Carola Berking

ORCID: 0000-0003-0229-8931
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About
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Research Areas
  • Cutaneous Melanoma Detection and Management
  • Nonmelanoma Skin Cancer Studies
  • Melanoma and MAPK Pathways
  • Cancer Immunotherapy and Biomarkers
  • Immunotherapy and Immune Responses
  • CAR-T cell therapy research
  • Cutaneous lymphoproliferative disorders research
  • Skin Protection and Aging
  • melanin and skin pigmentation
  • Ocular Oncology and Treatments
  • AI in cancer detection
  • Photodynamic Therapy Research Studies
  • Computational Drug Discovery Methods
  • Optical Coherence Tomography Applications
  • HER2/EGFR in Cancer Research
  • Cancer and Skin Lesions
  • Nail Diseases and Treatments
  • Peptidase Inhibition and Analysis
  • Protein Degradation and Inhibitors
  • Advanced Breast Cancer Therapies
  • Cell Image Analysis Techniques
  • Cancer Cells and Metastasis
  • Immune Cell Function and Interaction
  • Cancer Genomics and Diagnostics
  • Allergic Rhinitis and Sensitization

Friedrich-Alexander-Universität Erlangen-Nürnberg
2019-2025

Comprehensive Cancer Center Erlangen
2020-2025

Universitätsklinikum Erlangen
2019-2025

German Climate Computing Centre
2021-2025

Bayer (Germany)
2025

Ludwig-Maximilians-Universität München
2013-2024

LMU Klinikum
2011-2024

Zero to Three
2020-2023

Hautklinik Heidelberg
2022

München Klinik
2008-2019

Most patients with BRAF(V600)-mutant metastatic melanoma develop resistance to selective RAF kinase inhibitors. The spectrum of clinical genetic mechanisms inhibitors and options for salvage therapy are incompletely understood. We performed whole-exome sequencing on formalin-fixed, paraffin-embedded tumors from 45 who received vemurafenib or dabrafenib monotherapy. Genetic alterations in known putative inhibitor genes were observed 23 (51%). Besides previously characterized alterations, we...

10.1158/2159-8290.cd-13-0617 article EN Cancer Discovery 2013-11-22

Recently, we and others identified the microRNA miR-34a as a target of tumor suppressor gene product p53. Ectopic induces G(1) cell cycle arrest, senescence apoptosis. Here report that expression is silenced in several types cancer due to aberrant CpG methylation its promoter. 19 out 24 (79.1%) primary prostate carcinomas displayed promoter concomitant loss expression. was also detected breast (6/24; 25%), lung (7/24; 29.1%), colon (3/23; 13%), kidney (3/14; 21.4%), bladder (2/6; 33.3%)...

10.4161/cc.7.16.6533 article EN Cell Cycle 2008-08-15

Biomarkers for outcome after immune-checkpoint blockade are strongly needed as these may influence individual treatment selection or sequence. We aimed to identify baseline factors associated with overall survival (OS) pembrolizumab in melanoma patients.Serum lactate dehydrogenase (LDH), routine blood count parameters, and clinical characteristics were investigated 616 patients. Endpoints OS best response following treatment. Kaplan-Meier analysis Cox regression applied analysis.Relative...

10.1158/1078-0432.ccr-16-0127 article EN Clinical Cancer Research 2016-05-17

Background Ipilimumab, a cytotoxic T-lymphocyte antigen-4 (CTLA-4) blocking antibody, has been approved for the treatment of metastatic melanoma and induces adverse events (AE) in up to 64% patients. Treatment algorithms management common ipilimumab-induced AEs have lead reduction morbidity, e.g. due bowel perforations. However, spectrum less is expanding as ipilimumab increasingly applied. Stringent recognition will reduce drug-induced morbidity costs, thus, positively impact cost-benefit...

10.1371/journal.pone.0053745 article EN cc-by PLoS ONE 2013-01-14

The retinoic acid–inducible gene I (RIG-I) and melanoma differentiation–associated antigen 5 (MDA-5) helicases sense viral RNA in infected cells initiate antiviral responses such as the production of type IFNs. Here we have shown that RIG-I MDA-5 also a proapoptotic signaling pathway is independent In human cells, this required mitochondrial adapter Cardif (also known IPS-1) induced BH3-only proteins Puma Noxa. RIG-I– MDA-5–initiated apoptosis Noxa but was tumor suppressor p53. Triggering...

