Reduction of heart and lung normal tissue complication probability using automatic beam angle optimization and more generic optimization objectives for breast radiotherapy

Bakx, Nienke; Bluemink, Hanneke; Hagelaar, E.; Leer, Jorien van der; Sangen, Maurice van der; Theuws, Jacqueline; Hurkmans, Coen

doi:10.1016/j.phro.2021.04.002

Cited by 5 publications

(9 citation statements)

References 11 publications

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“…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%

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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%

“…The development of systems for automating radiation treatment planning has witnessed substantial growth in recent years 1–8 . These systems possess the capacity to alleviate the planner's workload, foster planning standardization, and reduce dependence on the planner's experience.…”

Section: Introductionmentioning

confidence: 99%

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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“…Alguns estudos sobre automatização do planejamento de mama usaram uma abordagem geométrica, semelhante às usadas nos Script_PTV e Script_PULMAO (13,14), e também uma abordagem dosimétrica (15). Em um trabalho semelhante, Lin et al desenvolveram um planejamento automatizado com o ESAPI para o tratamento de IMRT para a mama esquerda.…”

Section: Discussionunclassified

Automatização da Determinação dos Campos na Radioterapia de Mama: Desenvolvimento e Comparação entre Dois Algoritmos

Rivelli Rodrigues Zaratim,

Bicudo dos Santos,

Gomes dos Reis

et al. 2023

Rev Bras Fis Med

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Na radioterapia do câncer de mama, em que se utilizam campos tangentes, a escolha manual da angulação do gantry leva em consideração o volume de tecido sadio irradiado. O objetivo deste trabalho foi desenvolver, validar e comparar dois algoritmos para automatizar a seleção dos ângulos dos campos tangentes na radioterapia do câncer de mama. Um dos algoritmos escolhe as angulações das tangentes através da determinação da menor área da projeção do volume alvo de tratamento na projeção do feixe (Script_PTV); o outro (Script_PULMAO) considera o ângulo que possui a menor área de intersecção do volume alvo com o pulmão ipsilateral na projeção do feixe. Essas áreas foram calculadas com o Método de Gauss. Foram analisados retrospectivamente 55 planejamentos para o tratamento de câncer de mama sem irradiação nodal, sendo 31 para o lado esquerdo e 24 para o lado direito. Os desvios absolutos entre os ângulos obtidos manualmente e os Script_PTV e Script_PULMAO foram diferentes (z = -5,27, p < 0,001). Para o Script_ PTV a mediana, o primeiro e terceiro quartil foram, respectivamente, 2,50, 1,00 e 4,50 graus. Para o Script_ PULMAO esses resultados foram, respectivamente, 1,35, 0,50 e 2,50 graus. A proporção de incidências na mama contralateral no grupo cujos desvios absolutos foram maiores ou igual a 4 graus foi maior do que no grupo em que as diferenças foram inferiores a 4 graus (p < 0,001). Isso indica a relevância da ponderação da incidência dos feixes na mama contralateral na determinação dos ângulos de gantry. Além disso, as menores diferenças e dispersões dos resultados do Script_PULMAO, em relação à determinação manual das angulações do gantry, sugerem que esta abordagem é mais adequada geometricamente do que a do Script_PTV.

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“…The isocenter was positioned in the center of the PTV, unless this would lead to a collision with the gantry, in which case the isocenter was moved inwards. The tangential beams were initialized at 130 and 310 degrees and subsequently, automatic beam angle optimization was performed in the 3D-CRT module of the TPS aimed at minimizing the dose to the heart, lungs and contralateral breast as previously described by Bakx et al [ 16 ]. Using the same initial beam setup and beam energy, three plans were made: a manual plan and two model-based plans.…”

Section: Methodsmentioning

confidence: 99%

Clinical evaluation of two AI models for automated breast cancer plan generation

et al. 2022

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Background Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists. Methods For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time. Results The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model. Conclusions Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable.

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Reduction of heart and lung normal tissue complication probability using automatic beam angle optimization and more generic optimization objectives for breast radiotherapy

Cited by 5 publications

References 11 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

Automatização da Determinação dos Campos na Radioterapia de Mama: Desenvolvimento e Comparação entre Dois Algoritmos

Clinical evaluation of two AI models for automated breast cancer plan generation

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