AIMC Topic: Radiotherapy Planning, Computer-Assisted

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Automation in radiotherapy treatment planning: Examples of use in clinical practice and future trends for a complete automated workflow.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Modern radiotherapy treatment planning is a complex and time-consuming process that requires the skills of experienced users to obtain quality plans. Since the early 2000s, the automation of this planning process has become an important research topi...

A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

Physics in medicine and biology
We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to operate a treatment planning system (TPS) to generate high-quality radiation therapy (RT) treatment pla...

Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture.

Medical physics
PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reduc...

Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning.

Medical physics
PURPOSE: To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the neces...

Independent verification of brachytherapy treatment plan by using deep learning inference modeling.

Physics in medicine and biology
This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to verify the dwell positions and times for a given input brachytherapy isodo...

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.

Radiation oncology (London, England)
PURPOSE: We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical ra...

Synthetic CT generation from CBCT images via unsupervised deep learning.

Physics in medicine and biology
Adaptive-radiation-therapy (ART) is applied to account for anatomical variations observed over the treatment course. Daily or weekly cone-beam computed tomography (CBCT) is commonly used in clinic for patient positioning, but CBCT's inaccuracy in Hou...

Automatic clinical target volume delineation for cervical cancer in CT images using deep learning.

Medical physics
PURPOSE: Accurately delineating clinical target volumes (CTV) is essential for completing radiotherapy plans but is time-consuming, labor-intensive, and prone to inter-observer variation. Automating CTV delineation has the benefits of both speeding u...

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives.

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)
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significant...