10.1172/jci37155 article EN Journal of Clinical Investigation 2009-07-20
Titus J. Brinker Achim Hekler Alexander Enk Joachim Klode Axel Hauschild and 95 more Carola Berking Bastian Schilling Sebastian Haferkamp Dirk Schadendorf Tim Holland‐Letz Jochen Utikal Christof von Kalle Wiebke Ludwig‐Peitsch Judith Sirokay Lucie Heinzerling Magarete Albrecht Katharina Baratella Lena Bischof Eleftheria Chorti Anna Dith Christina Drusio Nina Giese Emmanouil Gratsias Klaus Griewank Sandra Hallasch Zdenka Hanhart Saskia Herz Katja Hohaus Philipp Jansen Finja Jockenhöfer Theodora Kanaki Sarah Knispel Katja Leonhard Anna Martaki Liliana Matei Johanna Matull Alexandra Olischewski Maximilian Petri Jan‐Malte Placke Simon Raub Katrin Salva Swantje Schlott Elsa Sody Nadine Steingrube Ingo Stoffels Selma Ugurel Anne Zaremba Christoffer Gebhardt Nina Booken Maria Christolouka Kristina Buder‐Bakhaya Therezia Bokor‐Billmann Alexander Enk Patrick Gholam Holger Hänßle Martin Salzmann Sarah K. Schäfer Knut Schäkel Timo Schank Ann‐Sophie Bohne Sophia Deffaa Katharina Drerup Friederike Egberts Anna‐Sophie Erkens Benjamin Ewald Sandra Falkvoll Sascha Gerdes Viola Harde Axel Hauschild Marion Jost Katja Kosova Laetitia Messinger Malte Metzner Kirsten Morrison Rogina Motamedi Anja Pinczker Anne Rosenthal Natalie Scheller Thomas Schwarz Dora Stölzl Federieke Thielking Elena Tomaschewski Ulrike Wehkamp Michael Weichenthal Oliver Wiedow Claudia Bär Sophia Bender-Säbelkampf Marc Horbrügger Ante Karoglan Luise Kraas Jörg Faulhaber Cyrill Géraud Ze Guo Philipp Koch Miriam Linke Nolwenn Maurier Verena Müller Benjamin Thomas Jochen Utikal Ali Saeed M. Alamri

Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification suspicious lesions by excessive proprietary image databases and limited numbers dermatologists. For first time, performance a algorithm trained open-source images exclusively is compared to large number dermatologists covering all levels within clinical hierarchy.We used methods from enhanced deep learning train convolutional neural network (CNN) with 12,378 dermoscopic...

10.1016/j.ejca.2019.04.001 article EN cc-by-nc-nd European Journal of Cancer 2019-04-10

BackgroundMelanoma is the most dangerous type of skin cancer but curable if detected early. Recent publications demonstrated that artificial intelligence capable in classifying images benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared dermatologist classification has not been reported to date.MethodsFor this comparative study, 4204 biopsy-proven (1:1) were used for training convolutional neural network (CNN). New techniques...

