AIMC Topic: Radiotherapy, Intensity-Modulated

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Complexity-based unsupervised machine learning for patient-specific VMAT quality assurance.

Medical physics
BACKGROUND: Patient-specific quality assurance (PSQA) is essential to guarantee the requested accuracy and safety of high-precision radiotherapy treatments. With the widespread adoption of modulated-intensity techniques, there is a growing need for i...

Knowledge-based trade-off prediction for NSCLC treatment planning using multi-output regression.

Medical physics
BACKGROUND: Knowledge-based planning (KBP) is a data-driven approach that utilizes the knowledge from previous high-quality treatment plans to predict dose-volume histogram (DVH) parameters for organs-at-risk (OARs) in new cases. Research has demonst...

Impact of deep learning model uncertainty on manual corrections to MRI-based auto-segmentation in prostate cancer radiotherapy.

Journal of applied clinical medical physics
BACKGROUND: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy. While methods exist to generate voxel-wise uncertainty maps from DL-based auto-segmentation models, these maps are rarely presented to clinicians.

A predictive quality assurance model for patient-specific gamma passing rate of hyperarc-based stereotactic radiotherapy and radiosurgery of brain metastases.

Journal of applied clinical medical physics
OBJECTIVE: Measurement-based patient specific quality assurance (PSQA) is an increasingly debated topic among medical physicists. Developments like online adaptive radiotherapy and same-day stereotactic treatments limit the time to do measurement-bas...

Automatic contour quality assurance using deep-learning based contours.

Physics in medicine and biology
Safe deployment of auto-contouring models requires the inclusion of automated quality assurance (QA). One such approach is to use two independent auto-contouring models and compare them geometrically for acceptability. This is not effective because g...

A novel dose calculation system implemented in image domain.

Medical physics
PURPOSE: Modern intensity-modulated radiotherapy, aiming to deliver an accurate dose to the planning target volume while protecting the surrounding organs at risk, is regarded as the indispensable treatment for cancer in the clinic. An accurate and e...

Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model.

Radiation oncology (London, England)
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...

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions.

Journal of radiation research
Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and vo...

Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Technology in cancer research & treatment
Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (...