Automation in intensity modulated radiotherapy treatment planning—a review of recent innovations

Hussein, Mohammad; Heijmen, B.; Verellen, Dirk; Nisbet, Andrew

doi:10.1259/bjr.20180270

Cited by 180 publications

(202 citation statements)

References 210 publications

(245 reference statements)

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“…However, what can be ascertained is that results from this study, which demonstrate that automated planning is non-inferior to expert manual planning, are consistent with existing literature and supportive of Pareto navigation guided automated planning. Furthermore, in a recent review on automated planning by Hussein et al [37] only two out of the 81 identified evaluative studies were for complex pelvis treatments (SIB technique with nodal irradiation) [8,38]. Our work builds on this limited evidence base, providing further data in support of automation for even the most complex tumour sites.…”

Section: Discussionmentioning

confidence: 80%

Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer

Wheeler

Chu

Holmes

et al. 2019

Radiotherapy and Oncology

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a b s t r a c tBackground and purpose: Current automated planning methods do not allow for the intuitive exploration of clinical trade-offs during calibration. Recently a novel automated planning solution, which is calibrated using Pareto navigation principles, has been developed to address this issue. The purpose of this work was to clinically validate the solution for prostate cancer patients with and without elective nodal irradiation. Materials and methods: For 40 randomly selected patients (20 prostate and seminal vesicles (PSV) and 20 prostate and pelvic nodes (PPN)) automatically generated volumetric modulated arc therapy plans (VMAT Auto ) were compared against plans created by expert dosimetrists under clinical conditions (VMAT Clinical ) and no time pressures (VMAT Ideal ). Plans were compared through quantitative comparison of dosimetric parameters and blind review by an oncologist. Results: Upon blind review 39/40 and 33/40 VMAT Auto plans were considered preferable or equal to VMAT Clinical and VMAT Ideal respectively, with all deemed clinically acceptable. Dosimetrically, VMAT Auto , VMAT Clinical and VMAT Ideal were similar, with observed differences generally of low clinical significance. Compared to VMAT Clinical , VMAT Auto reduced hands-on planning time by 94% and 79% for PSV and PPN respectively. Total planning time was significantly reduced from 22.2 mins to 14.0 mins for PSV, with no significant reduction observed for PPN. Conclusions: A novel automated planning solution has been evaluated, whose Pareto navigation based calibration enabled clinical decision-making on trade-off balancing to be intuitively incorporated into automated protocols. It was successfully applied to two sites of differing complexity and robustly generated high quality plans in an efficient manner. Ó 2019 The Authors. Published by Elsevier B.V. Radiotherapy and Oncology 141 (2019) 220-226 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) treatment plan generation is a complex process, traditionally performed manually by medical physicists or specialist dosimetrists. Manual methods can be time consuming and dependent on the treatment planner's experience [1]. A solution to this problem is automated planning, where high quality plans are generated autonomously with minimal operator interaction [2][3][4][5][6][7][8][9].A key challenge in automated planning is incorporating treatment planners' or oncologists' clinical experience and decisionmaking within the autonomous process. A number of different methods have been employed: knowledge based planning (KBP) utilises databases of previous clinical plans to correlate the relationship between patient geometry and the resultant dose distribution, which then informs the optimisation of new patients [3,[10][11][12][13]; sequential e-constraint planning (ec) optimises plans based on a list of clinically prioritised goals [2,7,8,[14][15...

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Section: Discussionmentioning

confidence: 80%

Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer

Wheeler

Chu

Holmes

et al. 2019

Radiotherapy and Oncology

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“…These treatment plans may therefore not reflect treatment plans produced in clinical practice, as less detailed feedback or discussion of treatment plans is possible and plans may therefore be suboptimal. Automated treatment planning techniques, for example knowledge-based treatment planning, protocolbased automatic iterative optimization or multicriteria optimization, could be used to determine whether a treatment plan can be further optimized [14].…”