10.1016/j.ejca.2019.05.023 article EN cc-by-nc-nd European Journal of Cancer 2019-08-08
Alexander Eggermont Christian U. Blank Mario Mandalá Georgina V Long Victoria Atkinson and 95 more Stéphane Dalle Andrew Haydon Andrey Meshcheryakov Adnan Khattak Matteo S. Carlino Shahneen Sandhu James Larkin Susana Puig Paolo A. Ascierto Piotr Rutkowski Dirk Schadendorf Rutger H.T. Koornstra Leonel F. Hernandez‐Aya Anna Maria Di Giacomo Alfons J.M. van den Eertwegh Jean‐Jacques Grob Ralf Gutzmer Rahima Jamal Paul Lorigan Alexander C.J. van Akkooi Clemens Krepler Nageatte Ibrahim Sandrine Marréaud Michal Kiciński Stefan Suciu Caroline Robert Alex Menzies Thierry Lesimple Michele Maio Gerald P. Linette Michael P. Brown Peter Hersey Inge Marie Svane Laurent Mortier Jacob Schachter Catherine Barrow Ragini R. Kudchadkar Xinni Song Caroline Dutriaux Pietro Quaglino Friedegund Meier Paola Queirolo Daniil Stroyakovskiy Lars Bastholt B. Guillot Claus Garbe Pablo L. Ortiz‐Romero Florent Grange Peter Mohr Alain P. Algazi Oliver Bechter Micaela Hernberg Jean‐Philippe Arnault Philippe Saïag Carmen Loquai Frank Meiß Jan‐Christoph Simon Gil Bar‐Sela Vanna Chiarion‐Sileni Bernard M. Fitzharris Mike McCrystal Phillip Parente Jean‐François Baurain P. Combemale Célèste Lebbe Axel Hauschild Naoya Yamazaki Reinhard Dummer Mohammed Milhem Marcin Dzienis John Walker L. Geoffrois M.‐T. Leccia Lutz Kretschmer Daniel Hendler Michal Lotem Andrzej Maćkiewicz Lidija Kandolf Sekulović Elaine Dunwoodie Christoph Höeller L. Machet Jessica C. Hassel Geke A.P. Hospers Maria-Jose Passos Max Levin Martin Fehr Pippa Corrie Ashita Waterston Sigrun Hallmeyer Henrik Schmidt V. Descamps J.‐P. Lacour Carola Berking Felix Kiecker Pier Francesco Ferrucci

10.1016/s1470-2045(21)00065-6 article EN The Lancet Oncology 2021-04-14
Achim Hekler Jochen Utikal Alexander Enk Axel Hauschild Michael Weichenthal and 95 more Roman C. Maron Carola Berking Sebastian Haferkamp Joachim Klode Dirk Schadendorf Bastian Schilling Tim Holland‐Letz Benjamin Izar Christof von Kalle Stefan Fröhling Titus J. Brinker Laurenz Schmitt Wiebke K. Peitsch Friederike Hoffmann Jürgen C. Becker Christina Drusio Philipp Jansen Joachim Klode Georg Lodde Stefanie Sammet Dirk Schadendorf Wiebke Sondermann Selma Ugurel Jeannine Zader Alexander Enk Martin Salzmann Sarah K. Schäfer Knut Schäkel Julia K. Winkler Priscilla Wölbing Hiba Asper Ann‐Sophie Bohne Victoria Brown Bianca Burba Sophia Deffaa Cecilia Dietrich Matthias Dietrich Katharina Drerup Friederike Egberts Anna‐Sophie Erkens Salim Greven Viola Harde Marion Jost Merit Kaeding Katharina Kosova S. Lischner Maria Maagk Anna Laetitia Messinger Malte Metzner Rogina Motamedi Ann-Christine Rosenthal Ulrich Seidl Jana Stemmermann Kaspar Torz Juliana Giraldo Velez Jennifer Haiduk Mareike Alter Claudia Bär Paul Bergenthal Anne Gerlach Christian Holtorf Ante Karoglan Sophie Kindermann Luise Kraas Moritz Felcht Maria Rita Gaiser Claus‐Detlev Klemke Hjalmar Kurzen Thomas Leibing Verena Müller Raphael Reinhard Jochen Utikal Franziska Winter Carola Berking Laurie Eicher Daniela Hartmann Markus V. Heppt Katharina Kilian Sebastian Krammer Diana Lill Anne‐Charlotte Niesert Eva Oppel Elke Sattler Sonja Senner Jens Wallmichrath Hans Wolff Anja Gesierich Tina Giner Valerie Glutsch Andreas Kerstan Dagmar Presser Philipp Schrüfer Patrick Schummer Ina Stolze Judith Weber

BackgroundIn recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these artificial intelligence were considered as opponents. However, the combination classifiers frequently yields superior results, both machine learning among humans. this study, we investigated potential benefit combining human for skin cancer classification.MethodsUsing 11,444 images, which divided into five diagnostic categories,...

