Lyase, Disease Progression, Drug Resistance, Neoplasm, Fibroblasts, Humans, Indoles, Male, Melanoma, Membrane Proteins, Mice, Microphthalmia-Associated Transcription Factor, Middle Aged, Molecular Targeted Therapy, Neoplasm Metastasis, Neovascularization, Pathologic, Oxidative Stress, Phenotype, Reactive Oxygen Species, Sulfonamides, Tumor Microenvironment, Vemurafenib, Wnt Signaling Pathway, Wnt1 Protein, beta Catenin, General Science & Technology
Abstract

Cancer is a disease of ageing. Clinically, aged cancer patients tend to have a poorer prognosis than young. This may be due to accumulated cellular damage, decreases in adaptive immunity, and chronic inflammation. However, the effects of the aged microenvironment on tumour progression have been largely unexplored. Since dermal fibroblasts can have profound impacts on melanoma progression, we examined whether age-related changes in dermal fibroblasts could drive melanoma metastasis and response to targeted therapy. Here we find that aged fibroblasts secrete a Wnt antagonist, sFRP2, which activates a multi-step signalling cascade in melanoma cells that results in a decrease in β-catenin and microphthalmia-associated transcription factor (MITF), and ultimately the loss of a key redox effector, APE1. Loss of APE1 attenuates the response of melanoma cells to DNA damage induced by reactive oxygen species, rendering the cells more resistant to targeted therapy (vemurafenib). Age-related increases in sFRP2 also augment both angiogenesis and metastasis of melanoma cells. These data provide an integrated view of how fibroblasts in the aged microenvironment contribute to tumour progression, offering new possibilities for the design of therapy for the elderly.

Published
2016

42. The transcription factor NRF2 enhances melanoma malignancy by blocking differentiation and inducing COX2 expression

Author

Jessen, Christina, Kreß, Julia K. C., Baluapuri, Apoorva, Hufnagel, Anita, Schmitz, Werner, Kneitz, Susanne, Roth, Sabine, Marquardt, André, Appenzeller, Silke, Ade, Carsten P., Glutsch, Valerie, Wobser, Marion, Friedmann-Angeli, José Pedro, Mosteo, Laura, Goding, Colin R., Schilling, Bastian, Geissinger, Eva, Wolf, Elmar, and Meierjohann, Svenja

Published
2020
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45. Future perspectives in melanoma research: meeting report from the “Melanoma Bridge”: Napoli, December 3rd–6th 2014

Author

Ascierto, Paolo A, Atkins, Michael, Bifulco, Carlo, Botti, Gerardo, Cochran, Alistair, Davies, Michael, Demaria, Sandra, Dummer, Reinhard, Ferrone, Soldano, Formenti, Silvia, Gajewski, Thomas F, Garbe, Claus, Khleif, Samir, Kiessling, Rolf, Lo, Roger, Lorigan, Paul, Arthur, Grant Mc, Masucci, Giuseppe, Melero, Ignacio, Mihm, Martin, Palmieri, Giuseppe, Parmiani, Giorgio, Puzanov, Igor, Romero, Pedro, Schilling, Bastian, Seliger, Barbara, Stroncek, David, Taube, Janis, Tomei, Sara, Zarour, Hassane M, Testori, Alessandro, Wang, Ena, Galon, Jérôme, Ciliberto, Gennaro, Mozzillo, Nicola, Marincola, Francesco M, and Thurin, Magdalena

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Vaccine Related, Immunization, Clinical Research, Cancer, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Good Health and Well Being, Biomarkers, Tumor, Humans, Immunotherapy, Italy, Melanoma, Tumor Microenvironment, Medical and Health Sciences, Biomedical and clinical sciences, Health sciences
Abstract

