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1,432 results on '"Boor, Peter"'

1. Histopathological biomarkers for predicting the tumour accumulation of nanomedicines

Author

May, Jan-Niklas, Moss, Jennifer I., Mueller, Florian, Golombek, Susanne K., Biancacci, Ilaria, Rizzo, Larissa, Elshafei, Asmaa Said, Gremse, Felix, Pola, Robert, Pechar, Michal, Etrych, Tomáš, Becker, Svea, Trautwein, Christian, Bülow, Roman D., Boor, Peter, Knuechel, Ruth, von Stillfried, Saskia, Storm, Gert, Puri, Sanyogitta, Barry, Simon T., Schulz, Volkmar, Kiessling, Fabian, Ashford, Marianne B., and Lammers, Twan

Published
2024
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4. Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis

Author

Gaertner, Florian, Ishikawa-Ankerhold, Hellen, Stutte, Susanne, Fu, Wenwen, Weitz, Jutta, Dueck, Anne, Nelakuditi, Bhavishya, Fumagalli, Valeria, van den Heuvel, Dominic, Belz, Larissa, Sobirova, Gulnoza, Zhang, Zhe, Titova, Anna, Navarro, Alejandro Martinez, Pekayvaz, Kami, Lorenz, Michael, von Baumgarten, Louisa, Kranich, Jan, Straub, Tobias, Popper, Bastian, Zheden, Vanessa, Kaufmann, Walter Anton, Guo, Chenglong, Piontek, Guido, von Stillfried, Saskia, Boor, Peter, Colonna, Marco, Clauß, Sebastian, Schulz, Christian, Brocker, Thomas, Walzog, Barbara, Scheiermann, Christoph, Aird, William C., Nerlov, Claus, Stark, Konstantin, Petzold, Tobias, Engelhardt, Stefan, Sixt, Michael, Hauschild, Robert, Rudelius, Martina, Oostendorp, Robert A. J., Iannacone, Matteo, Heinig, Matthias, and Massberg, Steffen

Published
2024
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6. Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative

Author

Zerbe, Norman, Schwen, Lars Ole, Geißler, Christian, Wiesemann, Katja, Bisson, Tom, Boor, Peter, Carvalho, Rita, Franz, Michael, Jansen, Christoph, Kiehl, Tim-Rasmus, Lindequist, Björn, Pohlan, Nora Charlotte, Schmell, Sarah, Strohmenger, Klaus, Zakrzewski, Falk, Plass, Markus, Takla, Michael, Küster, Tobias, Homeyer, André, and Hufnagl, Peter

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

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.

Published
2023
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8. A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients

Author

Yoo, Daniel, Divard, Gillian, Raynaud, Marc, Cohen, Aaron, Mone, Tom D., Rosenthal, John Thomas, Bentall, Andrew J., Stegall, Mark D., Naesens, Maarten, Zhang, Huanxi, Wang, Changxi, Gueguen, Juliette, Kamar, Nassim, Bouquegneau, Antoine, Batal, Ibrahim, Coley, Shana M., Gill, John S., Oppenheimer, Federico, De Sousa-Amorim, Erika, Kuypers, Dirk R. J., Durrbach, Antoine, Seron, Daniel, Rabant, Marion, Van Huyen, Jean-Paul Duong, Campbell, Patricia, Shojai, Soroush, Mengel, Michael, Bestard, Oriol, Basic-Jukic, Nikolina, Jurić, Ivana, Boor, Peter, Cornell, Lynn D., Alexander, Mariam P., Toby Coates, P., Legendre, Christophe, Reese, Peter P., Lefaucheur, Carmen, Aubert, Olivier, and Loupy, Alexandre

Published
2024
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13. Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

Author

Farris, Alton, Alexander, Mariam, Balis, Ulysses, Barisoni, Laura, Boor, Peter, Bülow, Roman, Cornell, Lynn, Demetris, Anthony, Farkash, Evan, Hermsen, Meyke, Hogan, Julien, Kain, Renate, Kers, Jesper, Kong, Jun, Levenson, Richard, Loupy, Alexandre, Naesens, Maarten, Sarder, Pinaki, Tomaszewski, John, van der Laak, Jeroen, van Midden, Dominique, Yagi, Yukako, and Solez, Kim

Subjects
Banff, artificial intelligence, digital pathology, image analysis, machine learning, Humans, Artificial Intelligence, Algorithms, Kidney Transplantation, Kidney
Abstract

