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Head and Neck Neoplasms

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Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.

International journal of radiation oncology, biology, physics Jan 24, 2020
PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning.
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Head and Neck Neoplasms Knowledge Bases Humans Radiotherapy Planning, Computer-Assisted
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Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth.

Physics in medicine and biology Jan 10, 2020
Enabling automated pipelines, image analysis and big data methodology in cancer clinics requires thorough understanding of the data. Automated quality assurance steps could improve the efficiency and robustness of these methods by verifying possible ...
Humans Neural Networks, Computer Tomography, X-Ray Computed Artifacts Quality Assurance, Health Care Automation Head and Neck Neoplasms Dental Implants Image Interpretation, Computer-Assisted
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Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.

Medical physics Jan 2, 2020
PURPOSE: To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.
Humans Tomography, X-Ray Computed Head and Neck Neoplasms Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging
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Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology Dec 9, 2019
PURPOSE: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents...
Head and Neck Neoplasms Lymphatic Metastasis Neoplasm Staging ROC Curve Lymph Nodes Squamous Cell Carcinoma of Head and Neck Extranodal Extension Humans Tomography, X-Ray Computed Reproducibility of Results Deep Learning
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Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 5, 2019
BACKGROUND: Deep learning-based auto-segmented contours (DC) aim to alleviate labour intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most previous DC validation studies have a limited number of expert observers for com...
Deep Learning Head and Neck Neoplasms Organs at Risk Humans Male Radiotherapy Planning, Computer-Assisted Observer Variation
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Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 22, 2019
INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms th...
Radiotherapy Planning, Computer-Assisted Observer Variation Neck Neoplasm Staging Organs at Risk Deep Learning Adolescent Head and Neck Neoplasms Female Aged Young Adult Adult Male Middle Aged Humans Neural Networks, Computer Algorithms
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Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy.

International journal of radiation oncology, biology, physics Aug 1, 2019
PURPOSE: To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans.
Head and Neck Neoplasms Poisson Distribution Regression Analysis Workload Radiotherapy Planning, Computer-Assisted Data Accuracy Female Reproducibility of Results Sensitivity and Specificity Humans Quality Assurance, Health Care Machine Learning Genital Neoplasms, Female Radiotherapy, Intensity-Modulated
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Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck.

The British journal of oral & maxillofacial surgery Jul 26, 2019
Most surgical specialties have attempted to address concerns about unfair comparison of outcomes by "risk-adjusting" data to benchmark specialty-specific outcomes that are indicative of the quality of care. We are building on previous work in head an...
Clinical Audit Humans Postoperative Complications Machine Learning Treatment Outcome Carcinoma, Squamous Cell Head and Neck Neoplasms
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Automated 4π radiotherapy treatment planning with evolving knowledge-base.

Medical physics Jul 26, 2019
PURPOSE: Non-coplanar 4π radiotherapy generalizes intensity modulated radiation therapy (IMRT) to automate beam geometry selection but requires complicated hyperparameter tuning to attain superior plan quality, which can be tedious and inconsistent. ...
Humans Head and Neck Neoplasms Automation Lung Neoplasms Radiotherapy Dosage Knowledge Bases Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network.

Medical physics Jul 9, 2019
PURPOSE: To develop and evaluate a patch-based convolutional neural network (CNN) to generate synthetic computed tomography (sCT) images for magnetic resonance (MR)-only workflow for radiotherapy of head and neck tumors. A patch-based deep learning m...
Radiometry Humans Neural Networks, Computer Tomography, X-Ray Computed Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Image Processing, Computer-Assisted Head and Neck Neoplasms Magnetic Resonance Imaging
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