mlsanityData to 14 Oct 2026

Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning

arXiv 2509.04886 ↗First posted 5 Sep 2025cs.CVcited by 0

On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery

Abstract (arXiv)

Cryoablation is a minimally invasive localised treatment for prostate cancer that destroys malignant tissue during de-freezing, while sparing surrounding healthy structures. Its success depends on accurate preoperative planning of cryoprobe placements to fully cover the tumour and avoid critical anatomy. This planning is currently manual, expertise-dependent, and time-consuming, leading to variability in treatment quality and limited scalability. In this work, we introduce Cryo-RL, a reinforcement learning framework that models cryoablation planning as a Markov decision process and learns an optimal policy for cryoprobe placement. Within a simulated environment that models clinical constraints and stochastic intraoperative variability, an agent sequentially selects cryoprobe positions and ice sphere diameters. Guided by a reward function based on tumour coverage, this agent learns a cryoablation strategy that leads to optimal cryoprobe placements without the need for any manually-designed plans. Evaluated on 583 retrospective prostate cancer cases, Cryo-RL achieved over 8 percentage-point Dice improvements compared with the best automated baselines, based on geometric optimisation, and matched human expert performance while requiring substantially less planning time. These results highlight the potential of reinforcement learning to deliver clinically viable, reproducible, and efficient cryoablation plans.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 21 references that are in the map. First posted on arXiv 5 Sep 2025.

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5 of 21 references.

References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registrationcited by 16
  3. 2024Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR imagescited by 4
  4. 2025Promptable cancer segmentation using minimal expert-curated datacited by 2
  5. 2025SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumourscited by 1

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  1. 2026Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation
  2. 2025Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model
  3. 2025Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy
  4. 2025Transforming Multimodal Models into Action Models for Radiotherapy
  5. 2025Patient-Specific Deep Reinforcement Learning for Automatic Replanning in Head-and-Neck Cancer Proton Therapy
  6. 2024Automating proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning
  7. 2026Learning Where to Look: A Reinforcement Learning Framework for Robust Micro-Ultrasound Prostate Cancer Detection
  8. 2025Actor Critic with Experience Replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy