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Organs at Risk

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Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology May 23, 2022
AIM: To train and validate a comprehensive deep-learning (DL) segmentation model for loco-regional breast cancer with the aim of clinical implementation.
Humans Female Breast Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Neoplasms, Second Primary Deep Learning Radiation Oncology
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Tackling the class imbalance problem of deep learning-based head and neck organ segmentation.

International journal of computer assisted radiology and surgery May 16, 2022
PURPOSE: The segmentation of organs at risk (OAR) is a required precondition for the cancer treatment with image- guided radiation therapy. The automation of the segmentation task is therefore of high clinical relevance. Deep learning (DL)-based medi...
Radiotherapy, Image-Guided Head and Neck Neoplasms Organs at Risk Humans Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Tomography, X-Ray Computed
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Registration-guided deep learning image segmentation for cone beam CT-based online adaptive radiotherapy.

Medical physics May 4, 2022
PURPOSE: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on on...
Humans Algorithms Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Deep Learning Cone-Beam Computed Tomography
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Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Apr 18, 2022
PURPOSE/OBJECTIVE(S): Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively imp...
Breast Neoplasms Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Female Organs at Risk Radiotherapy Planning, Computer-Assisted Humans
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Artificial Intelligence Radiotherapy Planning: Automatic Segmentation of Human Organs in CT Images Based on a Modified Convolutional Neural Network.

Frontiers in public health Apr 15, 2022
OBJECTIVE: Precise segmentation of human organs and anatomic structures (especially organs at risk, OARs) is the basis and prerequisite for the treatment planning of radiation therapy. In order to ensure rapid and accurate design of radiotherapy trea...
Image Processing, Computer-Assisted Artificial Intelligence Humans Tomography, X-Ray Computed Neural Networks, Computer Organs at Risk
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Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Mar 26, 2022
BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to de...
Nasopharyngeal Neoplasms Radiotherapy, Intensity-Modulated Nasopharyngeal Carcinoma Humans Organs at Risk Retrospective Studies Radiotherapy Planning, Computer-Assisted Deep Learning Radiotherapy Dosage
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Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

International journal of radiation oncology, biology, physics Mar 15, 2022
PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large i...
Tomography, X-Ray Computed Deep Learning Organs at Risk Nasopharyngeal Carcinoma Image Processing, Computer-Assisted Humans Nasopharyngeal Neoplasms Radiodermatitis
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The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.

Journal of applied clinical medical physics Mar 9, 2022
PURPOSE: To develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explo...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Endometrial Neoplasms Radiotherapy, Intensity-Modulated Feasibility Studies Humans Organs at Risk Female Uterine Cervical Neoplasms Deep Learning
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Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

Journal of applied clinical medical physics Mar 9, 2022
PURPOSE: Adaptive radiotherapy requires auto-segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto-segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN can...
Head and Neck Neoplasms Humans Deep Learning Magnetic Resonance Imaging Organs at Risk Image Processing, Computer-Assisted
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Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

Medical physics Feb 21, 2022
BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborio...
Organs at Risk Radiotherapy Planning, Computer-Assisted Deep Learning Prostate Artificial Intelligence Male Humans
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