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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
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  • Daniela Thorwarth
    Correspondence
    Corresponding author.
    Affiliations
    Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
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Open AccessPublished:November 07, 2022DOI:https://doi.org/10.1016/j.phro.2022.11.006
      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.
      ]. 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.
      ] 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.
      ]. 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.
      ]. 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.
      ] 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.
      ,
      • 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.
      ,
      • 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.
      ,
      • 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.
      ,
      • 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.
      ] 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.
      ,
      • 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.
      ,
      • 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.
      ,
      • 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.
      ,
      • 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.
      ] 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.
      ] 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.
      ] 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.
      ,
      • 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.
      ,
      • 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.
      ,
      • 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.
      ,
      • 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.
      ]. 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.
      ] 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.
      ,
      • 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.
      ] 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.
      ] 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.
      ,
      • 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.
      ,
      • 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.
      ,
      • 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.
      ]. 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.
      ] 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.
      ], 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.

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