Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model

• URL: http://arxiv.org/abs/2608.03086v1
• Date: Tue, 04 Aug 2026 03:53:50 GMT
• Title: Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model
• Abstract summary: We propose a digital twin-based automatic planning framework that combines a neural ablation prediction model with a genetic algorithm.<n>The framework enabled approximately 420-fold faster planning than numerical-simulation-based planning.
• Score: 2.2301454158576135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specific planning of antenna insertion trajectory, power, and treatment duration. Accurate numerical simulation can provide physically reliable ablation predictions; however, its high computational cost limits its use in optimization-based planning, where repeated forward evaluations are required. To address this issue, we propose a digital twin-based automatic planning framework that combines a neural ablation prediction model with a genetic algorithm. The model was trained on multiphysics simulation data generated from patient-specific tumor and vessel structures, antenna configurations, and treatment conditions, and was used as a fast forward model during planning. The prediction model achieved a Dice score of 95.1%, enabling accurate deep learning-based optimization. In 13 unseen planning cases, the proposed method improved ablation efficiency by 54.3% and reduced organ damage by 55.0% compared with clinician-defined planning, while slightly shortening the insertion path length by 3.3%. Most generated plans were also judged clinically applicable by MWA specialists. Furthermore, the framework enabled approximately 420-fold faster planning than numerical-simulation-based planning, demonstrating its potential as a fast digital twin for quantitative and personalized MWA treatment planning. The code is available at: https://github.com/SeonAengCho/MWA-Planning.git

Related papers

• Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning [0.0]
  Dose-PlanNet is a physics-guided 3D deep learning architecture designed to predict dose distributions.<n>This pipeline demonstrates that physics-informed deep learning can accelerate radiotherapy while safely maintaining the stringent dosimetric quality required for high-precision clinical deployment.
  arXiv  Detail & Related papers  (2026-08-27T10:05:01Z)
• 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)
• AI End-to-End Radiation Treatment Planning Under One Second [3.908966856945294]
  We introduce AIRT (Artificial Intelligence-based Radiotherapy), an end-to-end deep-learning framework that directly infers deliverable treatment plans from CT images and structure contours.<n>AIRT generates single-arc VMAT prostate plans, from imaging and anatomical inputs to leaf sequencing, in under one second on a single Nvidia A100 GPU.<n>Non-inferiority to RapidPlan Eclipse was demonstrated across target coverage and OAR sparing metrics.
  arXiv  Detail & Related papers  (2026-03-06T14:45:44Z)
• Demo: Generative AI helps Radiotherapy Planning with User Preference [8.699769678493807]
  We introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors.<n>These preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs)
  arXiv  Detail & Related papers  (2025-12-08T16:49:21Z)
• Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction [13.716930604289924]
  ADDiff-Dose is an Anatomical-Dose Dual Constraints Diffusion Model for end-to-end multi-tumor dose prediction.<n>It incorporates conditional features via a multi-head attention mechanism and utilizes a composite loss function combining MSE, conditional terms, and KL divergence.<n>It significantly outperforms traditional baselines, achieving an MAE of 0.101-0.154, a DICE coefficient of 0.927, and limiting spinal cord maximum dose error to within 0.1 Gy.
  arXiv  Detail & Related papers  (2025-08-04T04:25:47Z)
• A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment [46.776978552161395]
  Small language models (SLMs) offer a cost-effective alternative to large language models such as GPT-4.<n>SLMs offer a cost-effective alternative, but their limited capacity requires biomedical domain adaptation.<n>We propose a novel framework for adapting SLMs into high-performing clinical models.
  arXiv  Detail & Related papers  (2025-05-15T21:40:21Z)
• ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models [46.80682547774335]
  We propose ScaleMAI, an agent of AI-integrated data curation and annotation.<n>First, ScaleMAI creates a dataset of 25,362 CT scans, including per-voxel annotations for benign/malignant tumors and 24 anatomical structures.<n>Second, through progressive human-in-the-loop iterations, ScaleMAI provides Flagship AI Model that can approach the proficiency of expert annotators in detecting pancreatic tumors.
  arXiv  Detail & Related papers  (2025-01-06T22:12:00Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines [48.547200649819615]
  We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models.
  arXiv  Detail & Related papers  (2022-02-16T19:18:42Z)
• 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)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)

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.