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Editorial| Volume 24, P136-137, October 2022

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Towards real-time radiotherapy planning: The role of autonomous treatment strategies

  • Luise A. Künzel
    Affiliations
    National Center for Tumor Diseases (NCT), Dresden, Germany
    German Cancer Research Center (DKFZ), Heidelberg, Germany
    Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
    Helmholtz-Zentrum Dresden – Rossendorf (HZDR), Dresden, Germany
    Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
    Search for articles by this author
  • Daniela Thorwarth
    Correspondence
    Corresponding author.
    Affiliations
    Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
    Search for articles by this author
Open AccessPublished:November 07, 2022DOI:https://doi.org/10.1016/j.phro.2022.11.006
Towards real-time radiotherapy planning: The role of autonomous treatment strategies
Previous ArticleAchievements and challenges in normal tissue response modelling for proton therapy
Next ArticleBenchmarking daily adaptation using fully automated radiotherapy treatment plan optimization for rectal cancer
      Today’s radiation therapy (RT) is a lengthy process, where the patient needs several appointments for consultation, simulation and fractioned treatment. In recent years accelerated treatment regimens including hypofractionation and single-fraction treatments have gained attention and may improve patient comfort, workflow efficiency and reduce costs [
      • Finazzi T.
      • van Sörnsen de Koste J.R.
      • Palacios M.A.
      • Spoelstra F.O.B.
      • Slotman B.J.
      • Haasbeek C.J.A.
      • et al.
      Delivery of magnetic resonance-guided single-fraction stereotactic lung radiotherapy.
      Phys Imaging Radiat Oncol. 2020; 14: 17-23https://doi.org/10.1016/j.phro.2020.05.002
      ]. Palacios et al. [
      • Palacios M.A.
      • Verheijen S.
      • Schneiders F.L.
      • Bohoudi O.
      • Slotman B.J.
      • Lagerwaard F.J.
      • et al.
      Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.
      Phys Imaging Radiat Oncol. 2022; 24: 76-81https://doi.org/10.1016/j.phro.2022.09.010
      ] described in this volume of our journal a same-day consultation, simulation and treatment workflow for stereotactic ablative radiotherapy (SABR) using a magnetic resonance imaging linear accelerator (MRI-Linac). The study included ten patients with small lung tumors eligible for single fraction treatment. For all patients, the consultation, treatment simulation, planning and delivery were realized on the same day. The median time reported for the whole process was 6.6 h, with a median of 2.6 h for the treatment planning as the most time-consuming step. Good patient satisfaction was reported in a post treatment questionnaire.
      In Palacios et al.’s study, a main component to ensure a fast radiotherapy planning process was a pre-planning step based on the diagnostic computed tomography (CT) data set [
      • Palacios M.A.
      • Verheijen S.
      • Schneiders F.L.
      • Bohoudi O.
      • Slotman B.J.
      • Lagerwaard F.J.
      • et al.
      Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.
      Phys Imaging Radiat Oncol. 2022; 24: 76-81https://doi.org/10.1016/j.phro.2022.09.010
      ]. This pre-planning was used to facilitate the whole process for the involved physician and physicist and also to steer the planning constraints in order to reduce time for manual tweaking of the patient individual constraints on the planning day. Such pre-optimization might also be a way to increase efficacy of conventional planning procedures and speed-up this part of the workflow. Recent studies have proposed to predict radiation dose distributions based on deep learning (DL) models applied to diagnostic CT [
      • Draguet C.
      • Barragàn-Montero A.M.
      • Vera M.C.
      • Thomas M.
      • Populaire P.
      • Defraene G.
      • et al.
      Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer.
      Radiother Oncol. 2022; 176: 101-107https://doi.org/10.1016/j.radonc.2022.08.031
      ]. Such DL based decision support tools, applied to diagnostic imaging information, might in the future enable to estimate potential side effects and risks related to RT already at the time of patient consultation and thus enable the physician as well as the patient to take informed treatment decisions. Potentially, pre-planning based on diagnostic imaging might be used directly as input for online-adaptive RT, which has to the best of our knowledge not yet been investigated.
