AIMC Topic: Radiotherapy Planning, Computer-Assisted

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Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 19, 2021
BACKGROUND AND PURPOSE: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. D...
Observer Variation Soft Tissue Neoplasms Radiotherapy Planning, Computer-Assisted Humans Reproducibility of Results Deep Learning Sarcoma
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Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.

Medical physics Nov 16, 2021
PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation...
Humans Deep Learning Cone-Beam Computed Tomography Radiotherapy Planning, Computer-Assisted Image Processing, Computer-Assisted Radiotherapy Dosage Lung Neoplasms Proton Therapy
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Deep learning-based thoracic CBCT correction with histogram matching.

Biomedical physics & engineering express Oct 29, 2021
Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup...
Radiotherapy Planning, Computer-Assisted Humans Radiotherapy, Image-Guided Deep Learning Spiral Cone-Beam Computed Tomography
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Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.

The British journal of radiology Oct 28, 2021
OBJECTIVE: Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based...
Radiotherapy Planning, Computer-Assisted Humans Proton Therapy Machine Learning Carcinoma, Squamous Cell Retrospective Studies Head and Neck Neoplasms Tomography, X-Ray Computed
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Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique Oct 26, 2021
The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicate...
Observer Variation Radiation Oncology Humans Organs at Risk Deep Learning Radiotherapy Planning, Computer-Assisted
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Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 21, 2021
BACKGROUND AND PURPOSE: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a hig...
Humans Tomography, X-Ray Computed Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Organs at Risk Radiotherapy Planning, Computer-Assisted
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Few-shot learning for deformable image registration in 4DCT images.

The British journal of radiology Oct 18, 2021
OBJECTIVES: To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy.
Respiration Exhalation Inhalation Radiotherapy, Image-Guided Four-Dimensional Computed Tomography Deep Learning Time Factors Radiotherapy Planning, Computer-Assisted Humans Lung
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Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.

Radiation oncology (London, England) Oct 14, 2021
OBJECTIVE: To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.
Humans Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Neural Networks, Computer Radiotherapy, Intensity-Modulated Prognosis Bone Neoplasms Phantoms, Imaging Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Soft Tissue Neoplasms Cone-Beam Computed Tomography Lung Neoplasms Organs at Risk
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Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy.

Radiation oncology (London, England) Oct 14, 2021
PURPOSE: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts.
Deep Learning Image Processing, Computer-Assisted Organs at Risk Female Breast Neoplasms Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiation Oncologists Radiotherapy Planning, Computer-Assisted Observer Variation Radiotherapy, Adjuvant Humans
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Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.

Journal of applied clinical medical physics Oct 8, 2021
Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised ma...
Deep Learning Humans Radiotherapy Planning, Computer-Assisted Magnetic Resonance Imaging Reference Standards Male Prostatic Neoplasms
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