@phdthesis{Rehlinghaus2024,
  author    = {Rehlinghaus, Christine},
  title     = {Retrospektive Evaluation der intraven{\"o}sen Dexamethason- bzw. Methylprednisolon-Pulstherapie bei ausgepr{\"a}gter Alopecia areata},
  doi       = {10.25972/OPUS-36071},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-360711},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {Hintergrund: Bei der Entscheidung f{\"u}r eine intraven{\"o}se Kortikosteroid-Pulstherapie bei schweren Formen der AA ist die Abw{\"a}gung von Therapieaufwand, Nebenwirkungen und Risiken einerseits und der Erfolgsaussicht andererseits von zentraler Bedeutung. Ziel: Ziel dieser retrospektiven Analyse war es daher, die Wirksamkeit und Sicherheit der intraven{\"o}sen Kortikosteroid-Pulstherapie bei Patient:innen mit ausgepr{\"a}gter AA klinikintern als qualit{\"a}tssichernde Maßnahme zu untersuchen, prognostisch bedeutsame Faktoren f{\"u}r den Therapieeffekt zu ermitteln und hierdurch die beste Indikation herauszuarbeiten. Methode: 126 Patient:innen (13 Kinder und Jugendliche) erhielten Dexamethason 100 mg (122 Patienten) oder Methylprednisolon 20-30 mg/kg/KG (max. 1000 mg, 4 Patienten) an drei aufeinanderfolgenden Tagen f{\"u}r ein bis drei Zyklen. Ergebnisse: Patienten mit einer AA partialis bzw. diffusa zeigten im Hinblick auf ein vollst{\"a}ndiges oder kosmetisch akzeptables Wiederwachstum die besten Ansprechraten (44,3\%, n=43). Unter den Ophiasis-Patienten und den Patienten mit AA totalis/universalis sprach nur etwa ein Viertel auf die Therapie an (Ophiasis 23,8\%, n=5; AA totalis/universalis: 25\%, n=2). Schwerwiegende unerw{\"u}nschte Nebenwirkungen wurden nicht beobachtet. Schlussfolgerung: In der vorliegenden Untersuchung ließen sich eine l{\"a}ngere Bestandsdauer der Erkrankung und Erkrankungsepisode ({\"u}ber 6 Monate), ein schwerer Auspr{\"a}gungsgrad (Ophiasis, AA totalis/universalis) und krankheitstypische Nagelver{\"a}nderungen als wichtige ung{\"u}nstige prognostische Faktoren nachweisen. Dagegen wirkten sich die untersuchten Kriterien Alter, Geschlecht, atopisches Ekzem und andere Erkrankungen des atopischen Formenkreises, Schilddr{\"u}sen- und Autoimmunerkrankungen in der Eigenanamnese sowie AA in der Familienanamnese nicht negativ auf den Behandlungserfolg aus. Patienten mit AA partialis und einer Bestandsdauer der AA von maximal 6 Monaten haben die besten Erfolgsaussichten.},
  subject      = {Alopecia areata},
  language  = {de}
}
@article{KervarrecSamimiGuyetantetal.2019,
  author    = {Kervarrec, Thibault and Samimi, Mahtab and Guy{\´e}tant, Serge and Sarma, Bhavishya and Ch{\´e}ret, J{\´e}r{\´e}my and Blanchard, Emmanuelle and Berthon, Patricia and Schrama, David and Houben, Roland and Touz{\´e}, Antoine},
  title     = {Histogenesis of Merkel Cell Carcinoma: A Comprehensive Review},
  series = {Frontiers in Oncology},
  volume    = {9},
  journal   = {Frontiers in Oncology},
  doi       = {10.3389/fonc.2019.00451},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-325733},
  year      = {2019},
  abstract  = {Merkel cell carcinoma (MCC) is a primary neuroendocrine carcinoma of the skin. This neoplasia features aggressive behavior, resulting in a 5-year overall survival rate of 40\%. In 2008, Feng et al. identified Merkel cell polyomavirus (MCPyV) integration into the host genome as the main event leading to MCC oncogenesis. However, despite identification of this crucial viral oncogenic trigger, the nature of the cell in which MCC oncogenesis occurs is actually unknown. In fact, several hypotheses have been proposed. Despite the large similarity in phenotype features between MCC tumor cells and physiological Merkel cells (MCs), a specialized subpopulation of the epidermis acting as mechanoreceptor of the skin, several points argue against the hypothesis that MCC derives directly from MCs. Alternatively, MCPyV integration could occur in another cell type and induce acquisition of an MC-like phenotype. Accordingly, an epithelial as well as a fibroblastic or B-cell origin of MCC has been proposed mainly based on phenotype similarities shared by MCC and these potential ancestries. The aim of this present review is to provide a comprehensive review of the current knowledge of the histogenesis of MCC.},
  language  = {en}
}
@article{BrinkerHeklerHauschildetal.2019,
  author    = {Brinker, Titus J. and Hekler, Achim and Hauschild, Axel and Berking, Carola and Schilling, Bastian and Enk, Alexander H. and Haferkamp, Sebastian and Karoglan, Ante and von Kalle, Christof and Weichenthal, Michael and Sattler, Elke and Schadendorf, Dirk and Gaiser, Maria R. and Klode, Joachim and Utikal, Jochen S.},
  title     = {Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark},
  series = {European Journal of Cancer},
  volume    = {111},
  journal   = {European Journal of Cancer},
  doi       = {10.1016/j.ejca.2018.12.016},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220569},
  pages     = {30-37},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{BrinkerHeklerEnketal.2019,
  author    = {Brinker, Titus J. and Hekler, Achim and Enk, Alexander H. and Berking, Carola and Haferkamp, Sebastian and Hauschild, Axel and Weichenthal, Michael and Klode, Joachim and Schadendorf, Dirk and Holland-Letz, Tim and von Kalle, Christof and Fr{\"o}hling, Stefan and Schilling, Bastian and Utikal, Jochen S.},
  title     = {Deep neural networks are superior to dermatologists in melanoma image classification},
  series = {European Journal of Cancer},