      The one-day workflow proposed by Palacios et al. [
      • Palacios M.A.
      • Verheijen S.
      • Schneiders F.L.
      • Bohoudi O.
      • Slotman B.J.
      • Lagerwaard F.J.
      • et al.
      Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.
      Phys Imaging Radiat Oncol. 2022; 24: 76-81https://doi.org/10.1016/j.phro.2022.09.010
      ] used automation only to a minor extent and it is therefore highly dependent on the availability of staff throughout the day and not easily scalable to increasing patient numbers. Automated tools for various steps in the radiotherapy planning workflow such as automatic contouring [
      • Brunenberg E.J.L.
      • Steinseifer I.K.
      • van den Bosch S.
      • Kaanders J.H.A.M.
      • Brouwer C.L.
      • Gooding M.J.
      • et al.
      External validation of deep learning-based contouring of head and neck organs at risk.
      Phys Imaging Radiat Oncol. 2020; 15: 8-15https://doi.org/10.1016/j.phro.2020.06.006
      ,
      • Thor M.
      • Iyer A.
      • Jiang J.
      • Apte A.
      • Veeraraghavan H.
      • Allgood N.B.
      • et al.
      Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy.
      Phys Imaging Radiat Oncol. 2021; 19: 96-101https://doi.org/10.1016/j.phro.2021.07.009
      ,
      • Henderson E.G.A.
      • Vasquez Osorio E.M.
      • van Herk M.
      • Green A.F.
      Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data.
      Phys Imaging Radiat Oncol. 2022; 22: 44-50https://doi.org/10.1016/j.phro.2022.04.003
      ,
      • Johnston N.
      • De Rycke J.
      • Lievens Y.
      • van Eijkeren M.
      • Aelterman J.
      • Vandersmissen E.
      • et al.
      Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk.
      Phys Imaging Radiat Oncol. 2022; 23: 109-117https://doi.org/10.1016/j.phro.2022.07.004
      ,
      • Xie Y.
      • Kang K.
      • Wang Y.
      • Khandekar M.J.
      • Willers H.
      • Keane F.K.
      • et al.
      Automated clinical target volume delineation using deep 3D neural networks in radiation therapy of Non-small Cell Lung Cancer.
      Phys Imaging Radiat Oncol. 2021; 19: 131-137https://doi.org/10.1016/j.phro.2021.08.003
      ] and radiotherapy planning [
      • Fjellanger K.
      • Bolstad Hysing L.
      • Heijmen B.J.M.
      • Seime Pettersen H.E.
      • Sandvik I.M.
      • Husevåg Sulen T.
      • et al.
      Enhancing radiotherapy for locally advanced non-small cell lung cancer patients with iCE, a novel system for automated multi-criterial treatment planning including beam angle optimization.
      Cancers (Basel). 2021; 13: 5683https://doi.org/10.3390/cancers13225683
      ,
      • Marrazzo L.
      • Arilli C.
      • Pellegrini R.
      • Bonomo P.
      • Calusi S.
      • Talamonti C.
      • et al.
      Automated planning through robust templates and multicriterial optimization for lung VMAT SBRT of lung lesions.
      J Appl Clin Med Phys. 2020; 21: 114-120https://doi.org/10.1002/acm2.12872
      ,
      • Esposito P.G.
      • Castriconi R.
      • Mangili P.
      • Broggi S.
      • Fodor A.
      • Pasetti M.
      • et al.
      Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy.
      Phys Imaging Radiat Oncol. 2022; 23: 54-59https://doi.org/10.1016/j.phro.2022.06.009
      ,
      • Arends S.R.S.
      • Savenije M.H.F.
      • Eppinga W.S.C.
      • van der Velden J.M.
      • van den Berg C.A.T.
      • Verhoeff J.J.C.
      Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases.
      Phys Imaging Radiat Oncol. 2022; 21: 42-47https://doi.org/10.1016/j.phro.2022.02.003
      ,
      • van de Sande D.
      • Sharabiani M.
      • Bluemink H.
      • Kneepkens E.
      • Bakx N.
      • Hagelaar E.
      • et al.
      Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer.
      Phys Imaging Radiat Oncol. 2021; 20: 111-116https://doi.org/10.1016/j.phro.2021.11.007
      ] recently gained attention. For instance, Johnston et al. [
      • Johnston N.
      • De Rycke J.
      • Lievens Y.
      • van Eijkeren M.
      • Aelterman J.
      • Vandersmissen E.
      • et al.
      Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk.
      Phys Imaging Radiat Oncol. 2022; 23: 109-117https://doi.org/10.1016/j.phro.2022.07.004
      ] showed the usability of a convolutional neural network for segmentation of thoracic organs at risk. Although auto-contouring of targets is more challenging, Xie et al. [
      • Xie Y.
      • Kang K.
      • Wang Y.
      • Khandekar M.J.
      • Willers H.
      • Keane F.K.
      • et al.
      Automated clinical target volume delineation using deep 3D neural networks in radiation therapy of Non-small Cell Lung Cancer.
      Phys Imaging Radiat Oncol. 2021; 19: 131-137https://doi.org/10.1016/j.phro.2021.08.003
      ] recently introduced a 3D neural network for lung lesion contouring. Also, for treatment plan optimization different approaches were proposed [
      • Fjellanger K.
      • Bolstad Hysing L.
      • Heijmen B.J.M.
      • Seime Pettersen H.E.
      • Sandvik I.M.
      • Husevåg Sulen T.
      • et al.
      Enhancing radiotherapy for locally advanced non-small cell lung cancer patients with iCE, a novel system for automated multi-criterial treatment planning including beam angle optimization.
      Cancers (Basel). 2021; 13: 5683https://doi.org/10.3390/cancers13225683
      ,
      • Marrazzo L.
      • Arilli C.
      • Pellegrini R.
      • Bonomo P.
      • Calusi S.
      • Talamonti C.
      • et al.
      Automated planning through robust templates and multicriterial optimization for lung VMAT SBRT of lung lesions.
      J Appl Clin Med Phys. 2020; 21: 114-120https://doi.org/10.1002/acm2.12872
      ,
      • Esposito P.G.
      • Castriconi R.
      • Mangili P.
      • Broggi S.
      • Fodor A.
      • Pasetti M.
      • et al.
      Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy.
      Phys Imaging Radiat Oncol. 2022; 23: 54-59https://doi.org/10.1016/j.phro.2022.06.009
      ,
      • Arends S.R.S.
      • Savenije M.H.F.
      • Eppinga W.S.C.
      • van der Velden J.M.
      • van den Berg C.A.T.
      • Verhoeff J.J.C.
      Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases.
      Phys Imaging Radiat Oncol. 2022; 21: 42-47https://doi.org/10.1016/j.phro.2022.02.003
      ,
      • van de Sande D.
      • Sharabiani M.
      • Bluemink H.
      • Kneepkens E.
      • Bakx N.
      • Hagelaar E.
      • et al.
      Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer.
      Phys Imaging Radiat Oncol. 2021; 20: 111-116https://doi.org/10.1016/j.phro.2021.11.007
      ]. While automation tools for single workflow steps are already in clinical use, the next goal should be an autonomous workflow integrating contouring and plan optimization. Xia et al. [
      • Xia X.
      • Wang J.
      • Li Y.
      • Peng J.
      • Fan J.
      • Zhang J.
      • et al.
      An artificial intelligence-based full-process solution for radiotherapy: A proof of concept study on rectal cancer.
      Front Oncol. 2021; 10: 616721https://doi.org/10.3389/fonc.2020.616721
      ] already showed the feasibility of a full-process solution for rectal cancer, integrating artificial intelligence based automated contouring and planning. For prostate cancer Künzel et al. [
      • Künzel L.A.
