Deep learning for diagnosis and survival prediction in soft tissue sarcoma von Foersch, S., Eckstein, M., Wagner, D.-C., Gach, F., Woerl, A.-C., Geiger, J., Glasner, C., Schelbert, S., Schulz, S., Porubsky, S., Kreft, A., Hartmann, A., Agaimy, A., Roth, W.

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Molecular imaging of VEGF in gastrointestinal cancer in vivo using confocal laser endomicroscopy von Foersch, Sebastian, Kiesslich, Ralf, Waldner, Maximillian J, Delaney, Peter, Galle, Peter R, Neurath, Markus F, Goetz, Martin

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Prognostic significance of interferon regulating factor 4 (IRF4) in node-negative breast cancer von Heimes, Anne-Sophie, Madjar, K., Edlund, K., Battista, M. J., Almstedt, K., Gebhard, S., Foersch, S., Rahnenführer, J., Brenner, W., Hasenburg, A., Hengstler, J. G., Schmidt, M.

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13P Comparison study of different programmed death-ligand 1 (PD-L1) assays, readers and scoring methods in triple-negative breast cancer (TNBC) von Noske, A., Wagner, D-C., Schwamborn, K., Foersch, S., Steiger, K., Kiechle, M., Karapetyan, S., Oettler, D., Hapfelmeier, A., Roth, W., Weichert, W.

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Transient receptor potential melastatin 8 ion channel in macrophages modulates colitis through a balance-shift in TNF-alpha and interleukin-10 production von Khalil, M, Babes, A, Lakra, R, Försch, S, Reeh, P W, Wirtz, S, Becker, C, Neurath, M F, Engel, M A

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P075 Neutrophils prevent rectal bleeding in Ulcerative Colitis by peptidyl-arginine deiminase-4-dependent immunothrombosis von Leppkes, M, Lindemann, A, Gößwein, S, Paulus, S, Roth, D, Hartung, A, Liebing, E, Zundler, S, Acera, M G, Patankar, J V, Mascia, F, Scheibe, K, Hoffmann, M, Uderhardt, S, Schauer, C, Foersch, S, Neufert, C, Vieth, M, Schett, G, Atreya, R, Kühl, A A, Bleich, A, Becker, C, Herrmann, M, Neurath, M F

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Endomicroscopic Imaging of COX-2 Activity in Murine Sporadic and Colitis-Associated Colorectal Cancer von Waldner, Maximilian J., Neurath, Markus F., Neufert, Clemens, Foersch, Sebastian

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End-to-end prognostication in colorectal cancer by Deep Learning: a multicentric retrospective study von West, N, Jiang, X, Hoffmeister, M, Brenner, H, Muti, H.S, Yuan, T, Foersch, S, Brobeil, A, Jonnagaddala, J, Hawkins, N, Ward, R, Brinker, T, Saldanha, O, Ke, J, Müller, W, Grabsch, H, Quirke, P, Truhn, D, Kather, J.N

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Encrypted federated learning for secure decentralized collaboration in cancer image analysis von Truhn, D, Arasteh, S.T, Saldanha, O.L, Mueller-Franzes, G, Khader, F, Quirke, P, West, N.P, Gray, R, Hutchins, G.G.A, James, J.A, Loughrey, M.B, Salto-Tellez, M, Brenner, H, Brobeil, A, Yuan, T, Chang-Claude, J, Hoffmeister, M, Foersch, S, Han, T, Keil, S, Schulze-Hagen, M, Isfort, P, Bruners, P, Kaissis, G, Kuhl, C, Nebelung, S, Kather, J.N

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Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study von Niehues, JM, Quirke, P, West, NP, Grabsch, HI, van Treeck, M, Schirris, Y, Veldhuizen, GP, Hutchins, GGA, Richman, SD, Foersch, S, Brinker, TJ, Fukuoka, J, Bychkov, A, Uegami, W, Truhn, D, Brenner, H, Brobeil, A, Hoffmeister, M, Kather, JN

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Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study von Wagner, S.J, Reisenbüchler, D, West, N.P, Niehues, J.M, Zhu, J, Foersch, S, Veldhuizen, G.P, Quirke, P, Grabsch, H.I, van den Brandt, P.A, Hutchins, G.G.A, Richman, S.D, Yuan, T, Langer, R, Jenniskens, J.C.A, Offermans, K, Mueller, W, Gray, R, Gruber, S.B, Greenson, J.K, Rennert, G, Bonner, J.D, Schmolze, D, Jonnagaddala, J, Hawkins, N.J, Ward, R.L, Morton, D, Seymour, M, Magill, L, Nowak, M, Hay, J, Koelzer, V.H, Church, D.N, TransSCOT consortium, Matek, C, Geppert, C, Peng, C, Zhi, C, Ouyang, X, James, J.A, Loughrey, M.B, Salto-Tellez, M, Brenner, H, Hoffmeister, M, Truhn, D, Schnabel, J.A, Boxberg, M, Peng, T, Kather, J.N

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Swarm learning for decentralized artificial intelligence in cancer histopathology von Saldanha, OL, Quirke, P, West, NP, James, JA, Loughrey, MB, Grabsch, HI, Salto-Tellez, M, Alwers, E, Cifci, D, Ghaffari Laleh, N, Seibel, T, Gray, R, Hutchins, GGA, Brenner, H, van Treeck, M, Yuan, T, Brinker, TJ, Chang-Claude, J, Khader, F, Schuppert, A, Luedde, T, Trautwein, C, Muti, HS, Foersch, S, Hoffmeister, M, Truhn, D, Kather, JN

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Supplementary Material for: CD15 is a risk predictor and a novel target in clear cell renal cell carcinoma von P.J., Stenzel, M., Schindeldecker, L., Seidmann, E., Herpel, M., Hohenfellner, G., Hatiboglu, S., Foersch, S., Porubsky, S., Macher-Goeppinger, W., Roth, K.E., Tagscherer

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Artificial intelligence to identify genetic alterations in conventional histopathology von Cifci, Didem, Foersch, Sebastian, Kather, Jakob Nikolas

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Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides von Woerl, Ann-Christin, Eckstein, Markus, Geiger, Josephine, Wagner, Daniel C., Daher, Tamas, Stenzel, Philipp, Fernandez, Aurélie, Hartmann, Arndt, Wand, Michael, Roth, Wilfried, Foersch, Sebastian

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Colitis-associated neoplasia: molecular basis and clinical translation von Foersch, Sebastian, Neurath, Markus F

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Comparative analysis of nuclear and mitochondrial DNA from tissue and liquid biopsies of colorectal cancer patients von Haupts, Anna, Vogel, Anne, Foersch, Sebastian, Hartmann, Monika, Maderer, Annett, Wachter, Nicolas, Huber, Tobias, Kneist, Werner, Roth, Wilfried, Lang, Hauke, Moehler, Markus, Hartmann, Nils

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Artificial Intelligence in Pathology von Foersch, Sebastian, Klauschen, Frederick, Hufnagl, Peter, Roth, Wilfried

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Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning von Saldanha, Oliver Lester, Muti, Hannah Sophie, Grabsch, Heike I., Langer, Rupert, Dislich, Bastian, Kohlruss, Meike, Keller, Gisela, van Treeck, Marko, Hewitt, Katherine Jane, Kolbinger, Fiona R., Veldhuizen, Gregory Patrick, Boor, Peter, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas

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Medical domain knowledge in domain-agnostic generative AI von Kather, Jakob Nikolas, Ghaffari Laleh, Narmin, Foersch, Sebastian, Truhn, Daniel

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