AIMC Topic: Organs at Risk
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Creation of knowledge-based planning models intended for large scale distribution: Minimizing the effect of outlier plans.
Journal of applied clinical medical physics
Apr 6, 2018
Knowledge-based planning (KBP) can be used to estimate dose-volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not p...
Functional-guided radiotherapy using knowledge-based planning.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Apr 5, 2018
BACKGROUND AND PURPOSE: There are two significant challenges when implementing functional-guided radiotherapy using 4DCT-ventilation imaging: (1) lack of knowledge of realistic patient specific dosimetric goals for functional lung and (2) ensuring co...
Fully automated searching for the optimal VMAT jaw settings based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge-based planning.
Journal of applied clinical medical physics
Mar 25, 2018
PURPOSE: Eclipse treatment planning system has not been able to optimize the jaw positions for Volumetric Modulated Arc Therapy (VMAT). The arbitrary and planner-dependent jaw placements define the maximum field size within which multi-leaf-collimato...
Chest wall dose reduction using noncoplanar volumetric modulated arc radiation therapy for lung stereotactic ablative radiation therapy.
Practical radiation oncology
Dec 19, 2017
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 knowledge-based approach to automated planning for hepatocellular carcinoma.
Journal of applied clinical medical physics
Nov 15, 2017
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.
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
Oct 28, 2017
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...
Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study.
Physics in medicine and biology
Oct 12, 2017
Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum...
Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients?
Radiation oncology (London, England)
May 22, 2017
BACKGROUND: Knowledge-based planning (KBP) is a promising technique that can improve plan quality and increase planning efficiency. However, no attempts have been made to extend the domain of KBP for planners with different planning experiences so fa...
Clinical implementation of a knowledge based planning tool for prostate VMAT.
Radiation oncology (London, England)
May 8, 2017
BACKGROUND: A knowledge based planning tool has been developed and implemented for prostate VMAT radiotherapy plans providing a target average rectum dose value based on previously achievable values for similar rectum/PTV overlap. The purpose of this...
Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning.
Acta oncologica (Stockholm, Sweden)
Feb 8, 2017
BACKGROUND: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach.
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