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| Verfasst von: | Vens, Conchita [VerfasserIn]  |
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| | Luijk, P. van [VerfasserIn] |
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| | Vogelius, R. I. [VerfasserIn] |
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| | El Naqa, I. [VerfasserIn] |
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| | Humbert-Vidan, L. [VerfasserIn] |
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| | Neubeck, C. van [VerfasserIn] |
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| | Gomez-Roman, N. [VerfasserIn] |
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| | Bahn, Emanuel [VerfasserIn] |
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| | Brualla, L. [VerfasserIn] |
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| | Böhlen, T. T. [VerfasserIn] |
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| | Ecker, S. [VerfasserIn] |
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| | Koch, R. [VerfasserIn] |
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| | Handeland, A. [VerfasserIn] |
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| | Pereira, S. [VerfasserIn] |
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| | Possenti, L. [VerfasserIn] |
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| | Rancati, T. [VerfasserIn] |
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| | Todor, D. [VerfasserIn] |
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| | Vanderstraeten, B. [VerfasserIn] |
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| | Van Heerden, M. [VerfasserIn] |
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| | Ullrich, W. [VerfasserIn] |
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| | Jackson, M. [VerfasserIn] |
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| | Alber, Markus [VerfasserIn] |
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| | Marignol, L. [VerfasserIn] |
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| Titel: | A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy |
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| Verf.angabe: | C. Vens, P. van Luijk, R.I. Vogelius, I. El Naqa, L. Humbert-Vidan, C. von Neubeck, N. Gomez-Roman, E. Bahn, L. Brualla, T.T. Böhlen, S. Ecker, R. Koch, A. Handeland, S. Pereira, L. Possenti, T. Rancati, D. Todor, B. Vanderstraeten, M. Van Heerden, W. Ullrich, M. Jackson, M. Alber, L. Marignol, on behalf of the ESTRO DREAM team |
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| E-Jahr: | 2024 |
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| Jahr: | July 2024 |
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| Umfang: | 9 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Online veröffentlicht: 25. April 2024 ; Gesehen am 13.02.2025 |
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| Titel Quelle: | Enthalten in: Radiotherapy and oncology |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier Science, 1983 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 196(2024) vom: Juli, Artikel-ID 110277, Seite 1-9 |
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| ISSN Quelle: | 1879-0887 |
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| Abstract: | Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team’s consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines. |
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| DOI: | doi:10.1016/j.radonc.2024.110277 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1016/j.radonc.2024.110277 |
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| | kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S0167814024001993 |
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| | DOI: https://doi.org/10.1016/j.radonc.2024.110277 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | AI |
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| | Data science |
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| | Models |
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| | Prediction |
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| | Radiobiology |
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| K10plus-PPN: | 1917169035 |
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| Verknüpfungen: | → Zeitschrift |
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¬A¬ joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy / Vens, Conchita [VerfasserIn]; July 2024 (Online-Ressource)
