AIMC Topic: Radiotherapy Planning, Computer-Assisted

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Toward a human-centric co-design methodology for AI detection of differences between planned and delivered dose in radiotherapy.

Journal of applied clinical medical physics
INTRODUCTION: Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a...

Proton dose calculation with transformer: Transforming spot map to dose.

Medical physics
BACKGROUND: Conventional proton dose calculation methods are either time- and resource-intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade-off between computational efficiency and accu...

Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy.

Technology in cancer research & treatment
The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segme...

Deep learning techniques for proton dose prediction across multiple anatomical sites and variable beam configurations.

Physics in medicine and biology
To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, with a focus on scenarios involving limited or highly heterogeneous datasets.In this study, 541 prost...

Deep learning-based segmentation of head and neck organs at risk on CBCT images with dosimetric assessment for radiotherapy.

Physics in medicine and biology
Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger and accelerate treatment replanning but is s...

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

Critical reviews in oncology/hematology
The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subtype and anatomical location. This narrative review examines the role of imaging in sarcoma character...

A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy.

Scientific reports
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 ...

Evaluating the prognostic significance of artificial intelligence-delineated gross tumor volume and prostate volume measurements for prostate radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Artificial intelligence (AI) may extract prognostic information from MRI for localized prostate cancer. We evaluate whether AI-derived prostate and gross tumor volume (GTV) are associated with toxicity and oncologic outcomes a...

Closing the gap in plan quality: Leveraging deep-learning dose prediction for adaptive radiotherapy.

Journal of applied clinical medical physics
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...

Initial characterization of a novel dual-robot orthovoltage radiotherapy system.

Biomedical physics & engineering express
Adequate access to radiotherapy is a critical global concern affecting low-resource settings such as low- and middle-income countries and rural regions. We propose to reduce this disparity by developing a novel low-cost radiotherapy device that treat...