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

Published
2023

14. Adamts12 promotes fibrosis by restructuring extracellular matrix to enable activation of injury-responsive fibroblasts

Author

Hoeft, Konrad, Koch, Lars, Ziegler, Susanne, Zhang, Ling, Luetke, Steffen, Tanzer, Maria C., Mohanta, Debashish, Schumacher, David, Schreibing, Felix, Long, Qingqing, Kim, Hyojin, Klinkhammer, Barbara M., Schikarski, Carla, Maryam, Sidrah, Baens, Mathijs, Hermann, Juliane, Krieg, Sarah, Peisker, Fabian, De Laporte, Laura, Schaefer, Gideon J.L., Menzel, Sylvia, Jankowski, Joachim, Humphreys, Benjamin D., Wahida, Adam, Schneider, Rebekka K., Versele, Matthias, Boor, Peter, Mann, Matthias, Sengle, Gerhard, Hayat, Sikander, and Kramann, Rafael

Subjects
Medical research, Medicine, Experimental, Fibrosis -- Physiological aspects, Proteases -- Physiological aspects, Extracellular matrix -- Physiological aspects, Fibroblasts -- Physiological aspects -- Health aspects, Wounds and injuries -- Physiological aspects
Abstract

Fibrosis represents the uncontrolled replacement of parenchymal tissue with extracellular matrix (ECM) produced by myofibroblasts. While genetic fate-tracing and single-cell RNA-Seq technologies have helped elucidate fibroblast heterogeneity and ontogeny beyond fibroblast to myofibroblast differentiation, newly identified fibroblast populations remain ill defined, with respect to both the molecular cues driving their differentiation and their subsequent role in fibrosis. Using an unbiased approach, we identified the metalloprotease ADAMTS12 as a fibroblast-specific gene that is strongly upregulated during active fibrogenesis in humans and mice. Functional in vivo KO studies in mice confirmed that Adamts12 was critical during fibrogenesis in both heart and kidney. Mechanistically, using a combination of spatial transcriptomics and expression of catalytically active or inactive ADAMTS12, we demonstrated that the active protease of ADAMTS12 shaped ECM composition and cleaved hemicentin 1 (HMCN1) to enable the activation and migration of a distinct injury- responsive fibroblast subset defined by aberrant high JAK/STAT signaling., Introduction Fibrosis represents the uncontrolled replacement of parenchymal tissue by extracellular matrix (ECM) (1). Whereas deposition of ECM sustains tissue integrity immediately after injury, continued deposition of ECM by myofibroblasts [...]

Published
2024
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15. Recommendations on test datasets for evaluating AI solutions in pathology

Author

Homeyer, André, Geißler, Christian, Schwen, Lars Ole, Zakrzewski, Falk, Evans, Theodore, Strohmenger, Klaus, Westphal, Max, Bülow, Roman David, Kargl, Michaela, Karjauv, Aray, Munné-Bertran, Isidre, Retzlaff, Carl Orge, Romero-López, Adrià, Sołtysiński, Tomasz, Plass, Markus, Carvalho, Rita, Steinbach, Peter, Lan, Yu-Chia, Bouteldja, Nassim, Haber, David, Rojas-Carulla, Mateo, Sadr, Alireza Vafaei, Kraft, Matthias, Krüger, Daniel, Fick, Rutger, Lang, Tobias, Boor, Peter, Müller, Heimo, Hufnagl, Peter, and Zerbe, Norman

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

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations for the collection of test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.

Published
2022
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18. Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning

Author

Bouteldja, Nassim, Klinkhammer, Barbara Mara, Schlaich, Tarek, Boor, Peter, and Merhof, Dorit

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

In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on image-to-image translation is gaining importance in this field by addressing variabilities without the manual overhead. Here, we tackle the variation of different histological stains by unsupervised stain-to-stain translation to enable a stain-independent applicability of a deep learning segmentation model. We use CycleGANs for stain-to-stain translation in kidney histopathology, and propose two novel approaches to improve translational effectivity. First, we integrate a prior segmentation network into the CycleGAN for a self-supervised, application-oriented optimization of translation through semantic guidance, and second, we incorporate extra channels to the translation output to implicitly separate artificial meta-information otherwise encoded for tackling underdetermined reconstructions. The latter showed partially superior performances to the unmodified CycleGAN, but the former performed best in all stains providing instance-level Dice scores ranging between 78% and 92% for most kidney structures, such as glomeruli, tubules, and veins. However, CycleGANs showed only limited performance in the translation of other structures, e.g. arteries. Our study also found somewhat lower performance for all structures in all stains when compared to segmentation in the original stain. Our study suggests that with current unsupervised technologies, it seems unlikely to produce generally applicable simulated stains., Comment: Accepted for Journal of Pathology Informatics (JPI), 2022

