AIMC Topic: Organs at Risk

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Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.

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) Jan 6, 2020
PURPOSE: We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.
Tomography, X-Ray Computed Female Reproducibility of Results Adult Humans Neural Networks, Computer Radiation Oncology Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Uterine Cervical Neoplasms Middle Aged Deep Learning Image Processing, Computer-Assisted
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A hybrid automated treatment planning solution for esophageal cancer.

Radiation oncology (London, England) Dec 19, 2019
OBJECTIVE: This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer.
Adult Male Middle Aged Esophageal Neoplasms Humans Female Radiotherapy Dosage Radiotherapy, Intensity-Modulated Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Automation
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Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique.

Journal of applied clinical medical physics Dec 9, 2019
PURPOSE: Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided...
Humans Algorithms Phantoms, Imaging Organs at Risk Male Radiotherapy Planning, Computer-Assisted Prostatic Neoplasms Radiotherapy Dosage Knowledge Bases Radiotherapy, Intensity-Modulated
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Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 5, 2019
BACKGROUND: Deep learning-based auto-segmented contours (DC) aim to alleviate labour intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most previous DC validation studies have a limited number of expert observers for com...
Observer Variation Humans Male Deep Learning Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted
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Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England) Nov 27, 2019
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...
Observer Variation Radiotherapy Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Republic of Korea Female Reproducibility of Results Aged Male Middle Aged Deep Learning Liver Neoplasms Humans Neural Networks, Computer Tomography, X-Ray Computed
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Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer.

Breast (Edinburgh, Scotland) Nov 26, 2019
Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes para...
Humans Artificial Intelligence Precision Medicine Female Radiotherapy Dosage Breast Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted
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A simple knowledge-based tool for stereotactic radiosurgery pre-planning.

Journal of applied clinical medical physics Nov 19, 2019
We studied the dosimetry of single-isocenter treatment plans generated to treat a solitary intracranial lesion using linac-based stereotactic radiosurgery (SRS). A common metric for evaluating SRS plan quality is the volume of normal brain tissue irr...
Radiosurgery Retrospective Studies Brain Neoplasms Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Algorithms
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Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 22, 2019
INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms th...
Radiotherapy Planning, Computer-Assisted Aged Observer Variation Young Adult Humans Adult Middle Aged Neural Networks, Computer Head and Neck Neoplasms Male Deep Learning Algorithms Neck Adolescent Female Neoplasm Staging Organs at Risk
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Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 17, 2019
BACKGROUND AND PURPOSE: Manual contouring is labor intensive, and subject to variations in operator knowledge, experience and technique. This work aims to develop an automated computed tomography (CT) multi-organ segmentation method for prostate canc...
Prostatic Neoplasms Pelvis Organs at Risk Tomography, X-Ray Computed Male Deep Learning Magnetic Resonance Imaging Humans
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Future of Radiotherapy in Nasopharyngeal Carcinoma.

The British journal of radiology Jul 9, 2019
Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radiosensitivity. Radiotherapy has long been recognized as the mainstay for the treatment of NPC. However...
Radiotherapy, Intensity-Modulated Radiotherapy Precision Medicine Nasopharyngeal Carcinoma Humans Immunotherapy Radiation Injuries Nasopharyngeal Neoplasms Artificial Intelligence Organs at Risk Proton Therapy Forecasting Molecular Targeted Therapy
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