AIMC Topic: Organs at Risk

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Evaluation of a deep image-to-image network (DI2IN) auto-segmentation algorithm across a network of cancer centers.

Journal of cancer research and therapeutics Jan 22, 2024
PURPOSE/OBJECTIVE S: Due to manual OAR contouring challenges, various automatic contouring solutions have been introduced. Historically, common clinical auto-segmentation algorithms used were atlas-based, which required maintaining a library of self-...
Prospective Studies Tomography, X-Ray Computed Neoplasms Humans Algorithms Organs at Risk Radiotherapy Planning, Computer-Assisted Cancer Care Facilities Deep Learning Image Processing, Computer-Assisted
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Deep learning-based automatic segmentation of cardiac substructures for lung cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 19, 2023
PURPOSE: Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation model...
Humans Deep Learning Heart Organs at Risk Radiotherapy Planning, Computer-Assisted Lung Neoplasms Carcinoma, Non-Small-Cell Lung
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Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists Dec 6, 2023
Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithms have become available and been shown to provide accurate contouring for various anatomical sites. ...
Deep Learning Head and Neck Neoplasms Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Breast Neoplasms Female Algorithms Prostatic Neoplasms Male
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Deep learning based MLC aperture and monitor unit prediction as a warm start for breast VMAT optimisation.

Physics in medicine and biology Nov 16, 2023
. Automated treatment planning today is focussed on non-exact, two-step procedures. Firstly, dose-volume histograms (DVHs) or 3D dose distributions are predicted from the patient anatomy. Secondly, these are converted in multi-leaf collimator (MLC) a...
Radiotherapy, Intensity-Modulated Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Breast Organs at Risk Deep Learning
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Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study.

Breast (Edinburgh, Scotland) Nov 15, 2023
PURPOSE: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potentia...
Humans Female Deep Learning Breast Breast Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted
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Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.

Journal of applied clinical medical physics Nov 9, 2023
Quality of organ at risk (OAR) autosegmentation is often judged by concordance metrics against the human-generated gold standard. However, the ultimate goal is the ability to use unedited autosegmented OARs in treatment planning, while maintaining th...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Laryngeal Neoplasms Radiotherapy, Intensity-Modulated Humans Deep Learning Organs at Risk
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Extensive clinical testing of Deep Learning Segmentation models for thorax and breast cancer radiotherapy planning.

Acta oncologica (Stockholm, Sweden) Nov 7, 2023
BACKGROUND: The performance of deep learning segmentation (DLS) models for automatic organ extraction from CT images in the thorax and breast regions was investigated. Furthermore, the readiness and feasibility of integrating DLS into clinical practi...
Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Thoracic Wall Tomography, X-Ray Computed Breast Neoplasms Female Deep Learning
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Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.

Biomedical engineering online Nov 1, 2023
PURPOSE: The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and...
Head Image Processing, Computer-Assisted Humans Algorithms Head and Neck Neoplasms Deep Learning Organs at Risk
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Contour subregion error detection methodology using deep learning auto-segmentation.

Medical physics Oct 4, 2023
BACKGROUND: Inaccurate manual organ delineation is one of the high-risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of...
Image Processing, Computer-Assisted Head and Neck Neoplasms Neck Tomography, X-Ray Computed Humans Organs at Risk Deep Learning Radiotherapy Planning, Computer-Assisted
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Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.

Medical physics Sep 25, 2023
BACKGROUND: Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied.
Uterine Cervical Neoplasms Organs at Risk Humans Radiotherapy, Intensity-Modulated Female Radiotherapy Planning, Computer-Assisted Deep Learning Radiotherapy Dosage
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