F
Frederick Klauschen
Researcher at Humboldt University of Berlin
Publications - 238
Citations - 17312
Frederick Klauschen is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 45, co-authored 170 publications receiving 12451 citations. Previous affiliations of Frederick Klauschen include Charité & National Institutes of Health.
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Journal ArticleDOI
668P Comprehensive analysis of the tumor immune microenvironment (TIM) in advanced adenoid cystic (ACC) and non-adenoid cystic salivary gland cancers (SGC)
Damian T. Rieke,Ellen Blanc,B. von der Emde,Iris Piwonski,Frederick Klauschen,Ingeborg Tinhofer-Keilholz,Andreas Mock,Peter Horak,Uma Keller,Dieter Beule,Konrad Klinghammer,Stefan Fröhling,Sebastian Ochsenreither,Ulrich Keilholz +13 more
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Intratumoral Heterogeneity of Molecular Subtypes in Muscle-invasive Bladder Cancer-An Extensive Multiregional Immunohistochemical Analysis.
Simon Schallenberg,Mihnea P. Dragomir,Philipp Anders,B. Ebner,Yannic Volz,Lennert Eismann,Severin Rodler,Jozefina Casuscelli,Alexander Buchner,Frederick Klauschen,Christian G. Stief,David Horst,Gerald Schulz +12 more
TL;DR: In this paper , the extent of intratumoral heterogeneity (ITH) might affect subtyping of individual patients in muscle-invasive bladder cancer patients undergoing radical cystectomy was evaluated.
Pd25-02 intratumoral heterogeneity of molecular subtypes in muscle-invasive bladder cancer – an extensive multi-regional immunohistochemical analysis
Simon Schallenberg,Miruna Dragomir,Paetau Anders,B. Ebner,Yannic Volz,Lennert Eismann,Severin Rodler,Jorge Casuscelli,Alexander Buchner,Frederick Klauschen,C.G. Stief,D. Horst,Gerald Schulz +12 more
TL;DR: In this paper , the role of cancer associated macrophages in BC is unclear due to lack of consistent results, coupled with tissue nonspecific transcriptomic signature, while the prognostic value of immune system biomarkers has been well explored.
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Abstract 5441: Cell cycle arrest status predicted from H&E stained images using deep learning
Christina Aigner,Brian Reichholf,Simon Schallenberg,Rosemarie Krupar,Lukas Ruff,Sharon Ruane,Maximilian Alber,Frederick Klauschen,Francesca Trapani +8 more
TL;DR: Aigner et al. as discussed by the authors developed a deep learning model that predicts cell-level nuclear p21 status on H&E-stained tissue alone, aiming to bypass the IHC-staining step and all drawbacks associated with it.
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Abstract 457: Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples
Thomas Mrowiec,Sharon Ruane,Simon Schallenberg,Gabriel Dernbach,Rumyana Todorova,Cornelius Böhm,Walter de Back,Blanca Pablos,Roman Schulte-Sasse,Ivana Trajanovska,Adelaida Creosteanu,Emil Barbuta,Marcus Otte,Christian Ihling,Hans Juergen Grote,J. Scheuenpflug,V. Matyas,Maximilian Alber,Frederick Klauschen +18 more
TL;DR: The error introduced by pathologists' morphological assessment is quantify and mitigate this error by training AI-systems without manual pathologist annotations, using labels determined directly from IHC profiles, to demonstrate the value of combining histomorphological and IHC data for improved cell annotation.