Published
2021

19. The role of desmoglein-2 in kidney disease

Author

Xu, Tong, Herkens, Lea, Jia, Ting, Klinkhammer, Barbara M., Kant, Sebastian, Krusche, Claudia A., Buhl, Eva M., Hayat, Sikander, Floege, Jürgen, Strnad, Pavel, Kramann, Rafael, Djudjaj, Sonja, and Boor, Peter

Published
2024
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24. Inhibition of the renal apical sodium dependent bile acid transporter prevents cholemic nephropathy in mice with obstructive cholestasis

Author

Ghallab, Ahmed, González, Daniela, Strängberg, Ellen, Hofmann, Ute, Myllys, Maiju, Hassan, Reham, Hobloss, Zaynab, Brackhagen, Lisa, Begher-Tibbe, Brigitte, Duda, Julia C., Drenda, Carolin, Kappenberg, Franziska, Reinders, Joerg, Friebel, Adrian, Vucur, Mihael, Turajski, Monika, Seddek, Abdel-latief, Abbas, Tahany, Abdelmageed, Noha, Morad, Samy A.F., Morad, Walaa, Hamdy, Amira, Albrecht, Wiebke, Kittana, Naim, Assali, Mohyeddin, Vartak, Nachiket, van Thriel, Christoph, Sous, Ansam, Nell, Patrick, Villar-Fernandez, Maria, Cadenas, Cristina, Genc, Erhan, Marchan, Rosemarie, Luedde, Tom, Åkerblad, Peter, Mattsson, Jan, Marschall, Hanns-Ulrich, Hoehme, Stefan, Stirnimann, Guido, Schwab, Matthias, Boor, Peter, Amann, Kerstin, Schmitz, Jessica, Bräsen, Jan H., Rahnenführer, Jörg, Edlund, Karolina, Karpen, Saul J., Simbrunner, Benedikt, Reiberger, Thomas, Mandorfer, Mattias, Trauner, Michael, Dawson, Paul A., Lindström, Erik, and Hengstler, Jan G.

Published
2024
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28. Inflammation and vascular remodeling in COVID-19 hearts

Author

Werlein, Christopher, Ackermann, Maximilian, Stark, Helge, Shah, Harshit R., Tzankov, Alexandar, Haslbauer, Jasmin Dinonne, von Stillfried, Saskia, Bülow, Roman David, El-Armouche, Ali, Kuenzel, Stephan, Robertus, Jan Lukas, Reichardt, Marius, Haverich, Axel, Höfer, Anne, Neubert, Lavinia, Plucinski, Edith, Braubach, Peter, Verleden, Stijn, Salditt, Tim, Marx, Nikolaus, Welte, Tobias, Bauersachs, Johann, Kreipe, Hans-Heinrich, Mentzer, Steven J., Boor, Peter, Black, Stephen M., Länger, Florian, Kuehnel, Mark, and Jonigk, Danny

Published
2023
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29. IgA nephropathy

Author

Stamellou, Eleni, Seikrit, Claudia, Tang, Sydney C. W., Boor, Peter, Tesař, Vladimir, Floege, Jürgen, Barratt, Jonathan, and Kramann, Rafael

Published
2023
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37. Trends and perspectives for improving quality of chronic kidney disease care: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