The fourth "Melanoma Bridge Meeting" took place in Naples, December 3-6th, 2014. The four topics discussed at this meeting were: Molecular and Immunological Advances, Combination Therapies, News in Immunotherapy, and Tumor Microenvironment and Biomarkers. Until recently systemic therapy for metastatic melanoma patients was ineffective, but recent advances in tumor biology and immunology have led to the development of new targeted and immunotherapeutic agents that prolong progression-free survival (PFS) and overall survival (OS). New therapies, such as mitogen-activated protein kinase (MAPK) pathway inhibitors as well as other signaling pathway inhibitors, are being tested in patients with metastatic melanoma either as monotherapy or in combination, and all have yielded promising results. These include inhibitors of receptor tyrosine kinases (BRAF, MEK, and VEGFR), the phosphatidylinositol 3 kinase (PI3K) pathway [PI3K, AKT, mammalian target of rapamycin (mTOR)], activators of apoptotic pathway, and the cell cycle inhibitors (CDK4/6). Various locoregional interventions including radiotherapy and surgery are still valid approaches in treatment of advanced melanoma that can be integrated with novel therapies. Intrinsic, adaptive and acquired resistance occur with targeted therapy such as BRAF inhibitors, where most responses are short-lived. Given that the reactivation of the MAPK pathway through several distinct mechanisms is responsible for the majority of acquired resistance, it is logical to combine BRAF inhibitors with inhibitors of targets downstream in the MAPK pathway. For example, combination of BRAF/MEK inhibitors (e.g., dabrafenib/trametinib) have been demonstrated to improve survival compared to monotherapy. Application of novel technologies such sequencing have proven useful as a tool for identification of MAPK pathway-alternative resistance mechanism and designing other combinatorial therapies such as those between BRAF and AKT inhibitors. Improved survival rates have also been observed with immune-targeted therapy for patients with metastatic melanoma. Immune-modulating antibodies came to the forefront with anti-CTLA-4, programmed cell death-1 (PD-1) and PD-1 ligand 1 (PD-L1) pathway blocking antibodies that result in durable responses in a subset of melanoma patients. Agents targeting other immune inhibitory (e.g., Tim-3) or immune stimulating (e.g., CD137) receptors and other approaches such as adoptive cell transfer demonstrate clinical benefit in patients with melanoma as well. These agents are being studied in combination with targeted therapies in attempt to produce longer-term responses than those more typically seen with targeted therapy. Other combinations with cytotoxic chemotherapy and inhibitors of angiogenesis are changing the evolving landscape of therapeutic options and are being evaluated to prevent or delay resistance and to further improve survival rates for this patient population. This meeting's specific focus was on advances in combination of targeted therapy and immunotherapy. Both combination targeted therapy approaches and different immunotherapies were discussed. Similarly to the previous meetings, the importance of biomarkers for clinical application as markers for diagnosis, prognosis and prediction of treatment response was an integral part of the meeting. The overall emphasis on biomarkers supports novel concepts toward integrating biomarkers into contemporary clinical management of patients with melanoma across the entire spectrum of disease stage. Translation of the knowledge gained from the biology of tumor microenvironment across different tumors represents a bridge to impact on prognosis and response to therapy in melanoma.

