Learn to optimize for automatic proton PBS treatment planning for H&N cancers

• URL: http://arxiv.org/abs/2508.11085v1
• Date: Thu, 14 Aug 2025 21:50:31 GMT
• Title: Learn to optimize for automatic proton PBS treatment planning for H&N cancers
• Authors: Qingqing Wang, Liqiang Xiao, Chang Chang,
• Abstract summary: We propose a data-driven inverse and integrate it into a PPO-based automatic treatment planning framework.<n>The PPO framework functions as an outer-loop virtual planner, autonomously adjusting objective parameters through a policy network.<n>Compared with L-BFGSB, our L2O-based inverse improves the effectiveness and efficiency by 22.97% and 36.41%, respectively.
• Score: 3.384581246727367
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Proton PBS treatment planning for H&N cancers involves numerous conflicting objectives, requiring significant effort from human planners to balance and satisfy multiple clinical goals during planning. To achieve this, experience-demanding objective parameter adjustment and computationally expensive inverse optimization are performed iteratively. Extensive efforts have been made to automatically adjust objective parameters, but the most time-consuming component, i.e., inverse optimization, still relies heavily on theory-driven approaches. We propose a data-driven inverse optimizer and integrate it into a PPO-based automatic treatment planning framework to automatically generate high-quality plans within a clinical acceptable planning time. The inverse optimizer is a L2O method that predicts update steps by learning from the task-specific data distribution. For the first time, we integrate techniques designed for long-context processing, originally developed for LLMs, into a Transformer-based L2O framework to address the scalability issue of existing L2O methods. The PPO framework functions as an outer-loop virtual planner, autonomously adjusting objective parameters through a policy network, and the dose predictor is used to initialize objective parameters. The inner-loop L2O inverse optimizer computes machine-deliverable MU values based on objectives refined by the PPO policy network. 97 patients are collected in this study, and compared with L-BFGSB, our L2O-based inverse optimizer improves the effectiveness and efficiency by 22.97% and 36.41%, respectively. In conjunction with the PPO-based learned virtual planner, plans generated by our framework within an average of 2.55 hours show improved or comparable OAR sparing with superior target coverage for patients with different prescription dose levels, number of target volumes, beam angles, etc., compared with human-generated plans.