Author

Al-Aly, Ziyad, Ashuntantang, Gloria E., Boor, Peter, Calice da Silva, Viviane, Coleman, Jill, Coresh, Josef, Delanaye, Pierre, Ebert, Natalie, Enghard, Philipp, Feldman, Harold I., Fisher, Lori, Flythe, Jennifer E., Fukui, Akira, Grams, Morgan E., Ix, Joseph H., Jardine, Meg J., Jha, Vivek, Ju, Wenjun, Jurish, Robert, Kalyesubula, Robert, Kashihara, Naoki, Levey, Andrew S., Levin, Adeera, Luyckx, Valerie, Małyszko, Jolanta, Manski-Nankervis, Jo-Anne, Navaneethan, Sankar D., Obrador, Greg, Ortiz, Alberto, Ortiz, John, Cardoso Dos Santos, Bento Fortunato, Sarnak, Mark J., Schaeffner, Elke, Selby, Nick M., Simpson, David M., Solá, Laura, St. Peter, Wendy L., Stevens, Paul E., Tangri, Navdeep, Tannor, Elliot Koranteng, Tchokhonelidze, Irma, Wilck, Nicola, Wong, Michelle M.Y., Eckardt, Kai-Uwe, Delgado, Cynthia, Heerspink, Hiddo J.L., Pecoits-Filho, Roberto, Ricardo, Ana C., Stengel, Bénédicte, Tonelli, Marcello, Cheung, Michael, Jadoul, Michel, Winkelmayer, Wolfgang C., and Kramer, Holly

Published
2023
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41. Biallelic variants in CRIPT cause a Rothmund-Thomson-like syndrome with increased cellular senescence

Author

Averdunk, Luisa, Huetzen, Maxim A., Moreno-Andrés, Daniel, Kalb, Reinhard, McKee, Shane, Hsieh, Tzung-Chien, Seibt, Annette, Schouwink, Marten, Lalani, Seema, Faqeih, Eissa Ali, Brunet, Theresa, Boor, Peter, Neveling, Kornelia, Hoischen, Alexander, Hildebrandt, Barbara, Graf, Elisabeth, Lu, Linchao, Jin, Weidong, Schaper, Joerg, Omer, Jamal A., Demaret, Tanguy, Fleischer, Nicole, Schindler, Detlev, Krawitz, Peter, Mayatepek, Ertan, Wieczorek, Dagmar, Wang, Lisa L., Antonin, Wolfram, Jachimowicz, Ron D., von Felbert, Verena, and Distelmaier, Felix

Published
2023
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43. Adult human kidney organoids originate from CD24+ cells and represent an advanced model for adult polycystic kidney disease

Author

Xu, Yaoxian, Kuppe, Christoph, Perales-Patón, Javier, Hayat, Sikander, Kranz, Jennifer, Abdallah, Ali T., Nagai, James, Li, Zhijian, Peisker, Fabian, Saritas, Turgay, Halder, Maurice, Menzel, Sylvia, Hoeft, Konrad, Kenter, Annegien, Kim, Hyojin, van Roeyen, Claudia R. C., Lehrke, Michael, Moellmann, Julia, Speer, Thimoteus, Buhl, Eva M., Hoogenboezem, Remco, Boor, Peter, Jansen, Jitske, Knopp, Cordula, Kurth, Ingo, Smeets, Bart, Bindels, Eric, Reinders, Marlies E. J., Baan, Carla, Gribnau, Joost, Hoorn, Ewout J., Steffens, Joachim, Huber, Tobias B., Costa, Ivan, Floege, Jürgen, Schneider, Rebekka K., Saez-Rodriguez, Julio, Freedman, Benjamin S., and Kramann, Rafael

Published
2022
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46. Ordinal Classification and Regression Techniques for Distinguishing Neutrophilic Cell Maturity Stages in Human Bone Marrow

Author

Gräbel, Philipp, Crysandt, Martina, Klinkhammer, Barbara M., Boor, Peter, Brümmendorf, Tim H., Merhof, Dorit, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, El Yacoubi, Mounîm, editor, Granger, Eric, editor, Yuen, Pong Chi, editor, Pal, Umapada, editor, and Vincent, Nicole, editor

Published
2022
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48. Platelet-instructed SPP1+ macrophages drive myofibroblast activation in fibrosis in a CXCL4-dependent manner

Author

Hoeft, Konrad, Schaefer, Gideon J.L., Kim, Hyojin, Schumacher, David, Bleckwehl, Tore, Long, Qingqing, Klinkhammer, Barbara Mara, Peisker, Fabian, Koch, Lars, Nagai, James, Halder, Maurice, Ziegler, Susanne, Liehn, Elisa, Kuppe, Christoph, Kranz, Jennifer, Menzel, Sylvia, Costa, Ivan, Wahida, Adam, Boor, Peter, Schneider, Rebekka K., Hayat, Sikander, and Kramann, Rafael

Published
2023
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