Section: Discussionmentioning

confidence: 99%

Radiotherapy quality assurance for mesorectum treatment planning within the multi-center phase II STAR-TReC trial: Dutch results

et al. 2020

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Background: The STAR-TReC trial is an international multi-center, randomized, phase II study assessing the feasibility of short-course radiotherapy or long-course chemoradiotherapy as an alternative to total mesorectal excision surgery. A new target volume is used for both (chemo)radiotherapy arms which includes only the mesorectum. The treatment planning QA revealed substantial variation in dose to organs at risk (OAR) between centers. Therefore, the aim of this study was to determine the treatment plan variability in terms of dose to OAR and assess the effect of a national study group meeting on the quality and variability of treatment plans for mesorectum-only planning for rectal cancer. Methods: Eight centers produced 25 × 2 Gy treatment plans for five cases. The OAR were the bowel cavity, bladder and femoral heads. A study group meeting for the participating centers was organized to discuss the planning results. At the meeting, the values of the treatment plan DVH parameters were distributed among centers so that results could be compared. Subsequently, the centers were invited to perform replanning if they considered this to be necessary. Results: All treatment plans, both initial planning and replanning, fulfilled the target constraints. Dose to OAR varied considerably for the initial planning, especially for dose levels below 20 Gy, indicating that there was room for trade-offs between the defined OAR. Five centers performed replanning for all cases. One center did not perform replanning at all and two centers performed replanning on two and three cases, respectively. On average, replanning reduced the bowel cavity V20Gy by 12.6%, bowel cavity V10Gy by 22.0%, bladder V35Gy by 14.7% and bladder V10Gy by 10.8%. In 26/30 replanned cases the V10Gy of both the bowel cavity and bladder was lower, indicating an overall lower dose to these OAR instead of a different trade-off. In addition, the bowel cavity V10Gy and V20Gy showed more similarity between centers.Conclusions: Dose to OAR varied considerably between centers, especially for dose levels below 20 Gy. The study group meeting and the distribution of the initial planning results among centers resulted in lower dose to the defined OAR and reduced variability between centers after replanning.

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“…Artificial intelligence (AI) techniques will rapidly find relevant and extensive applications in radiotherapy (Thompson et al, 2018). An important example is the ability of AI in supporting planners to generate automated solutions for treatment planning optimization that are integrating (in part replacing) and improving the traditional, manually optimized, planning (Hussein et al, 2018). AI is also particularly promising to support online treatment planning and adaptive radiotherapy (Boon et al, 2018).…”

Section: Artificial Intelligence and Big Datamentioning

confidence: 99%

Technology‐driven research for radiotherapy innovation

et al. 2020

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Technology has a pivotal role in the continuous development of radiotherapy. The long road toward modern ‘high‐tech’ radiation oncology has been studded with discoveries and technological innovations that resulted from the interaction of various disciplines. In the last decades, a dramatic technology‐driven revolution has hugely improved the capability of accurately and safely delivering complex‐shaped dose distributions. This has contributed to many clinical improvements, such as the successful management of lung cancer and oligometastatic disease through stereotactic body radiotherapy. Technology‐driven research is an active and lively field with promising potential in several domains, including image guidance, adaptive radiotherapy, integration of artificial intelligence, heavy‐particle therapy, and ‘flash’ ultra‐high dose‐rate radiotherapy. The evolution toward personalized Oncology will deeply influence technology‐driven research, aiming to integrate predictive models and omics analyses into fast and efficient solutions to deliver the best treatment for each single patient. Personalized radiation oncology will need affordable technological solutions for middle‐/low‐income countries, as these are expected to experience the highest increase of cancer incidence and mortality. Moreover, technology solutions for automation of commissioning, quality assurance, safety tests, image segmentation, and plan optimization will be required. Although a large fraction of cancer patients receive radiotherapy, this is certainly not reflected in the worldwide budget for radiotherapy research. Differently from the pharmaceutical companies‐driven research, resources for research in radiotherapy are highly limited to equipment vendors, who can, in turn, initiate a limited number of collaborations with academic research centers. Thus, enhancement of investments in technology‐driven radiotherapy research via public funds, national governments, and the European Union would have a crucial societal impact. It would allow for radiotherapy to further strengthen its role as a highly effective and cost‐efficient cancer treatment modality, and it could facilitate a rapid and equalitarian large‐scale transfer of technology to clinic, with direct impact on patient care.

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Automation in intensity modulated radiotherapy treatment planning—a review of recent innovations

Cited by 180 publications

References 210 publications

Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer

Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer

Radiotherapy quality assurance for mesorectum treatment planning within the multi-center phase II STAR-TReC trial: Dutch results

Technology‐driven research for radiotherapy innovation

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