AIMC Topic: Radiotherapy Planning, Computer-Assisted

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Automatic lung dose painting for functional lung avoidance radiotherapy through multi-modality-guided dose prediction.

Physics in medicine and biology
This study aims to develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for functional lung avoidance radiotherapy (FLART) guided by voxel-wise lung function images.The proposed auto-planning algorithm consists of a no...

Deep learning-based prediction of dynamic blood dose estimates for head-and-neck cancer.

Physics in medicine and biology
During radiotherapy, the radiation dose delivered to circulating blood can result in radiation-induced lymphopenia, which is correlated with adverse clinical outcomes like lower survival. Increasingly complex models to simulate radiation dose deliver...

Integrating aperture shape controller and machine learning prediction to improve gamma passing rates in lattice radiotherapy.

Physics in medicine and biology
This study proposes a workflow integrating the aperture shape controller (ASC) in the Varian Eclipse system with a machine learning-based verification prediction model to improve gamma passing rates (GPRs) of LATTICE Radiotherapy (LRT) plans and redu...

New insights into automatic treatment planning for cancer radiotherapy using explainable artificial intelligence.

Physics in medicine and biology
This study aims to uncover the opaque decision-making process of an artificial intelligence (AI) agent for automatic treatment planning.We examined a previously developed AI agent based on the actor-critic with experience replay (ACER) network, which...

Investigation of data-driven stopping power calibration of treatment planning x-ray CT from simulated sparse-view proton radiographies.

Physics in medicine and biology
Proton therapy treatment planning currently needs to account for relatively large range uncertainty margins primarily due to the semi-empirical calibration of the treatment planning x-ray computed tomography (CT) to proton stopping power relative to ...

Incorporating and quantifying deformable image registration uncertainties in dose accumulation: a feasibility study on the benefit of online adaptive therapy.

Physics in medicine and biology
. Accurate dose accumulation relies on deformable image registration (DIR) to track dose across multiple images. However, DIR introduces uncertainties that can impact cumulative dose distributions. In this study, we present a probabilistic framework ...

MR-based synthetic CT generation using dual-attention enhanced 3D Conditional GAN for head and neck radiotherapy.

Biomedical physics & engineering express
. This study aims to synthesize CT from MR images for radiotherapy planning of head and neck tumor using an improved three-dimensional conditional generative adversarial network (3D cGAN) based on dual-attention modules.. A total of 212 paired CT and...

Neural network-driven direct CBCT-based dose calculation for head-and-neck proton treatment planning.

Physics in medicine and biology
Accurate dose calculation on cone beam computed tomography (CBCT) images is essential for modern proton treatment planning workflows, particularly when accounting for inter-fractional anatomical changes in adaptive treatment scenarios. Traditional CB...

Dose stratification-based convolutional neural networks for dose distribution prediction in radiotherapy.

Biomedical physics & engineering express
The fidelity of dose distribution prediction is paramount for radiotherapy planning. While existing deep learning-based methods have obtained noteworthy performance, most of them pursue the accurate prediction of global dose distribution but neglect ...

Adaptive radiotherapy for gastrointestinal malignancies.

Radiation oncology (London, England)
BACKGROUND: Adaptive radiotherapy (ART) is an advanced form of image-guided radiotherapy that involves the re-contouring and re-planning of a patient's treatment plan, either while the patient is on the table (online) or in between fractions (offline...