AIMC Topic: Organs at Risk

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Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 29, 2020
OBJECTIVE: Dose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess thei...
Abdominal Neoplasms Proton Therapy Protons Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Deep Learning Child Organs at Risk
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Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.

Journal of applied clinical medical physics Nov 25, 2020
OBJECTIVE: To evaluate the accuracy of a deep learning-based auto-segmentation mode to that of manual contouring by one medical resident, where both entities tried to mimic the delineation "habits" of the same clinical senior physician.
Humans Deep Learning Algorithms Organs at Risk Female Uterine Cervical Neoplasms Radiotherapy Planning, Computer-Assisted
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Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

International journal of radiation oncology, biology, physics Nov 13, 2020
PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses...
Humans Algorithms Tomography, X-Ray Computed Adult Deep Learning Male Lung Neoplasms Middle Aged Clinical Trials, Phase III as Topic Carcinoma, Non-Small-Cell Lung Female Linear Models Heart Aged Statistics, Nonparametric Proportional Hazards Models Organs at Risk Aged, 80 and over Radiotherapy Dosage Confidence Intervals
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Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers.

JAMA network open Nov 2, 2020
IMPORTANCE: Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and inst...
Prostatic Neoplasms Organs at Risk Quality Improvement Middle Aged Deep Learning Head and Neck Neoplasms Observer Variation Radiotherapy, Intensity-Modulated Radiotherapy, Image-Guided Humans Neural Networks, Computer Tomography, X-Ray Computed Reproducibility of Results Aged Male
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Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy.

Medical physics Oct 26, 2020
PURPOSE: To develop a biological dose prediction model considering tissue bio-reactions in addition to patient anatomy for achieving a more comprehensive evaluation of tumor control and promoting the automatic planning of bulky lung cancer.
Lung Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Deep Learning Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiosurgery
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Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions.

Physics in medicine and biology Oct 21, 2020
This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image information, into a densely-connected network (DCNN). Firstly, a four-channel feature map, consisting of a PT...
Tomography, X-Ray Computed Image Processing, Computer-Assisted Esophageal Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Neural Networks, Computer Radiotherapy, Intensity-Modulated
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A deep learning model to predict dose-volume histograms of organs at risk in radiotherapy treatment plans.

Medical physics Oct 15, 2020
PURPOSE: To develop a deep learning-based model to predict achievable dose-volume histograms (DVHs) of organs at risk (OARs) for automation of inverse planning.
Radiotherapy, Intensity-Modulated Nasopharyngeal Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Deep Learning
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Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 8, 2020
PURPOSE: The delineation of the clinical target volume (CTV) is a crucial, laborious and subjective step in cervical cancer radiotherapy. The aim of this study was to propose and evaluate a novel end-to-end convolutional neural network (CNN) for full...
Radiotherapy Planning, Computer-Assisted Humans Algorithms Uterine Cervical Neoplasms Female Deep Learning Organs at Risk
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Complete abdomen and pelvis segmentation using U-net variant architecture.

Medical physics Oct 7, 2020
PURPOSE: Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs-at-risk, which is laborious and time...
Organs at Risk Image Processing, Computer-Assisted Abdomen Pelvis Humans Neural Networks, Computer Tomography, X-Ray Computed
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Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Sep 28, 2020
Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of dee...
Humans Deep Learning Tomography, X-Ray Computed Organs at Risk Breast Neoplasms Radiotherapy Planning, Computer-Assisted
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