Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers von Trebeschi, S., Drago, S.G., Birkbak, N.J., Kurilova, I., Cǎlin, A.M., Delli Pizzi, A., Lalezari, F., Lambregts, D.M.J., Rohaan, M.W., Parmar, C., Rozeman, E.A., Hartemink, K.J., Swanton, C., Haanen, J B A G, Blank, C.U., Smit, E.F., Beets-Tan, R.G.H., Aerts, H.J.W.L

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Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures von Valentinitsch, A., Trebeschi, S., Kaesmacher, J., Lorenz, C., Löffler, M. T., Zimmer, C., Baum, T., Kirschke, J. S.

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CT texture analysis in colorectal liver metastases and the surrounding liver parenchyma and its potential as an imaging biomarker of disease aggressiveness, response and survival von Beckers, R.C.J., Trebeschi, S., Maas, M., Schnerr, R.S., Sijmons, J.M.L., Beets, G.L., Houwers, J.B., Beets-Tan, R.G.H., Lambregts, D.M.J.

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Integrated noninvasive diagnostics for prediction of survival in immunotherapy von Yeghaian, M., Bodalal, Z., Tareco Bucho, T.M., Kurilova, I., Blank, C.U., Smit, E.F., van der Heijden, M.S., Nguyen-Kim, T.D.L., van den Broek, D., Beets-Tan, R.G.H., Trebeschi, S.

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Radiomics in immuno-oncology von Bodalal, Z., Wamelink, I., Trebeschi, S., Beets-Tan, R.G.H.

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Radiomics signature of primary diffusion MR for treatment response prediction in rectal carcinoma von Van Griethuysen, J.J.M, Trebeschi, S, Beets-Tan, R, Lambregts, D.M.J, Lahaye, M, Bakers, F.C.H, Peeters, N, Vliegen, R.F.A, Voest, E, Aerts, H.J.W.L

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1910MO ARTIMES: Automated response evaluation to treatment in mesothelioma von Groot Lipman, K.B.W., Wittenberg, R., de Oliveira Taveira, M., Smesseim, I., Schmitz, A., Boellaard, T., Chupetlovska, K., Abdelatty Elsayed, M., Petrychenko, L., Jain, N., Arico, F., Bajaj, A., Fennell, D.A., Curioni-Fontecedro, A., Nguyen-kim, T.D.L., Tissier, R., Baas, P., Burgers, S., De Gooijer, D., Trebeschi, S.

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30P AI-based response monitoring in gastroenteropancreatic neuroendocrine tumors von Chupetlovska, K., Engel, J., Beets-Tan, R., Tesselaar, M., Maas, M., Trebeschi, S.

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P228 MRI-based response assessment in muscle-invasive bladder cancer: AnalyzingObserver variability in neoadjuvant immunotherapy von Greidanus, J., Van Dijk - De Haan, M.C., Bruining, A., Westerink, B., Dehghanpour, A., Van Rhijn, B.W.G., Van Der Heijden, M.S., Beets- Tan, R.G.H., Trebeschi, S., Boellaard, T.N.

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Deep learning radiomics distinguishes intrapulmonary disease from metastases in immunotherapy-treated melanoma patients von Nguyen-Kim, T.D.L., Trebeschi, S., Pouw, J.E.E., Milanese, G., Topff, L., Bodalal, Z., Mangana, J., Frauenfelder, T., Haanen, J B A G, Blank, C.U., Aerts, H J W L, Beets-Tan, R., Dummer, R.

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1301P - Deep learning radiomics distinguishes intrapulmonary disease from metastases in immunotherapy-treated melanoma patients von Nguyen-Kim, T.D.L., Trebeschi, S., Pouw, J.E.E., Milanese, G., Topff, L., Bodalal, Z., Mangana, J., Frauenfelder, T., Haanen, J B A G, Blank, C.U., Aerts, H J W L, Beets-Tan, R., Dummer, R.

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1301PDeep learning radiomics distinguishes intrapulmonary disease from metastases in immunotherapy-treated melanoma patients von Nguyen-Kim, T D L, Trebeschi, S, Pouw, J E E, Milanese, G, Topff, L, Bodalal, Z, Mangana, J, Frauenfelder, T, Haanen, J B A G, Blank, C U, Aerts, H J W L, Beets-Tan, R, Dummer, R

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102P Redefining radiological response assessment in soft tissue sarcoma von van der Loo, I., Chupetlovska, K., Gennaro, N., Bruining, A., Bodalal, Z., Reijers, S., Haas, R.L., van der Graaf, W.T.A., Beets-Tan, R., Imholz, A., Trebeschi, S.

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456 - Radiomics signature of primary diffusion MR for treatment response prediction in rectal carcinoma von Van Griethuysen, J.J.M., Trebeschi, S., Beets-Tan, R., Lambregts, D.M.J., Lahaye, M., Bakers, F.C.H., Peeters, N., Vliegen, R.F.A., Voest, E., Aerts, H.J.W.L.

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Prognostic value of deep learning-mediated treatment monitoring in lung cancer patients receiving immunotherapy von Trebeschi, S., Bodalal, Z., Boellaard, T.N., Bucho, T.T.M., Drago, S.G., Kurilova, I., Calin-Vainak, A.M., Delli Pizzi, A., Muller, M., Hummelink, K., Hartemink, K.J., Nguyen-Kim, T.D.L., Smit, E.F., Aerts, H.J.W.L., Beets-Tan, R.G.H.

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Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers von Trebeschi, S., Drago, S.G., Birkbak, N.J., Kurilova, I., Calin, A.M., Pizzi, A.D., Lalezari, F., Lambregts, D.M.J., Rohaan, M.W., Parmar, C., Rozeman, E.A., Hartemink, K.J., Swanton, C., Haanen, J.B.A.G., Blank, C.U., Smit, E.F., Beets-Tan, R.G.H., Aerts, H.J.W.L.

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Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers von Trebeschi, S, Drago, SG, Birkbak, NJ, Kurilova, I, Calin, AM, Pizzi, AD, Lalezari, F, Lambregts, DMJ, Rohaan, MW, Parmar, C, Rozeman, EA, Hartemink, KJ, Swanton, C, Haanen, JBAG, Blank, CU, Smit, EF, Beets-Tan, RGH, Aerts, HJWL

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DTI-basierte Pradiktion des IDH-Status niedriggradiger Gliome durch neuronale Netzwerke von Eichinger, P, Alberts, E, Valentinitsch, A, Trebeschi, S, Delbridge, C, Huber, T, Bette, S, Gempt, J, Zimmer, C, Menze, B, Wiestler, B

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Constant Mean Curvature Hypersurfaces in Anti-de Sitter Space von Trebeschi, Enrico

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MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer von Delli Pizzi, Andrea, Chiarelli, Antonio Maria, Chiacchiaretta, Piero, d’Annibale, Martina, Croce, Pierpaolo, Rosa, Consuelo, Mastrodicasa, Domenico, Trebeschi, Stefano, Lambregts, Doenja Marina Johanna, Caposiena, Daniele, Serafini, Francesco Lorenzo, Basilico, Raffaella, Cocco, Giulio, Di Sebastiano, Pierluigi, Cinalli, Sebastiano, Ferretti, Antonio, Wise, Richard Geoffrey, Genovesi, Domenico, Beets-Tan, Regina G. H., Caulo, Massimo

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