Actor critic with experience replay‐based automatic treatment planning for prostate cancer intensity modulated radiotherapy Article Swipe
Mohammed Burhan Abrar
,
Parvat Sapkota
,
Damon Sprouts
,
Xun Jia
,
Yujie Chi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/mp.17915
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/mp.17915
Background Achieving highly efficient treatment planning in intensity‐modulated radiotherapy (IMRT) is challenging due to the complex interactions between radiation beams and the human body. The introduction of artificial intelligence (AI) has automated treatment planning, significantly improving efficiency. However, existing automatic treatment planning agents often rely on supervised or unsupervised AI models that require large datasets of high‐quality patient data for training. Additionally, these networks are generally not universally applicable across patient cases from different institutions and can be vulnerable to adversarial attacks. Deep reinforcement learning (DRL), which mimics the trial‐and‐error process used by human planners, offers a promising new approach to address these challenges. Purpose This work aims to develop a stochastic policy‐based DRL agent for automatic treatment planning that facilitates effective training with limited datasets, universal applicability across diverse patient datasets, and robust performance under adversarial attacks. Methods We employ an actor–critic with experience replay (ACER) architecture to develop the automatic treatment planning agent. This agent operates the treatment planning system (TPS) for inverse treatment planning by automatically tuning treatment planning parameters (TPPs). We use prostate cancer IMRT patient cases as our testbed, which includes one target and two organs at risk (OARs), along with 18 discrete TPP tuning actions. The network takes dose–volume histograms (DVHs) as input and outputs a policy for effective TPP tuning, accompanied by an evaluation function for that policy. Training utilizes DVHs from treatment plans generated by an in‐house TPS under randomized TPPs for a single patient case, with validation conducted on two other independent cases. Both online asynchronous learning and offline, sample‐efficient experience replay methods are employed to update the network parameters. After training, six groups, comprising more than 300 initial treatment plans drawn from three datasets, were used for testing. These groups have beam and anatomical configurations distinct from those of the training case. The ProKnow scoring system for prostate cancer IMRT, with a maximum score of 9, is used to evaluate plan quality. The robustness of the network is further assessed through adversarial attacks using the fast gradient sign method (FGSM). Results Despite being trained on treatment plans from a single patient case, the network converges efficiently when validated on two independent cases. For testing performance, the mean standard deviation of the plan scores across all test cases before ACER‐based treatment planning is . After implementing ACER‐based treatment planning, of the cases achieve a perfect score of 9, with only scoring between 8 and 9, and no cases being below 7. The corresponding mean standard deviation is . This performance highlights the ACER agent's high generality across patient data from various sources. Further analysis indicates that the ACER agent effectively prioritizes leading reasonable TPP tuning actions over obviously unsuitable ones by several orders of magnitude, showing its efficacy. Additionally, results from FGSM attacks demonstrate that the ACER‐based agent remains comparatively robust against various levels of perturbation. Conclusions We successfully trained a DRL agent using the ACER technique for high‐quality treatment planning in prostate cancer IMRT. It achieves high generality across diverse patient datasets and exhibits high robustness against adversarial attacks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/mp.17915
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17915
- OA Status
- hybrid
- Cited By
- 2
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410939552
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410939552Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/mp.17915Digital Object Identifier
- Title
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Actor critic with experience replay‐based automatic treatment planning for prostate cancer intensity modulated radiotherapyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-31Full publication date if available
- Authors
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Mohammed Burhan Abrar, Parvat Sapkota, Damon Sprouts, Xun Jia, Yujie ChiList of authors in order
- Landing page
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https://doi.org/10.1002/mp.17915Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17915Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17915Direct OA link when available
- Concepts
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Reinforcement learning, Computer science, Radiation treatment planning, Artificial intelligence, Machine learning, Radiation therapy, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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