This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs) in cervical cancer brachytherapy. Using ...
Clinical oncology (Royal College of Radiologists (Great Britain))
40120536
AIMS: To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).
Journal of applied clinical medical physics
40108745
PURPOSE: Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re-optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a hi...
This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic cancer using a prototype DL-based automated planning support system, named RatoGuide, to evaluate it...
Head-and-neck simultaneous integrated boost (SIB) treatment planning using intensity modulated radiation therapy is particularly challenging due to the proximity to organs-at-risk. Depending on the specific clinical conditions, different parotid-spar...
FLASH radiotherapy (RT), microbeam RT (MRT) and minibeam RT (MBRT) are novel RT techniques that have been shown to reduce normal tissue complication probabilities, by modulating the dose distributions through different parameters in space and time. T...
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
40174514
PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remains under-validated. We reported an investigation that validated a robust semi-supervised conditional ...
BACKGROUND: Radiotherapy treatment planning traditionally involves complex and time-consuming processes, often relying on trial-and-error methods. The emergence of artificial intelligence, particularly Large Language Models (LLMs), surpassing human c...
Uncertainty assessment of deep learning autosegmentation (DLAS) models can support contour corrections in adaptive radiotherapy (ART), e.g. by utilizing Monte Carlo Dropout (MCD) uncertainty maps. However, poorly calibrated uncertainties at the patie...