Radiotherapy Dose Prediction

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12 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

Learning the Dose Manifold: A Latent Diffusion Framework for Volumetric Dose Prediction in Automated Radiotherapy Treatment Planning
Oct 19, 2026J. Boyer-Chammard, R. Vauclin, M. C. Biston +7Radiotherapy Dose Prediction 3D Diffusion Models
BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction
Sep 4, 2026Chen Cheng, Michael Ferraro, James Grover +2Radiotherapy Dose Prediction Healthcare
Image X Institute, The University of Sydney, Sydney, Australia
PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation
Sep 1, 2026Lukas Zimmermann, Hermann Fuchs, Attila Simkó +1Healthcare Radiotherapy Dose Prediction
Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria · Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Medical University of Vienna, Vienna, Austria · Department of Diagnostics and Intervention, Umeå University, Umeå, Sweden
Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning
Aug 27, 2026Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty +1Radiotherapy Dose Prediction Physics-Informed ML
Department of Mechanical Engineering, Indian Institute of Technology Kharagpur · Department of Radiation Oncology, Tata Medical Center Kolkata
DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy
Aug 10, 2026Zerun Zhang, Xiaoda Cong, Xiangkun Xu +2Healthcare Radiotherapy Dose Prediction
Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy
May 12, 2026Zhenxi Zhang, Yitao Zhuang, Yao Pu +7Radiotherapy Dose Prediction
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR · Department of Orthodontics and Orofacial Orthopedics, Friedrich-Alexander-University Erlangen-Nuremberg, Germany · Institute of Scientific Instrumentation, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China +2
Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
May 10, 2026Yuhan Wang, Zihan Li, Han Liu +7Diffusion Model Alignment Radiotherapy Dose Prediction
1UC Santa Cruz · 2Siemens Healthineers · University of Washington
Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations
Apr 28, 2026Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim +2Distribution Shift Robustness Transformer
Wayne State University, Detroit, MI, USA · Henry Ford Health, Detroit, MI
DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy
Apr 14, 2026Fan Xiao, Nikolaos Delopoulos, Niklas Wahl +13Radiotherapy Dose Prediction Medical Image Benchmarks
Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany · Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany · National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany +15
A Fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation
Apr 10, 2026Chi-Hieu Pham, Didier Benoit, Vincent Bourbonne +3Radiotherapy Dose Prediction Hybrid CNN-Transformer Architectures
LaTIM, INSERM-UMR1101, University of Brest, 29200 Brest, France · radiotherapy department of the Brest Hospital University, France
Clinical DVH metrics as a loss function for 3D dose prediction in head and neck radiotherapy
Mar 31, 2026Ruochen Gao, Marius Staring, Frank DankersRadiotherapy Dose Prediction Oncology
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands. · Department of Radiation Oncology, Leiden University Medical Center, Leiden, the Netherlands.
Toward Actionable Digital Twins for Radiation-Based Imaging and Therapy: Mathematical Formulation, Modular Workflow, and an OpenKBP-Based Dose-Surrogate Prototype
Mar 26, 2026Hsin-Hsiung Huang, Bulent SoykanRadiotherapy Dose Prediction Digital Twins
School of Data, Mathematical, and Statistical Sciences, University of Central Florida, Orlando, FL 32816 USA · Institute for Simulation and Training, University of Central Florida, Orlando, FL 32826 USA

Oct 19, 2026J/K move · Enter open · S save

Learning the Dose Manifold: A Latent Diffusion Framework for Volumetric Dose Prediction in Automated Radiotherapy Treatment Planning

J. Boyer-Chammard, R. Vauclin, M. C. Biston, P. Maury+6

Radiotherapy Dose Prediction 3D Diffusion Models Latent Diffusion Models

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Automated treatment planning (ATP) aims to reduce the unwarranted inter-planner variability of manual planning, while improving treatment quality and efficiency. Most ATP pipelines rely on the prediction of the achievable three-dimensional dose distribution for a given anatomy and the subsequent optimization of machine parameters to reproduce it. Diffusion-based methods are largely slice-wise due to the high dimensionality of radiotherapy data, and assume a one-to-one mapping between anatomy and optimal dose despite the multi-objective nature of treatment planning. They are evaluated on their ability to match reference doses and rarely on their integration in ATP frameworks. We present LaDose, a latent diffusion framework for fully volumetric dose prediction. An autoencoder learned on dose distributions, trained so that perturbed latents decode to plausible doses, compresses dose maps 128-fold and enables 3D diffusion on a single GPU. Prescription values are represented as exact scalar vectors that bypass the image encoder, avoiding resampling and latent compression. Multiscale classifier-free guidance allows for the modulation of clinical trade-offs at inference, without retraining or additional labeled data. On 900 clinically delivered head-and-neck volumetric modulated arc therapy plans from five centers, LaDose reached a median absolute error of 1.18 Gy (1.4--1.8% of the prescription) and was the only one of six frameworks free of systematic bias with respect to the reference distribution. Guidance scales acted predictably on target coverage and organ-at-risk sparing. Integrated with direct aperture optimization, the pipeline generated plans in 15.2 minutes; in a blinded review, senior clinicians rated 14 of 15 plans acceptable without editing.