AIMC Topic: Radiotherapy, Intensity-Modulated

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Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks.

Medical physics
PURPOSE: Patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other comm...

Knowledge-based dose prediction models for head and neck cancer are strongly affected by interorgan dependency and dataset inconsistency.

Medical physics
PURPOSE: The goal of this study was to generate a large treatment plan database for head and neck (H&N) cancer patients that can be considered as the gold standard to train and validate models for knowledge-based (KB) treatment planning and QA. With ...

Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique.

Medical physics
PURPOSE: To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorith...

A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning.

Medical physics
PURPOSE: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy trea...

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer.

Journal of applied clinical medical physics
PURPOSE: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning ...

Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.

Physics in medicine and biology
Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor...

The application of artificial intelligence in the IMRT planning process for head and neck cancer.

Oral oncology
Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI algorithms make them susceptible to misuse by researchers and clinicians. Understanding nuances of ne...

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy.

Journal of applied clinical medical physics
Knowledge-based planning (KBP) can be used to improve plan quality, planning speed, and reduce the inter-patient plan variability. KPB may also identify and reduce systematic variations in VMAT plans, something very important in multi-institutional c...

Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis.

Radiation oncology (London, England)
BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis.

Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

Radiological physics and technology
The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through...