  volume    = {119},
  journal   = {European Journal of Cancer},
  doi       = {10.1016/j.ejca.2019.05.023},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220539},
  pages     = {11-17},
  year      = {2019},
  abstract  = {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).},
  language  = {en}
}
@article{ScholzCosgareaSuesskindetal.2018,
  author    = {Scholz, S. L. and Cosgarea, I. and S{\"u}ßkind, D. and Murali, R. and M{\"o}ller, I. and Reis, H. and Leonardelli, S. and Schilling, B. and Schimming, T. and Hadaschik, E. and Franklin, C. and Paschen, A. and Sucker, A. and Steuhl, K. P. and Schadendorf, D. and Westekemper, H. and Griewank, K. G.},
  title     = {NF1 mutations in conjunctival melanoma},
  series = {British Journal of Cancer},
  volume    = {118},
  journal   = {British Journal of Cancer},
  doi       = {10.1038/s41416-018-0046-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-233329},
  pages     = {1243-1247},
  year      = {2018},
  abstract  = {Background Conjunctival melanoma is a potentially deadly eye tumour. Despite effective local therapies, tumour recurrence and metastasis remain frequent. The genetics of conjunctival melanomas remain incompletely understood. Methods A large cohort of 63 conjunctival melanomas was screened for gene mutations known to be important in other melanoma subtypes by targeted next-generation sequencing. Mutation status was correlated with patient prognosis. Results Frequent mutations in genes activating the MAP kinase pathway were identified. NF1 mutations were most frequent (n = 21, 33\%). Recurrent activating mutations were also identified in BRAF (n = 16, 25\%) and RAS genes (n = 12, 19\%; 11 NRAS and 1 KRAS). Conclusions Similar to cutaneous melanomas, conjunctival melanomas can be grouped genetically into four groups: BRAF-mutated, RAS-mutated, NF1-mutated and triple wild-type melanomas. This genetic classification may be useful for assessment of therapeutic options for patients with metastatic conjunctival melanoma},
  language  = {en}
}
@article{SondermannUtikalEnketal.2019,
  author    = {Sondermann, Wiebke and Utikal, Jochen Sven and Enk, Alexander H. and Schadendorf, Dirk and Klode, Joachim and Hauschild, Axel and Weichenthal, Michael and French, Lars E. and Berking, Carola and Schilling, Bastian and Haferkamp, Sebastian and Fr{\"o}hling, Stefan and von Kalle, Christof and Brinker, Titus J.},
  title     = {Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data},
  series = {European Journal of Cancer},
  volume    = {119},
  journal   = {European Journal of Cancer},
  doi       = {10.1016/j.ejca.2019.07.009},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-239263},
  pages     = {30-34},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{TappenbeckSchroederNiebergallRothetal.2019,
  author    = {Tappenbeck, Nils and Schr{\"o}der, Hannes M. and Niebergall-Roth, Elke and Hassinger, Fathema and Dehio, Ulf and Dieter, Kathrin and Kraft, Korinna and Kerstan, Andreas and Esterlechner, Jasmina and Frank, Natasha Y. and Scharffetter-Kochanek, Karin and Murphy, George F. and Orgill, Dennis P. and Beck, Joachim and Frank, Markus H. and Ganss, Christoph and Kluth, Mark A.},
  title     = {In vivo safety profile and biodistribution of GMP-manufactured human skin-derived ABCB5-positive mesenchymal stromal cells for use in clinical trials},
  series = {Cytotherapy},
  volume    = {21},
  journal   = {Cytotherapy},
  doi       = {10.1016/j.jcyt.2018.12.005},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-240456},
  pages     = {546-560},
  year      = {2019},
  abstract  = {Background aims Human dermal ABCB5-expressing mesenchymal stromal cells (ABCB5+ MSCs) represent a promising candidate for stem cell-based therapy of various currently uncurable diseases in several fields of regenerative medicine. We have developed and validated a method to isolate, from human skin samples, and expand ABCB5+ MSCs that meet the guideline criteria of the International Society for Cellular Therapy. We are able to process these cells into a Good Manufacturing Practice-conforming, MSC-based advanced-therapy medicinal product. Methods To support the development of ABCB5+ MSCs for potential therapeutic topical, intramuscular and intravenous administration, we have tested our product in a series of Good Laboratory Practice-compliant nonclinical in-vivo studies addressing all relevant aspects of biosafety, including potential long-term persistence and proliferation, distribution to nontarget tissues, differentiation into undesired cell types, ectopic tissue formation, tumor formation and local tissue reaction. Results (i) Subcutaneous application of 1 × 107 ABCB5+ MSCs/animal and intravenous application of 2 × 106 ABCB5+ MSCs/animal, respectively, to immunocompromised mice did not result in safety-relevant biodistribution, persistence or proliferation of the cells; (ii) three monthly subcutaneous injections of ABCB5+ MSCs at doses ranging from 1 × 105 to 1 × 107 cells/animal and three biweekly intravenous injections of 2 × 106 ABCB5+ MSCs/animal, respectively, to immunocompromised mice were nontoxic and revealed no tumorigenic potential; and (iii) intramuscular injection of 5 × 106 ABCB5+ MSCs/animal to immunocompromised mice was locally well tolerated. Discussion The present preclinical in vivo data demonstrate the local and systemic safety and tolerability of a novel advanced-therapy medicinal product based on human skin-derived ABCB5+ MSCs.},
  language  = {en}
}