AIMC Topic: Organs at Risk

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Breast radiotherapy planning: A decision-making framework using deep learning.

Medical physics Dec 3, 2024
BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation th...
Humans Retrospective Studies Female Radiotherapy Planning, Computer-Assisted Radiotherapy, Conformal Radiotherapy Dosage Radiotherapy, Intensity-Modulated Breast Neoplasms Deep Learning Decision Making Organs at Risk
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Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.

Journal of applied clinical medical physics Dec 1, 2024
PURPOSE: This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geome...
Male Deep Learning Imaging, Three-Dimensional Prostatic Neoplasms Humans Observer Variation Retrospective Studies Radiotherapy Dosage Tomography, X-Ray Computed Radiotherapy, Intensity-Modulated Prostate Radiometry Feasibility Studies Organs at Risk Radiotherapy Planning, Computer-Assisted
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Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer.

Journal of applied clinical medical physics Dec 1, 2024
OBJECTIVES: This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers.
Radiotherapy, Intensity-Modulated Humans Algorithms Head and Neck Neoplasms Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Quality Assurance, Health Care Radiotherapy Dosage
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Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.

Journal of applied clinical medical physics Nov 29, 2024
PURPOSE: Synthetic computed tomography (sCT)-algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and ...
Image Processing, Computer-Assisted Algorithms Magnetic Resonance Imaging Humans Radiotherapy Planning, Computer-Assisted Retrospective Studies Prostatic Neoplasms Artificial Intelligence Radiotherapy Dosage Organs at Risk Tomography, X-Ray Computed Radiotherapy, Intensity-Modulated Male
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Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.

Journal of applied clinical medical physics Nov 29, 2024
BACKGROUND: Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high ...
Lung Neoplasms Middle Aged Feasibility Studies Radiotherapy Dosage Deep Learning Humans Organs at Risk Radiotherapy, Intensity-Modulated Cone-Beam Computed Tomography Female Radiotherapy Planning, Computer-Assisted Image Processing, Computer-Assisted Aged Male
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Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.

Radiation oncology (London, England) Nov 25, 2024
BACKGROUND: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm...
Monte Carlo Method Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiosurgery Algorithms Humans Deep Learning Lung Neoplasms Organs at Risk
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Deep learning-based multiple-CT optimization: An adaptive treatment planning approach to account for anatomical changes in intensity-modulated proton therapy for head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 23, 2024
BACKGROUNDS: Intensity-modulated proton therapy (IMPT) is particularly susceptible to range and setup uncertainties, as well as anatomical changes.
Deep Learning Cone-Beam Computed Tomography Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Proton Therapy Humans Algorithms Retrospective Studies
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Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas.

Radiation oncology (London, England) Nov 21, 2024
INTRODUCTION: The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the ...
Lymph Nodes Female Magnetic Resonance Imaging Adult Organs at Risk Humans Male Radiotherapy Planning, Computer-Assisted Artificial Intelligence Child Software Tomography, X-Ray Computed
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Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.

Journal of applied clinical medical physics Nov 15, 2024
BACKGROUND: Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, bu...
Radiotherapy Planning, Computer-Assisted Brachytherapy Radiotherapy Dosage Radiotherapy, Intensity-Modulated Uterine Cervical Neoplasms Humans Deep Learning Imaging, Three-Dimensional Algorithms Organs at Risk Retrospective Studies Female
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Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

Journal of applied clinical medical physics Nov 14, 2024
OBJECTIVE: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.
Radiation Dosage Signal-To-Noise Ratio Pelvic Neoplasms Radiotherapy Dosage Radiographic Image Interpretation, Computer-Assisted Feasibility Studies Organs at Risk Radiotherapy Planning, Computer-Assisted Radiographic Image Enhancement Radiotherapy, Intensity-Modulated Phantoms, Imaging Abdominal Neoplasms Radiotherapy, Image-Guided Humans Algorithms Prospective Studies Tomography, X-Ray Computed Female Male Deep Learning Image Processing, Computer-Assisted
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