      • Nachbar M.
      • Hagmüller M.
      • Gani C.
      • Boeke S.
      • Zips D.
      • et al.
      First experience of autonomous, un-supervised treatment planning integrated in adaptive MR-guided radiotherapy and delivered to a patient with prostate cancer.
      Radiother Oncol. 2021; 159: 197-201https://doi.org/10.1016/j.radonc.2021.03.032
      ,
      • Künzel L.A.
      • Nachbar M.
      • Hagmüller M.
      • Gani C.
      • Boeke S.
      • Wegener D.
      • et al.
      Clinical evaluation of autonomous, unsupervised planning integrated in MR-guided radiotherapy for prostate cancer.
      Radiother Oncol. 2022; 168: 229-233https://doi.org/10.1016/j.radonc.2022.01.036
      ] proved that such automated tools can be combined to an autonomous treatment planning workflow without human interaction for reference plans in magnetic resonance guided radiotherapy. In such a way the treatment planning process would be accelerated in a scalable approach.
      The work published by Palacios et al. [
      • Palacios M.A.
      • Verheijen S.
      • Schneiders F.L.
      • Bohoudi O.
      • Slotman B.J.
      • Lagerwaard F.J.
      • et al.
      Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.
      Phys Imaging Radiat Oncol. 2022; 24: 76-81https://doi.org/10.1016/j.phro.2022.09.010
      ] has demonstrated the potential related to timing efficiency with respect to the whole RT chain, i.e. simulation, data annotation, planning, patient-specific quality assurance and RT delivery. In their study, the authors impressively showed that the whole treatment planning and delivery chain can be effectuated in one day. In the same way of thought, several recent studies have shown that fully automated contouring and RT planning is possible [
      • Xia X.
      • Wang J.
      • Li Y.
      • Peng J.
      • Fan J.
      • Zhang J.
      • et al.
      An artificial intelligence-based full-process solution for radiotherapy: A proof of concept study on rectal cancer.
      Front Oncol. 2021; 10: 616721https://doi.org/10.3389/fonc.2020.616721
      ,
      • Künzel L.A.
      • Nachbar M.
      • Hagmüller M.
      • Gani C.
      • Boeke S.
      • Zips D.
      • et al.
      First experience of autonomous, un-supervised treatment planning integrated in adaptive MR-guided radiotherapy and delivered to a patient with prostate cancer.
      Radiother Oncol. 2021; 159: 197-201https://doi.org/10.1016/j.radonc.2021.03.032
      ,
      • Künzel L.A.
      • Nachbar M.
      • Hagmüller M.
      • Gani C.
      • Boeke S.
      • Wegener D.
      • et al.
      Clinical evaluation of autonomous, unsupervised planning integrated in MR-guided radiotherapy for prostate cancer.
      Radiother Oncol. 2022; 168: 229-233https://doi.org/10.1016/j.radonc.2022.01.036
      ,
      • Jagt T.Z.
      • Janssen T.M.
      • Betgen A.
      • Wiersema L.
      • Verhage R.
      • Garritsen S.
      • et al.
      Benchmarking daily adaptation using fully automated radiotherapy treatment plan optimization for rectal cancer.
      Phys Imaging Radiat Oncol. 2022; 24: 7-13https://doi.org/10.1016/j.phro.2022.08.006
      ]. Future developments might therefore enable real-time annotation, planning and delivery. Consequently, this might allow for one-stop-shop simulation and treatment delivery making separate simulation exams obsolete.
      In conclusion, the work published by Palacios et al. [
      • Palacios M.A.
      • Verheijen S.
      • Schneiders F.L.
      • Bohoudi O.
      • Slotman B.J.
      • Lagerwaard F.J.
      • et al.
      Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.
