Literature DB >> 33039425

Grand challenges for medical physics in radiation oncology.

Claudio Fiorino1, Robert Jeraj2, Catharine H Clark3, Cristina Garibaldi4, Dietmar Georg5, Ludvig Muren6, Wouter van Elmpt7, Thomas Bortfeld8, Nuria Jornet9.   

Abstract

Medical physics has made considerable contributions to recent advances in radiation oncology. Medical physicists are key players in the clinical and scientific radiation oncology context due to their unique skill sets, flexibility, clinical involvement and intrinsic translational character. The continuing development and widespread adoption of "high-tech" radiotherapy has led to an increased need for medical physics involvement. More recently, our field is rapidly changing towards an era of "precision oncology". These changes have opened new challenges for the definition of the professional and scientific roles and responsibilities of medical physicists. In this paper, we have identified four grand challenges of medical physics in radiation oncology: (1) improving target volume definition, (2) adoption of artificial intelligence and automation, (3) development of predictive models of biological effects for precision medicine, and (4) need for leadership. New visions and suggestions to orientate medical physics to successfully face these new challenges are summarized. We foresee that the scientific and professional challenges of our times are pushing medical physicists to accelerate toward multidisciplinarity. Medical physicists are expected to innovatively drive interactions and collaborations with other specialists outside radiation oncology while the radiation physics core will remain central. Medical physicists will retain strong and pivotal roles in quality, safety and in managing ever more complex technologies. The new challenges will require medical physicists to continuously update skills and innovate education, adapt curricula to include new fields, reinforce multi-disciplinary attitude and spirit of innovation. These challenges require visionary and open leadership, which is able to merge established roles with the exciting new fields where medical physics should increasingly contribute.
Copyright © 2020 Elsevier B.V. All rights reserved.

Keywords:  AI; Leadership; Medical physics; Radiation oncology; Radiobiology models; Target definition

Mesh:

Year:  2020        PMID: 33039425     DOI: 10.1016/j.radonc.2020.10.001

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  4 in total

1.  Modeling the propagation of tumor fronts with shortest path and diffusion models-implications for the definition of the clinical target volume.

Authors:  Thomas Bortfeld; Gregory Buti
Journal:  Phys Med Biol       Date:  2022-07-25       Impact factor: 4.174

2.  Evolution and Evaluation of a Structured Applied Physics Course for Radiation Oncology and Radiation Physics Trainees.

Authors:  S Babic; A L McNiven; A Bezjak; J M Balogh; K Mah; M N Tsao
Journal:  J Cancer Educ       Date:  2022-06-28       Impact factor: 1.771

3.  Current status and developments of German curriculum-based residency training programmes in radiation oncology.

Authors:  Hans Christiansen; Maximilian Niyazi; Marcel Büttner; Nils Cordes; Tobias Gauer; Daniel Habermehl; Gunther Klautke; Oliver Micke; Matthias Mäurer; Jan Sokoll; Esther Gera Cornelia Troost
Journal:  Radiat Oncol       Date:  2021-03-20       Impact factor: 3.481

4.  Machine learning applications in radiation oncology: Current use and needs to support clinical implementation.

Authors:  Charlotte L Brouwer; Anna M Dinkla; Liesbeth Vandewinckele; Wouter Crijns; Michaël Claessens; Dirk Verellen; Wouter van Elmpt
Journal:  Phys Imaging Radiat Oncol       Date:  2020-11-30
  4 in total

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