Published
2015

46. Superior skin cancer classification by the combination of human and artificial intelligence

Author

Schmitt, Laurenz, Peitsch, Wiebke K., Hoffmann, Friederike, Becker, Jürgen C., Drusio, Christina, Jansen, Philipp, Klode, Joachim, Lodde, Georg, Sammet, Stefanie, Schadendorf, Dirk, Sondermann, Wiebke, Ugurel, Selma, Zader, Jeannine, Enk, Alexander, Salzmann, Martin, Schäfer, Sarah, Schäkel, Knut, Winkler, Julia, Wölbing, Priscilla, Asper, Hiba, Bohne, Ann-Sophie, Brown, Victoria, Burba, Bianca, Deffaa, Sophia, Dietrich, Cecilia, Dietrich, Matthias, Drerup, Katharina Antonia, Egberts, Friederike, Erkens, Anna-Sophie, Greven, Salim, Harde, Viola, Jost, Marion, Kaeding, Merit, Kosova, Katharina, Lischner, Stephan, Maagk, Maria, Messinger, Anna Laetitia, Metzner, Malte, Motamedi, Rogina, Rosenthal, Ann-Christine, Seidl, Ulrich, Stemmermann, Jana, Torz, Kaspar, Velez, Juliana Giraldo, Haiduk, Jennifer, Alter, Mareike, Bär, Claudia, Bergenthal, Paul, Gerlach, Anne, Holtorf, Christian, Karoglan, Ante, Kindermann, Sophie, Kraas, Luise, Felcht, Moritz, Gaiser, Maria R., Klemke, Claus-Detlev, Kurzen, Hjalmar, Leibing, Thomas, Müller, Verena, Reinhard, Raphael R., Utikal, Jochen, Winter, Franziska, Berking, Carola, Eicher, Laurie, Hartmann, Daniela, Heppt, Markus, Kilian, Katharina, Krammer, Sebastian, Lill, Diana, Niesert, Anne-Charlotte, Oppel, Eva, Sattler, Elke, Senner, Sonja, Wallmichrath, Jens, Wolff, Hans, Gesierich, Anja, Giner, Tina, Glutsch, Valerie, Kerstan, Andreas, Presser, Dagmar, Schrüfer, Philipp, Schummer, Patrick, Stolze, Ina, Weber, Judith, Drexler, Konstantin, Haferkamp, Sebastian, Mickler, Marion, Stauner, Camila Toledo, Thiem, Alexander, Hekler, Achim, Utikal, Jochen S., Enk, Alexander H., Hauschild, Axel, Weichenthal, Michael, Maron, Roman C., Schilling, Bastian, Holland-Letz, Tim, Izar, Benjamin, von Kalle, Christof, Fröhling, Stefan, and Brinker, Titus J.

Published
2019
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48. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Author

Schmitt, Laurenz, Peitsch, Wiebke K., Hoffmann, Friederike, Becker, Jürgen C., Drusio, Christina, Jansen, Philipp, Klode, Joachim, Lodde, Georg, Sammet, Stefanie, Schadendorf, Dirk, Sondermann, Wiebke, Ugurel, Selma, Zader, Jeannine, Enk, Alexander, Salzmann, Martin, Schäfer, Sarah, Schäkel, Knut, Winkler, Julia, Wölbing, Priscilla, Asper, Hiba, Bohne, Ann-Sophie, Brown, Victoria, Burba, Bianca, Deffaa, Sophia, Dietrich, Cecilia, Dietrich, Matthias, Drerup, Katharina Antonia, Egberts, Friederike, Erkens, Anna-Sophie, Greven, Salim, Harde, Viola, Jost, Marion, Kaeding, Merit, Kosova, Katharina, Lischner, Stephan, Maagk, Maria, Messinger, Anna Laetitia, Metzner, Malte, Motamedi, Rogina, Rosenthal, Ann-Christine, Seidl, Ulrich, Stemmermann, Jana, Torz, Kaspar, Velez, Juliana Giraldo, Haiduk, Jennifer, Alter, Mareike, Bär, Claudia, Bergenthal, Paul, Gerlach, Anne, Holtorf, Christian, Karoglan, Ante, Kindermann, Sophie, Kraas, Luise, Felcht, Moritz, Gaiser, Maria R., Klemke, Claus-Detlev, Kurzen, Hjalmar, Leibing, Thomas, Müller, Verena, Reinhard, Raphael R., Utikal, Jochen, Winter, Franziska, Berking, Carola, Eicher, Laurie, Hartmann, Daniela, Heppt, Markus, Kilian, Katharina, Krammer, Sebastian, Lill, Diana, Niesert, Anne-Charlotte, Oppel, Eva, Sattler, Elke, Senner, Sonja, Wallmichrath, Jens, Wolff, Hans, Giner, Tina, Glutsch, Valerie, Kerstan, Andreas, Presser, Dagmar, Schrüfer, Philipp, Schummer, Patrick, Stolze, Ina, Weber, Judith, Drexler, Konstantin, Haferkamp, Sebastian, Mickler, Marion, Stauner, Camila Toledo, Thiem, Alexander, Maron, Roman C., Weichenthal, Michael, Utikal, Jochen S., Hekler, Achim, Hauschild, Axel, Enk, Alexander H., Holland-Letz, Tim, Schilling, Bastian, von Kalle, Christof, Fröhling, Stefan, Gesierich, Anja, Kähler, Katharina C., Wehkamp, Ulrike, and Brinker, Titus J.

Published
2019
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