Adaptive Chemotherapy Control under Tumor Heterogeneity via Reinforcement Learning

• URL: http://arxiv.org/abs/2609.12264v1
• Date: Thu, 10 Sep 2026 22:42:43 GMT
• Title: Adaptive Chemotherapy Control under Tumor Heterogeneity via Reinforcement Learning
• Abstract summary: We develop and compare closed-loop deep reinforcement learning (DRL) dosing policies with continuous (TD3) and discrete (DQN) action spaces trained on a high-dimensional heterogeneous tumor model.<n>Overall, the results show that simulation-trained DRL can learn state-dependent feedback dosing policies that complement open-loop optimal control benchmarks.
• Score: 4.00700063243877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing effective chemotherapy regimens is hindered by tumor heterogeneity and drug resistance, which complicate the deployment of patient-specific model-based optimal control across diverse populations. We develop and compare closed-loop deep reinforcement learning (DRL) dosing policies with continuous (TD3) and discrete (DQN) action spaces trained on a high-dimensional heterogeneous tumor model. The DRL policies are benchmarked against a Pontryagin's Maximum Principle (PMP)-derived open-loop benchmark. We assess generalization under parametric heterogeneity using a 100-patient virtual cohort with plus or minus 10 percent uniform perturbations in growth and drug-sensitivity parameters. Across this cohort, TD3 achieves higher average tumor reduction, while DQN yields tighter inter-patient dosing consistency, revealing a clear efficacy-consistency trade-off in this study. Our simulations assume full observation of all tumor subpopulations; translation to sparse and noisy clinical measurements will require partial-observability formulations and/or state estimation. Overall, the results show that simulation-trained DRL can learn state-dependent feedback dosing policies that complement open-loop optimal control benchmarks.

Related papers

• AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling [13.239769357659412]
  We propose an AI-augmented adaptive digital twin (DT) framework for brain tumor evolution prediction and treatment scheduling.<n>The framework integrates an interpretable reaction--diffusion (RD) model, a 3D residual learning module for model-form correction, and patient-specific DT updating.
  arXiv  Detail & Related papers  (2026-07-15T14:26:14Z)
• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability [6.650703382349715]
  We investigate whether memory-augmented policies can improve chemotherapy control under partial observability.<n>Across ten random seeds, recurrence yields modest benefit under full observability but substantially stronger and more stable performance under partial observability.
  arXiv  Detail & Related papers  (2026-05-04T13:00:55Z)
• Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering [58.85421622061983]
  We propose StsPatient for the fine-grained simulation of cognitively impaired patients.<n>We capture domain-specific features by extracting steering vectors from contrastive pairs of instructions and responses.<n>StsPatient significantly outperforms baselines in both clinical authenticity and severity controllability.
  arXiv  Detail & Related papers  (2026-04-14T02:37:46Z)
• Brain-WM: Brain Glioblastoma World Model [56.45393372490125]
  We present Brain-WM, a pioneering GBM world model that unifies next-step treatment prediction and future MRI generation.<n>Brain-WM encodes latent dynamics into a shared latent space for joint autoregressive treatment prediction and flow-based future MRI generation.<n>Ultimately, Brain-WM offers a robust clinical sandbox for optimizing patient healthcare.
  arXiv  Detail & Related papers  (2026-03-08T09:54:08Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamics [13.83306392535935]
  Accurate prediction of tumor trajectories under standard-of-care (SoC) therapies remains a major unmet need in oncology.<n>We introduce Standard-of-Care Digital Twin (SoC-DT), a differentiable framework that unifies reaction-diffusion tumor growth models.<n>An implicit-explicit exponential time-differencing solver, IMEX-SoC, is also proposed, which ensures stability, positivity, and scalability in SoC treatment situations.
  arXiv  Detail & Related papers  (2025-09-29T04:14:11Z)
• MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
  We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation.<n>Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning.<n>Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
  arXiv  Detail & Related papers  (2025-03-18T15:39:44Z)
• Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models [18.222267022441734]
  Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates.<n> Partial differential equation-based models offer promising potential to enhance therapeutic outcomes.<n>We introduce an approach leveraging a neural forward solver with gradient-based optimization to significantly reduce calibration time.
  arXiv  Detail & Related papers  (2025-01-14T16:10:25Z)
• DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime [18.443316087890324]
  Reinforcement learning (RL) has garnered increasing recognition for its potential to optimise dynamic treatment regimes (DTRs) in personalised medicine. We introduce textitDTR-Bench, a benchmarking platform for simulating diverse healthcare scenarios. We evaluate various state-of-the-art RL algorithms across these settings, particularly highlighting their performance amidst real-world challenges.
  arXiv  Detail & Related papers  (2024-05-28T21:40:00Z)
• Reinforcement Learning with Heterogeneous Data: Estimation and Inference [84.72174994749305]
  We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity. We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class. We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset.
  arXiv  Detail & Related papers  (2022-01-31T20:58:47Z)

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.