Improving the efficiency of breast radiotherapy treatment planning using a semi‐automated approach

Mitchell, Robert A.; Wai, Philip Y.; Colgan, R.; Kirby, Anna M.; Donovan, Ellen M.

doi:10.1002/acm2.12006

Cited by 27 publications

(4 citation statements)

References 20 publications

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“…[1][2][3][4][5][6][7][8]25 While there have been reports on the automation of breast irradiation planning, not all of them incorporate the automatic insertion of tangential fields into the process. 6,8,25 Moreover, a one-size-fits-all approach, not personalized, often employed in these plans can lead to compromised target volume coverage, higher doses to organs at risk, or constraints on the inclusion of tangential fields. 11,26 Purdie et al automated the planning process for 158 breast cancer patients undergoing whole-breast IMRT within the Pinnacle Treatment Planning System.…”

Section: Discussionmentioning

confidence: 99%

“…The literature discusses several automation tools utilizing various Treatment Planning Systems 1–8,25 . While there have been reports on the automation of breast irradiation planning, not all of them incorporate the automatic insertion of tangential fields into the process 6,8,25 .…”

Section: Discussionmentioning

confidence: 99%

“… 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 25 While there have been reports on the automation of breast irradiation planning, not all of them incorporate the automatic insertion of tangential fields into the process. 6 , 8 , 25 Moreover, a one‐size‐fits‐all approach, not personalized, often employed in these plans can lead to compromised target volume coverage, higher doses to organs at risk, or constraints on the inclusion of tangential fields. 11 , 26 …”

Section: Discussionmentioning

confidence: 99%

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Automated treatment planning for whole breast irradiation with individualized tangential IMRT fields

Zaratim,

dos Reis,

dos Santos

et al. 2024

J Applied Clin Med Phys

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PurposesThis study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics.Material and MethodsWe retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System. We employed three beam insertion geometries and three optimization strategies, resulting in 3600 plans, each delivering a 40.05 Gy dose in 15 fractions. Gantry angles in the tangent fields were selected based on a criterion involving the minimum intersection area between the Planning Target Volume (PTV) and the ipsilateral lung in the Beam's Eye View projection. ESAPI was also used to gather patient anatomical data, serving as input for Random Forest models to select the optimal plan. The Random Forest classification considered both beam insertion geometry and optimization strategy. Dosimetric data were evaluated in accordance with the Radiation Therapy Oncology Group (RTOG) 1005 protocol.ResultsOverall, all approaches generated high‐quality plans, with approximately 94% meeting the acceptable dose criteria for organs at risk and/or target coverage as defined by RTOG guidelines. Average automated plan generation time ranged from 6 min and 37 s to 9 min and 22 s, with the mean time increasing with additional fields. The Random Forest approach did not successfully enable automatic planning strategy selection. Instead, our automated planning system allows users to choose from the tested geometry and strategy options.ConclusionsAlthough our attempt to correlate patient anatomical features with planning strategy using machine learning tools was unsuccessful, the resulting dosimetric outcomes proved satisfactory. Our algorithm consistently produced high‐quality plans, offering significant time and efficiency advantages.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Automated treatment planning for whole breast irradiation with individualized tangential IMRT fields

Zaratim,

dos Reis,

dos Santos

et al. 2024

J Applied Clin Med Phys

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“…Above all, the time saved for planners will be appreciable [27]. If each lung cancer plan saved 70.5 ± 13.2 minutes, then 50 lung cancer plans would be able to save 2 to 3 working days.…”

Section: Discussionmentioning

confidence: 99%

Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer

Wang,

Fan,

et al. 2024

Preprint

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Background and purpose The purpose of this study is to investigate the clinical application and assessment of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer patients.Materials and methods We developed a deep learning model for predicting patient-specific dose that was trained and validated on a dataset of 235 lung cancer patients, and the model was integrated into clinical workflow to assist planners in generating treatment plans. We retrospectively selected and recovered additional 50 clinically treated lung cancer patients’ manual volumetric modulated arc therapy (VMAT) plans with different target volumes and different treatment patterns. Subsequently, automatic plans were generated for each of these patients. Both automatic and manual plans were subsequently compared in terms of overall plan quality metric (PQM), target coverage and homogeneity, organ at risk (OAR) sparing, monitor units (MUs), and planning time. Additionally, qualitative reviews of automatic and manual plans were implemented by four expert reviewers to assess the clinical applicability of DL-assisted automatic plans.Results The average PQM score was 40.7 ± 13.1 for manual plans and 40.8 ± 13.5 for automatic plans, and they had equivalent overall plan quality. The targets coverage and homogeneity of the automatic plans were considered equivalent or superior when compared to manual plans. Both plans had their own advantages in OAR sparing, such as better sparing of lung for manual plans and better sparing of heart for automatic plans. It is worth to note that the average planning time of automatic plans was reduced from 103.1 ± 18.5 minutes to 32.6 ± 5.3 minutes (P<0.001) and the MUs were reduced from 789.9 ± 234.3 to 692.5 ± 210.7 (P<0.001). In qualitative evaluation, automatic plans were deemed to be clinically acceptable for treatment in 88% of reviews (176/200), and all were accepted after fine tuning. Most expert reviews indicated a preference for equivalence between automatic and manual plans when making their selection.Conclusion The DL-assisted lung cancer plans demonstrated comparable or superior quality to manual plans, improved planning and treatment efficiency, and significantly reduced planning time and MUs. It has the potential to enhance the workflow of radiotherapy departments, ultimately providing tangible benefit to lung cancer patients.

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Application of Deep Learning in Radiation Therapy

Rawat¹,

Singh²,

Alam³

et al. 2022

Deep Learning for Targeted Treatments

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Improving the efficiency of breast radiotherapy treatment planning using a semi‐automated approach

Cited by 27 publications

References 20 publications

Automated treatment planning for whole breast irradiation with individualized tangential IMRT fields

Automated treatment planning for whole breast irradiation with individualized tangential IMRT fields

Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer

Application of Deep Learning in Radiation Therapy

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