      Phys Imaging Radiat Oncol. 2022; 24: 76-81https://doi.org/10.1016/j.phro.2022.09.010
      ] in this virtual special issue of Physics and Imaging in Radiation Oncology focusing on highlights of ESTRO 2022 medical physics contributions impressively showed that developments towards low latency time or real-time RT simulation and planning is a current research focus. To enable future clinical implementation of such artificial intelligence driven real-time applications [
      • Brouwer C.L.
      • Dinkla A.M.
      • Vandewinckele L.
      • Crijns W.
      • Claessens M.
      • Verellen D.
      • et al.
      Machine learning applications in radiation oncology: Current use and needs to support clinical implementation.
      Phys Imaging Radiat Oncol. 2020; 16: 144-148https://doi.org/10.1016/j.phro.2020.11.002
      ], further research is needed in the fields of automation in data annotation and target contouring, RT planning including dose calculation but also dedicated tools for the quality assurance of fully automated workflows need to be developed. Furthermore, ethical aspects related to autonomous cancer treatments including definitions of dedicated checkpoints for human interaction to allow expert checks and stopping rules need to be defined and investigated.

      Declaration of Competing Interest

      The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: DT reports institutional collaborations with Elekta, Philips, TheraPanacea, Kaiku Health, Dr. Sennewald and PTW Freiburg. LK has no conflicts of interest to declare.

      References

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        • van Sörnsen de Koste J.R.
        • Palacios M.A.
        • Spoelstra F.O.B.
        • Slotman B.J.
        • Haasbeek C.J.A.
        • et al.
        Delivery of magnetic resonance-guided single-fraction stereotactic lung radiotherapy.
        Phys Imaging Radiat Oncol. 2020; 14: 17-23https://doi.org/10.1016/j.phro.2020.05.002
        View in Article
        • Palacios M.A.
        • Verheijen S.
        • Schneiders F.L.
        • Bohoudi O.
        • Slotman B.J.
        • Lagerwaard F.J.
        • et al.
        Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.
        Phys Imaging Radiat Oncol. 2022; 24: 76-81https://doi.org/10.1016/j.phro.2022.09.010
        View in Article
        • Draguet C.
        • Barragàn-Montero A.M.
        • Vera M.C.
        • Thomas M.
        • Populaire P.
        • Defraene G.
        • et al.
        Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer.
        Radiother Oncol. 2022; 176: 101-107https://doi.org/10.1016/j.radonc.2022.08.031
        View in Article
        • Brunenberg E.J.L.
        • Steinseifer I.K.
        • van den Bosch S.
        • Kaanders J.H.A.M.
        • Brouwer C.L.
        • Gooding M.J.
        • et al.
        External validation of deep learning-based contouring of head and neck organs at risk.
        Phys Imaging Radiat Oncol. 2020; 15: 8-15https://doi.org/10.1016/j.phro.2020.06.006
        View in Article
        • Thor M.
        • Iyer A.
        • Jiang J.
        • Apte A.
        • Veeraraghavan H.
        • Allgood N.B.
        • et al.
        Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy.
        Phys Imaging Radiat Oncol. 2021; 19: 96-101https://doi.org/10.1016/j.phro.2021.07.009
        View in Article
        • Henderson E.G.A.
        • Vasquez Osorio E.M.
        • van Herk M.
        • Green A.F.
        Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data.
        Phys Imaging Radiat Oncol. 2022; 22: 44-50https://doi.org/10.1016/j.phro.2022.04.003
        View in Article
        • Johnston N.
        • De Rycke J.
        • Lievens Y.
        • van Eijkeren M.
        • Aelterman J.
        • Vandersmissen E.
        • et al.
        Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk.
        Phys Imaging Radiat Oncol. 2022; 23: 109-117https://doi.org/10.1016/j.phro.2022.07.004
        View in Article
        • Xie Y.
        • Kang K.
        • Wang Y.
        • Khandekar M.J.
        • Willers H.
        • Keane F.K.
        • et al.
