AI Medical Compendium Topic:
Radiotherapy Planning, Computer-Assisted

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A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

Medical physics
PURPOSE: Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed t...

Impact of database quality in knowledge-based treatment planning for prostate cancer.

Practical radiation oncology
PURPOSE: This article investigates dose-volume prediction improvements in a common knowledge-based planning (KBP) method using a Pareto plan database compared with using a conventional, clinical plan database.

Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.

PloS one
PURPOSE: Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant am...

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy.

Physics in medicine and biology
With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendo...

Chest wall dose reduction using noncoplanar volumetric modulated arc radiation therapy for lung stereotactic ablative radiation therapy.

Practical radiation oncology
PURPOSE: Stereotactic ablative radiation therapy (SABR) to lung tumors close to the chest wall can cause rib fractures or chest wall pain. We evaluated and propose a clinically practical solution of using noncoplanar volumetric modulated arc radiatio...

A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV)....

Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify wh...

A knowledge-based approach to automated planning for hepatocellular carcinoma.

Journal of applied clinical medical physics
PURPOSE: To build a knowledge-based model of liver cancer for Auto-Planning, a function in Pinnacle, which is used as an automated inverse intensity modulated radiation therapy (IMRT) planning system.

Neural network dose models for knowledge-based planning in pancreatic SBRT.

Medical physics
PURPOSE: Stereotactic body radiation therapy (SBRT) for pancreatic cancer requires a skillful approach to deliver ablative doses to the tumor while limiting dose to the highly sensitive duodenum, stomach, and small bowel. Here, we develop knowledge-b...

Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

Medical physics
PURPOSE: Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DD...