Impact of treatment planning quality assurance software on volumetric-modulated arc therapy plans for prostate cancer patients

Sasaki, Motoharu; Nakaguchi, Yuji; Kamomae, Takeshi; Kajino, Akimi; Ikushima, Hitoshi

doi:10.1016/j.meddos.2021.03.013

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…Sasaki et al showed that treatment planning quality is affected by differences in the planner’s technique, even when the FDVH is referenced [ 10 ]. In this study, the same factors may be responsible for the fact that radiological technologists with less experience in treatment planning could not achieve dose reduction for lung-iGTV compared to other professionals and for the variation in the quality of the treatment plans.…”

Section: Discussionmentioning

confidence: 99%

Differences between professionals in treatment planning for patients with stage III lung cancer using treatment-planning QA software

Sato,

Sasaki,

Nakaguchi

et al. 2023

Rep Pract Oncol Radiother.

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Background The quality of treatment planning for stage III non-small cell lung cancer varies within and between facilities due to the different professions involved in planning. Dose estimation parameters were calculated using a feasibility dose-volume histogram (FDVH) implemented in the treatment planning quality assurance software PlanIQ. This study aimed to evaluate differences in treatment planning between occupations using manual FDVH-referenced treatment planning to identify their characteristics. Materials and methods The study included ten patients with stage III non-small cell lung cancer, and volumetric-modulated arc therapy was used as the treatment planning technique. Fifteen planners, comprising five radiation oncologists, five medical physicists, and five radiological technologists, developed treatment strategies after referring to the FDVH. Results Medical physicists had a higher mean dose at D98% of the planning target volume (PTV) and a lower mean dose at D2% of the PTV than those in other occupations. Medical physicists had the lowest irradiation lung volumes (V5 Gy and V13 Gy) compared to other professions, and radiation oncologists had the lowest V20 Gy and mean lung dose. Radiological technologists had the highest irradiation volumes for dose constraints at all indexes on the normal lung volume. Conclusions The quality of the treatment plans developed in this study differed between occupations due to their background expertise, even when an FDVH was used as a reference. Therefore, discussing and sharing knowledge and treatment planning techniques among professionals is essential to determine the optimal treatment plan for each facility and patient.

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Section: Discussionmentioning

confidence: 99%

Differences between professionals in treatment planning for patients with stage III lung cancer using treatment-planning QA software

Sato,

Sasaki,

Nakaguchi

et al. 2023

Rep Pract Oncol Radiother.

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“…Sasaki et al . showed that the plan quality may be improved by the commercial software that presents the limit point [ 15 ]. In addition, the clinical plans whose normalization was different from the clinical protocol were modified to match the clinical protocol without majorly changing the plan quality (from D95@PTV to D95@PTV1).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients

Kadoya,

Kimura,

Tozuka

et al. 2023

Journal of Radiation Research

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This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70–74 Gy/28–37 fr) at our hospital were used (n = 55 for training and n = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans—i.e. preDose and deliDose—with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04–6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00–15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer.

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“…However, this optimization process requires repeated trial and error by the treatment planner and a treatment planning time of several hours. Another drawback is that the quality of treatment planning is influenced by the planner’s skill level [ 60 ]. Knowledge-based treatment planning, a machine learning model, has been implemented in commercial treatment planning systems (TPSs) since 2014 and is widely used today [ 61 ].…”

Section: Radiotherapy Planningmentioning

confidence: 99%

Revolutionizing radiation therapy: the role of AI in clinical practice

Kawamura,

Kamomae,

Yanagawa

et al. 2023

Journal of Radiation Research

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This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist’s perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

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Impact of treatment planning quality assurance software on volumetric-modulated arc therapy plans for prostate cancer patients

Cited by 7 publications

References 13 publications

Differences between professionals in treatment planning for patients with stage III lung cancer using treatment-planning QA software

Differences between professionals in treatment planning for patients with stage III lung cancer using treatment-planning QA software

Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients

Revolutionizing radiation therapy: the role of AI in clinical practice

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