AIMC Topic: Head and Neck Neoplasms

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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 Image Interpretation, Computer-Assisted Artifacts Quality Assurance, Health Care Automation Tomography, X-Ray Computed Head and Neck Neoplasms Dental Implants
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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.
Multimodal Imaging Image Processing, Computer-Assisted Head and Neck Neoplasms Magnetic Resonance Imaging Humans Tomography, X-Ray Computed Deep Learning
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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...
Humans ROC Curve Lymph Nodes Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Tomography, X-Ray Computed Reproducibility of Results Deep Learning Lymphatic Metastasis Neoplasm Staging Extranodal Extension
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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...
Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Humans Male Deep Learning
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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 Adult Male Middle Aged Deep Learning Adolescent Head and Neck Neoplasms Female Aged Young Adult 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.
Humans Regression Analysis Machine Learning Sensitivity and Specificity Genital Neoplasms, Female Female Data Accuracy Workload Head and Neck Neoplasms Radiotherapy Planning, Computer-Assisted Reproducibility of Results Radiotherapy, Intensity-Modulated Quality Assurance, Health Care Poisson Distribution
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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...
Treatment Outcome Head and Neck Neoplasms Clinical Audit Machine Learning Carcinoma, Squamous Cell Postoperative Complications Humans
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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. ...
Radiotherapy, Intensity-Modulated Knowledge Bases Humans Automation Radiotherapy Planning, Computer-Assisted Lung Neoplasms Radiotherapy Dosage Head and Neck Neoplasms
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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...
Magnetic Resonance Imaging Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiometry Tomography, X-Ray Computed Image Processing, Computer-Assisted Head and Neck Neoplasms Humans Neural Networks, Computer
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Benefits of deep learning for delineation of organs at risk in head and neck cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology May 27, 2019
PURPOSE/OBJECTIVE: Precise delineation of organs at risk (OARs) in head and neck cancer (HNC) is necessary for accurate radiotherapy. Although guidelines exist, significant interobserver variability (IOV) remains. The aim was to validate a 3D convolu...
Middle Aged Organs at Risk Deep Learning Aged Humans Aged, 80 and over Head and Neck Neoplasms Observer Variation Neural Networks, Computer Male Female
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