Related papers

• Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
  Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
  arXiv  Detail & Related papers  (2026-03-04T14:48:53Z)
• Zero-Shot Large Language Model Agents for Fully Automated Radiotherapy Treatment Planning [14.814676057920067]
  A large language model (LLM)-based agent navigates inverse treatment planning for intensity-modulated radiation therapy (IMRT)<n>The agent's decision-making process is informed by current observations and previous optimization attempts and evaluations.<n>This study demonstrates the feasibility of a zero-shot, LLM-driven workflow for automated IMRT treatment planning in a commercial TPS.
  arXiv  Detail & Related papers  (2025-10-12T19:21:21Z)
• An Iterative LLM Framework for SIBT utilizing RAG-based Adaptive Weight Optimization [11.168299220031662]
  This study proposes an adaptive weight optimization framework for SIBT planning, driven by large language models (LLMs)<n>A clinical knowledge base, constructed and queried via retrieval-augmented generation (RAG), enhances the model's domain-specific reasoning.<n>The proposed method was validated on 23 patient cases, showing that the LLM-assisted approach produces plans that are comparable to or exceeding clinically approved and fixed-weight plans.
  arXiv  Detail & Related papers  (2025-09-10T08:54:16Z)
• Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning [3.9838929530763076]
  The objective of this study is to develop a fully automated HDR brachytherapy planning framework.<n>We propose a hierarchical two-stage autoplanning framework.<n>For the unseen test patients, the RL-based automated planning method achieved an average score of 93.89%, outperforming the clinical plans which averaged 91.86%.
  arXiv  Detail & Related papers  (2025-06-13T17:07:30Z)
• Accelerating RL for LLM Reasoning with Optimal Advantage Regression [52.0792918455501]
  We propose a novel two-stage policy optimization framework that directly approximates the optimal advantage function.<n>$A$*-PO achieves competitive performance across a wide range of mathematical reasoning benchmarks.<n>It reduces training time by up to 2$times$ and peak memory usage by over 30% compared to PPO, GRPO, and REBEL.
  arXiv  Detail & Related papers  (2025-05-27T03:58:50Z)
• Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent [2.1986172572830096]
  Dose Optimization Language Agent (DOLA) is an autonomous large language model (LLM)-based agent designed for optimizing radiotherapy treatment plans.<n>DOLA integrates the LLaMa3.1 LLM directly with a commercial treatment planning system.<n> operating entirely within secure local infrastructure.
  arXiv  Detail & Related papers  (2025-03-21T22:01:19Z)
• DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization [53.27954325490941]
  Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives.<n>This research introduces a novel reinforcement learning algorithm to finetune a drug optimization LLM-based generative model.
  arXiv  Detail & Related papers  (2025-02-11T04:00:21Z)
• Hierarchical Preference Optimization: Learning to achieve goals via feasible subgoals prediction [71.81851971324187]
  This work introduces Hierarchical Preference Optimization (HPO), a novel approach to hierarchical reinforcement learning (HRL) HPO addresses non-stationarity and infeasible subgoal generation issues when solving complex robotic control tasks. Experiments on challenging robotic navigation and manipulation tasks demonstrate impressive performance of HPO, where it shows an improvement of up to 35% over the baselines.
  arXiv  Detail & Related papers  (2024-11-01T04:58:40Z)
• Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting [56.92178753201331]
  We propose the Observation-Aware Spectral (OAS) estimation technique, which enables the POMDP parameters to be learned from samples collected using a belief-based policy. We show the consistency of the OAS procedure, and we prove a regret guarantee of order $mathcalO(sqrtT log(T)$ for the proposed OAS-UCRL algorithm.
  arXiv  Detail & Related papers  (2024-10-02T08:46:34Z)
• Automating proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning [0.7519872646378836]
  We propose an automatic treatment planning model using the proximal policy optimization (PPO) algorithm and a dose distribution-based reward function. A set of empirical rules is used to create auxiliary planning structures from target volumes and organs-at-risk. A decision-making policy network trained using PPO is developed to iteratively adjust the involved planning objective parameters in a continuous action space.
  arXiv  Detail & Related papers  (2024-09-17T22:01:56Z)
• Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation [73.04087903322237]
  We formulate throughput optimisation as Continual Reinforcement Learning of control policies. Simulation results suggest that the proposed system is able to shorten the end-to-end deployment lead-time by two-fold.
  arXiv  Detail & Related papers  (2024-04-30T11:23:31Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts.<n>We identify two pivotal factors in model parameter learning: update direction and update method.<n>We develop a capable Gradient-inspired Prompt-based GPO.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)
• Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs [27.41101006357176]
  In this work, we take a minorization-maximization perspective to iteratively optimize the. w.r.t. a locally tight lower-bounded objective. This novel formulation of learning as iterative lower bound optimization (ILBO) is particularly appealing because (i) each step is structurally easier to optimize than the overall objective. Empirical evaluation confirms that ILBO is significantly more sample-efficient than the state-of-the-art planner.
  arXiv  Detail & Related papers  (2022-03-23T19:06:16Z)
• Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
  Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. BaBSim.Hospital is a tool for capacity planning based on discrete event simulation. We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
  arXiv  Detail & Related papers  (2021-05-16T12:38:35Z)
• A feasibility study of a hyperparameter tuning approach to automated inverse planning in radiotherapy [68.8204255655161]
  The purpose of this study is to automate the inverse planning process to reduce active planning time while maintaining plan quality. We investigated the impact of the choice of dose parameters, random and Bayesian search methods, and utility function form on planning time and plan quality. Using 100 samples was found to produce satisfactory plan quality, and the average planning time was 2.3 hours.
  arXiv  Detail & Related papers  (2021-05-14T18:37:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.