Advanced treatment planning strategies to enhance quality and efficiency of radiotherapy

Cozzi, Luca; Heijmen, B.; Muren, L.P.

doi:10.1016/j.phro.2019.09.002

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…The evolution of computer hard-and software allowed implementation of advanced algorithms for dose calculation and optimisation, and facilitated the introduction of many complex treatment techniques [3][4][5][6]. Radiotherapy treatment planning is currently in an exciting era with novel developments in, for example, automated planning, robust planning, online adaptive radio-therapy, knowledge-based planning and plan quality assessment methods [7][8][9][10][11].…”

Section: Motivation and Aim Of The Rating Frameworkmentioning

confidence: 99%

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Hansen

Crijns

Hussein

et al. 2020

Radiotherapy and Oncology

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Section: Motivation and Aim Of The Rating Frameworkmentioning

confidence: 99%

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Hansen

Crijns

Hussein

et al. 2020

Radiotherapy and Oncology

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“…The creation of optimised RT plans is an iterative process involving experts in radiotherapy such as the radiation oncologist, medical physicists and dosimetrists [3] . Automated planning which incorporates dose prediction has been demonstrated as advantageous in terms of time efficiency and plan quality [4] , [5] , while also has the potential of reducing inter-patient and inter-institution variation [6] .…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based versus deep learning based treatment planning for breast radiotherapy

Portik,

Clementel,

Krayenbühl

et al. 2024

Physics and Imaging in Radiation Oncology

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“…Technical developments in external beam radiotherapy (EBRT), e.g., replacement of 3D-conformal radiotherapy (3DCRT) by intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) (2)(3)(4), and improvements in image guidance (5)(6)(7), have significantly improved treatment outcome and/or reduced radiation induced side effects in a variety of treatment sites. Recently, developments in automation of treatment planning have further enhanced opportunities for generation of high quality treatment plans (8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity—A Proof of Principle Study for Prostate Cancer

et al. 2020

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Background: Currently, radiation-oncologists generally evaluate a single treatment plan for each patient that is possibly adapted by the planner prior to final approval. There is no systematic exploration of patient-specific trade-offs between planning aims, using a set of treatment plans with a-priori defined (slightly) different balances. To this purpose, we developed an automated workflow and explored its use for prostate cancer. Materials and Methods: For each of the 50 study patients, seven plans were generated, including the so-called clinical plan, with currently clinically desired ≥99% dose coverage for the low-dose planning target volume (PTV Low). The six other plans were generated with different, reduced levels of PTV Low coverage, aiming at reductions in rectum dose and consequently in predicted grade≥2 late gastro-intestinal (GI) normal tissue complication probabilities (NTCPs), while keeping other dosimetric differences small. The applied NTCP model included diabetes as a non-dosimetric predictor. All plans were generated with a clinically applied, in-house developed algorithm for automated multi-criterial plan generation. Results: With diabetes, the average NTCP reduced from 24.9 ± 4.5% for ≥99% PTV Low coverage to 17.3 ± 2.6% for 90%, approaching the NTCP (15.4 ± 3.0%) without diabetes and full PTV Low coverage. Apart from intended differences in PTV Low coverage and rectum dose, other differences between the clinical plan and the six alternatives were indeed minor. Obtained NTCP reductions were highly patient-specific (ranging from 14.4 to 0.1%), depending on patient anatomy. Even for patients with equal NTCPs in the clinical plan, large differences were found in NTCP reductions. Conclusions: A clinically feasible workflow has been proposed for systematic exploration of patient-specific trade-offs between various treatment aims. For each patient, automated planning is used to generate a limited set of treatment plans with Bijman et al. Automated Planning for Trade-Off Exploration well-defined variations in the balances between the aims. For prostate cancer, trade-offs between PTV Low coverage and predicted GI NTCP were explored. With relatively small coverage reductions, significant NTCP reductions could be obtained, strongly depending on patient anatomy. Coverage reductions could also make up for enhanced NTCPs related to diabetes as co-morbidity, again dependent on the patient. The proposed system can play an important role in further personalization of patient care.

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Advanced treatment planning strategies to enhance quality and efficiency of radiotherapy

Cited by 3 publications

References 27 publications

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Knowledge-based versus deep learning based treatment planning for breast radiotherapy

Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity—A Proof of Principle Study for Prostate Cancer

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