10.1016/j.ejca.2019.07.019 article EN cc-by-nc-nd European Journal of Cancer 2019-09-10
Titus J. Brinker Achim Hekler Alexander Enk Joachim Klode Axel Hauschild and 95 more Carola Berking Bastian Schilling Sebastian Haferkamp Dirk Schadendorf Stefan Fröhling Jochen Utikal Christof von Kalle Wiebke Ludwig‐Peitsch Judith Sirokay Lucie Heinzerling Magarete Albrecht Katharina Baratella Lena Bischof Eleftheria Chorti Anna Dith Christina Drusio Nina Giese Emmanouil Gratsias Klaus Griewank Sandra Hallasch Zdenka Hanhart Saskia Herz Katja Hohaus Philipp Jansen Finja Jockenhöfer Theodora Kanaki Sarah Knispel Katja Leonhard Anna Martaki Liliana Matei Johanna Matull Alexandra Olischewski Maximilian Petri Jan-Malte Placke Simon Raub Katrin Salva Swantje Schlott Elsa Sody Nadine Steingrube Ingo Stoffels Selma Ugurel Wiebke Sondermann Anne Zaremba Christoffer Gebhardt Nina Booken Maria Christolouka Kristina Buder‐Bakhaya Therezia Bokor‐Billmann Alexander Enk Patrick Gholam Holger Hänßle Martin Salzmann Sarah K. Schäfer Knut Schäkel Timo Schank Ann-Sophie Bohne Sophia Deffaa Katharina Drerup Friederike Egberts Anna-Sophie Erkens Benjamin Ewald Sandra Falkvoll Sascha Gerdes Viola Harde Axel Hauschild Marion Jost Katja Kosova Laetitia Messinger Malte Metzner Kirsten Morrison Rogina Motamedi Anja Pinczker Anne Rosenthal Natalie Scheller Thomas Schwarz Dora Stölzl Federieke Thielking Elena Tomaschewski Ulrike Wehkamp Michael Weichenthal Oliver Wiedow Claudia Bär Sophia Bender-Säbelkampf Marc Horbrügger Ante Karoglan Luise Kraas Jörg Faulhaber Cyrill Géraud Ze Guo Philipp Koch Miriam Linke Nolwenn Maurier Verena Müller Benjamin Thomas Jochen Utikal

10.1016/j.ejca.2019.02.005 article EN cc-by-nc-nd European Journal of Cancer 2019-03-07

Purpose Uveal melanoma is the most common primary intraocular malignancy in adults with no effective systemic treatment option metastatic setting. Selumetinib (AZD6244, ARRY-142886) an oral, potent, and selective MEK1/2 inhibitor a short half-life, which demonstrated single-agent activity patients uveal randomized phase II trial. Methods The (AZD6244: (Hyd-Sulfate) Metastatic Melanoma (SUMIT) study was III, double-blind trial ( ClinicalTrial.gov identifier: NCT01974752) prior therapy were...

10.1200/jco.2017.74.1090 article EN Journal of Clinical Oncology 2018-03-12

BackgroundThe diagnosis of most cancers is made by a board-certified pathologist based on tissue biopsy under the microscope. Recent research reveals high discordance between individual pathologists. For melanoma, literature reports 25–26% for classifying benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance precision lung and breast cancer diagnoses. The aim this study illustrate potential deep assist human assessment histopathologic melanoma...

10.1016/j.ejca.2019.04.021 article EN cc-by-nc-nd European Journal of Cancer 2019-05-23

PURPOSE We have previously reported on the 3-year results of phase III German Dermatologic Cooperative Oncology Group trial (DeCOG; ClinicalTrials.gov identifier: NCT02434107 ) comparing distant metastasis-free survival (DMFS), recurrence-free (RFS), and overall (OS) in patients with positive sentinel lymph-node biopsy who were randomly assigned to complete lymph node dissection (CLND) or observation. Here, we report final analysis 72 months median follow up. PATIENTS AND METHODS The...

10.1200/jco.18.02306 article EN Journal of Clinical Oncology 2019-09-26

BackgroundMultiple studies have compared the performance of artificial intelligence (AI)–based models for automated skin cancer classification to human experts, thus setting cornerstone a successful translation AI-based tools into clinicopathological practice.ObjectiveThe objective study was systematically analyse current state research on reader involving melanoma and assess their potential clinical relevance by evaluating three main aspects: test set characteristics...

10.1016/j.ejca.2021.06.049 article EN cc-by-nc-nd European Journal of Cancer 2021-09-08
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