AIMC Topic: Organs at Risk

Clear Filters Showing 281 to 290 of 315 articles

Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning.

Radiation oncology (London, England) May 10, 2015
BACKGROUND: A knowledge-based radiation therapy (KBRT) treatment planning algorithm was recently developed. The purpose of this work is to investigate how plans that are generated with the objective KBRT approach compare to those that rely on the jud...
Tomography, X-Ray Computed Male Prostatic Neoplasms Neoplasm Staging Rectum Knowledge Bases Humans Algorithms Prognosis Radiotherapy, Intensity-Modulated Urinary Bladder Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
View on PubMed DOI

Evaluation of a knowledge-based planning solution for head and neck cancer.

International journal of radiation oncology, biology, physics Jan 30, 2015
PURPOSE: Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new pati...
Organ Sparing Treatments Pharyngeal Muscles Salivary Glands Male Head and Neck Neoplasms Benchmarking Radiotherapy, Intensity-Modulated Knowledge Bases Radiation Injuries Mouth Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Radiotherapy Dosage
View on PubMed DOI

Automatic contour quality assurance using deep-learning based contours.

Physics in medicine and biology Jul 16, 2025
Safe deployment of auto-contouring models requires the inclusion of automated quality assurance (QA). One such approach is to use two independent auto-contouring models and compare them geometrically for acceptability. This is not effective because g...
Head and Neck Neoplasms Radiotherapy, Intensity-Modulated Radiotherapy Planning, Computer-Assisted Humans Quality Assurance, Health Care Organs at Risk Female Quality Control Automation Deep Learning
View on PubMed DOI

U-Attention-Net: a deep learning automatic delineation model for parotid glands in head and neck cancer organs at risk on radiotherapy localization computed tomography images.

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) Jul 1, 2025
OBJECTIVE: This study aimed to develop a novel deep learning model, U-Attention-Net (UA-Net), for precise segmentation of parotid glands on radiotherapy localization CT images.
Humans Head and Neck Neoplasms Tomography, X-Ray Computed Organs at Risk Parotid Gland Deep Learning Automation Image Processing, Computer-Assisted
View on PubMed DOI

Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Jul 1, 2025
PURPOSES: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets.
Neuroblastoma Adolescent Organs at Risk Humans Abdominal Neoplasms Male Child, Preschool Child Tomography, X-Ray Computed Infant Female Deep Learning Radiotherapy Planning, Computer-Assisted Kidney Neoplasms
View on PubMed DOI

Automated field-in-field planning for tangential breast radiation therapy based on digitally reconstructed radiograph.

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) Jun 1, 2025
BACKGROUND: The tangential field-in-field (FIF) technique is a widely used method in breast radiation therapy, known for its efficiency and the reduced number of fields required in treatment planning. However, it is labor-intensive, requiring manual ...
Automation Radiotherapy Dosage Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Female Humans Breast Neoplasms
View on PubMed DOI

Virtual monochromatic image-based automatic segmentation strategy using deep learning method.

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) Jun 1, 2025
BACKGROUND AND PURPOSE: The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thor...
Deep Learning Image Processing, Computer-Assisted Organs at Risk Automation Humans Retrospective Studies Tomography, X-Ray Computed
View on PubMed DOI

On factors that influence deep learning-based dose prediction of head and neck tumors.

Physics in medicine and biology May 22, 2025
This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components ne...
Radiotherapy Planning, Computer-Assisted Radiation Dosage Radiotherapy Dosage Deep Learning Head and Neck Neoplasms Organs at Risk Humans Tomography, X-Ray Computed
View on PubMed DOI

Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution.

Radiation oncology (London, England) May 19, 2025
PURPOSE: Accurate pre-treatment dose prediction is essential for efficient radiotherapy planning. Although deep learning models have advanced automated dose distribution, comprehensive multi-tumor analyses remain scarce. This study assesses deep lear...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Middle Aged Neoplasms Deep Learning Humans Female Male
View on PubMed DOI

Patient-specific uncertainty calibration of deep learning-based autosegmentation networks for adaptive MRI-guided lung radiotherapy.

Physics in medicine and biology May 16, 2025
Uncertainty assessment of deep learning autosegmentation (DLAS) models can support contour corrections in adaptive radiotherapy (ART), e.g. by utilizing Monte Carlo Dropout (MCD) uncertainty maps. However, poorly calibrated uncertainties at the patie...
Uncertainty Deep Learning Lung Neoplasms Calibration Organs at Risk Magnetic Resonance Imaging Humans Radiotherapy, Image-Guided Radiotherapy Planning, Computer-Assisted
View on PubMed DOI
Popular Topics
Recent Journals