AIMC Topic: Organs at Risk

Clear Filters Showing 261 to 270 of 315 articles

Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials.

Practical radiation oncology Dec 15, 2018
PURPOSE: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectiv...
Humans Knowledge Bases Organs at Risk Quality Control Radiometry Radiotherapy Planning, Computer-Assisted Dose Fractionation, Radiation Software Decision Support Systems, Clinical Carcinoma, Non-Small-Cell Lung Lung Neoplasms Feasibility Studies Models, Biological
View on PubMed DOI

Deep-learning based surface region selection for deep inspiration breath hold (DIBH) monitoring in left breast cancer radiotherapy.

Physics in medicine and biology Dec 12, 2018
Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convoluti...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Breath Holding Female Deep Learning Humans Monitoring, Physiologic Unilateral Breast Neoplasms
View on PubMed DOI

Technical Note: More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades.

Medical physics Dec 7, 2018
PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve ...
Time Factors Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Neural Networks, Computer Tomography, X-Ray Computed
View on PubMed DOI

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

Journal of applied clinical medical physics Nov 12, 2018
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 ...
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Patient Positioning Supine Position Prone Position Rectal Neoplasms Humans Organs at Risk Neural Networks, Computer Radiotherapy Planning, Computer-Assisted
View on PubMed DOI

Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.

Acta oncologica (Stockholm, Sweden) Nov 6, 2018
BACKGROUND: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compar...
Esophagus Spine Organs at Risk Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Image Processing, Computer-Assisted Lung Neoplasms Radiography, Abdominal Atlases as Topic Anatomy, Artistic Tumor Burden Pattern Recognition, Automated Radiography, Thoracic Humans Neural Networks, Computer
View on PubMed DOI

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

Journal of applied clinical medical physics Oct 19, 2018
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...
Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Radiometry Radiotherapy Dosage Radiotherapy, Intensity-Modulated Prostatic Neoplasms Pelvis Models, Biological Humans Male Lymph Nodes
View on PubMed DOI

Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.

European radiology Oct 9, 2018
OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (te...
Patient Care Planning Humans Aged Male Young Adult Nasopharyngeal Carcinoma Adolescent Middle Aged Adult Organ Sparing Treatments Neural Networks, Computer Organs at Risk Deep Learning Sensitivity and Specificity Tomography, X-Ray Computed Female
View on PubMed DOI

Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.

Medical physics Sep 19, 2018
PURPOSE: Intensity modulated radiation therapy (IMRT) is commonly employed for treating head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing. Accurate delineation of organs-at-risk (OARs) on H&N CT images is thus es...
Humans Organs at Risk Neural Networks, Computer Automation Models, Theoretical Tomography, X-Ray Computed Head and Neck Neoplasms Image Processing, Computer-Assisted
View on PubMed DOI

Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT.

Medical physics Sep 10, 2018
BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose-volume histogram-based metric may grossly under/overestimate the therapeutic toxicity ...
Neural Networks, Computer Precision Medicine Liver Humans Radiosurgery Organs at Risk Risk Treatment Failure Radiotherapy Planning, Computer-Assisted Biliary Tract Radiotherapy Dosage
View on PubMed DOI

Knowledge-Based Planning for Identifying High-Risk Stereotactic Ablative Radiation Therapy Treatment Plans for Lung Tumors Larger Than 5 cm.

International journal of radiation oncology, biology, physics Aug 14, 2018
PURPOSE: Stereotactic ablative body radiation therapy (SABR) for lung tumors ≥5 cm can be associated with more toxicity than that for smaller tumors. We investigated the relationship between dosimetry and toxicity and used a knowledge-based planning ...
Radiosurgery Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Lung Neoplasms Radiotherapy Dosage Knowledge Bases
View on PubMed DOI
Popular Topics
Recent Journals