        Automated clinical target volume delineation using deep 3D neural networks in radiation therapy of Non-small Cell Lung Cancer.
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        View in Article
        • Fjellanger K.
        • Bolstad Hysing L.
        • Heijmen B.J.M.
        • Seime Pettersen H.E.
        • Sandvik I.M.
        • Husevåg Sulen T.
        • et al.
        Enhancing radiotherapy for locally advanced non-small cell lung cancer patients with iCE, a novel system for automated multi-criterial treatment planning including beam angle optimization.
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        • Marrazzo L.
        • Arilli C.
        • Pellegrini R.
        • Bonomo P.
        • Calusi S.
        • Talamonti C.
        • et al.
        Automated planning through robust templates and multicriterial optimization for lung VMAT SBRT of lung lesions.
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        View in Article
        • Esposito P.G.
        • Castriconi R.
        • Mangili P.
        • Broggi S.
        • Fodor A.
        • Pasetti M.
        • et al.
        Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy.
        Phys Imaging Radiat Oncol. 2022; 23: 54-59https://doi.org/10.1016/j.phro.2022.06.009
        View in Article
        • Arends S.R.S.
        • Savenije M.H.F.
        • Eppinga W.S.C.
        • van der Velden J.M.
        • van den Berg C.A.T.
        • Verhoeff J.J.C.
        Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases.
        Phys Imaging Radiat Oncol. 2022; 21: 42-47https://doi.org/10.1016/j.phro.2022.02.003
        View in Article
        • van de Sande D.
        • Sharabiani M.
        • Bluemink H.
        • Kneepkens E.
        • Bakx N.
        • Hagelaar E.
        • et al.
        Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer.
        Phys Imaging Radiat Oncol. 2021; 20: 111-116https://doi.org/10.1016/j.phro.2021.11.007
        View in Article
        • Xia X.
        • Wang J.
        • Li Y.
        • Peng J.
        • Fan J.
        • Zhang J.
        • et al.
        An artificial intelligence-based full-process solution for radiotherapy: A proof of concept study on rectal cancer.
        Front Oncol. 2021; 10: 616721https://doi.org/10.3389/fonc.2020.616721
        View in Article
        • Künzel L.A.
        • Nachbar M.
        • Hagmüller M.
        • Gani C.
        • Boeke S.
        • Zips D.
        • et al.
        First experience of autonomous, un-supervised treatment planning integrated in adaptive MR-guided radiotherapy and delivered to a patient with prostate cancer.
        Radiother Oncol. 2021; 159: 197-201https://doi.org/10.1016/j.radonc.2021.03.032
        View in Article
        • Künzel L.A.
        • Nachbar M.
        • Hagmüller M.
        • Gani C.
        • Boeke S.
        • Wegener D.
        • et al.
        Clinical evaluation of autonomous, unsupervised planning integrated in MR-guided radiotherapy for prostate cancer.
        Radiother Oncol. 2022; 168: 229-233https://doi.org/10.1016/j.radonc.2022.01.036
        View in Article
        • Jagt T.Z.
        • Janssen T.M.
        • Betgen A.
        • Wiersema L.
        • Verhage R.
        • Garritsen S.
        • et al.
        Benchmarking daily adaptation using fully automated radiotherapy treatment plan optimization for rectal cancer.
        Phys Imaging Radiat Oncol. 2022; 24: 7-13https://doi.org/10.1016/j.phro.2022.08.006
        View in Article
        • Brouwer C.L.
        • Dinkla A.M.
        • Vandewinckele L.
        • Crijns W.
        • Claessens M.
        • Verellen D.
        • et al.
        Machine learning applications in radiation oncology: Current use and needs to support clinical implementation.
        Phys Imaging Radiat Oncol. 2020; 16: 144-148https://doi.org/10.1016/j.phro.2020.11.002
        View in Article

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      Published online: November 07, 2022

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      DOI: https://doi.org/10.1016/j.phro.2022.11.006

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