AIMC Topic: Head and Neck Neoplasms

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Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients.

European journal of nuclear medicine and molecular imaging Feb 9, 2021
PURPOSE: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV...
Humans Positron Emission Tomography Computed Tomography Tumor Burden Observer Variation Deep Learning Head and Neck Neoplasms
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Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer.

International journal of radiation oncology, biology, physics Feb 6, 2021
PURPOSE: Accurate lymph node (LN) malignancy classification is essential for treatment target identification in head and neck cancer (HNC) radiation therapy. Given the constraints imposed by relatively small sample sizes in real-world medical applica...
ROC Curve Lymph Nodes Neural Networks, Computer Lymphatic Metastasis Head and Neck Neoplasms Male Positron Emission Tomography Computed Tomography Deep Learning Humans
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The implementation of TORS for head and neck surgery in Thailand.

Journal of robotic surgery Feb 2, 2021
Transoral robotic surgery (TORS) is a novel surgical treatment of head and neck cancers, mainly for limited tumor in oropharynx and supraglottis. Despite the major advantage of favorable postoperative functional outcomes, many obstacles exist during ...
Head and Neck Neoplasms Robotics Robotic Surgical Procedures Humans Retrospective Studies Thailand
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Application of radiomics and machine learning in head and neck cancers.

International journal of biological sciences Jan 1, 2021
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition...
Humans Machine Learning Head and Neck Neoplasms
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Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.

Physics in medicine and biology Dec 11, 2020
Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning, but manual delineation is tedious, slow, and inconsistent. A self-channel-and-spatial-attention neural network (SCSA-Net) is devel...
Humans Neural Networks, Computer Organs at Risk Tomography, X-Ray Computed Image Processing, Computer-Assisted Head and Neck Neoplasms
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miRNA-Based Feature Classifier Is Associated with Tumor Mutational Burden in Head and Neck Squamous Cell Carcinoma.

BioMed research international Dec 9, 2020
Tumor mutation burden (TMB) is considered to be an independent genetic biomarker that can predict the tumor patient's response to immune checkpoint inhibitors (ICIs). Meanwhile, microRNA (miRNA) plays a key role in regulating the anticancer immune re...
Mutation Male Middle Aged MicroRNAs Immunotherapy Female Humans Regression Analysis Squamous Cell Carcinoma of Head and Neck Aged Machine Learning Head and Neck Neoplasms Biomarkers, Tumor
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Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients.

Physics in medicine and biology Dec 5, 2020
Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep c...
Proton Therapy Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Cone-Beam Computed Tomography Image Processing, Computer-Assisted Head and Neck Neoplasms Humans Adult Magnetic Resonance Imaging Neural Networks, Computer Male Organs at Risk Middle Aged Female Aged
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Strategies to improve deep learning-based salivary gland segmentation.

Radiation oncology (London, England) Dec 1, 2020
BACKGROUND: Deep learning-based delineation of organs-at-risk for radiotherapy purposes has been investigated to reduce the time-intensiveness and inter-/intra-observer variability associated with manual delineation. We systematically evaluated ways ...
Head and Neck Neoplasms Organs at Risk Humans Observer Variation Deep Learning Salivary Glands
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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...
Deep Learning Head and Neck Neoplasms Prostatic Neoplasms Organs at Risk Quality Improvement Aged Male Middle Aged Tomography, X-Ray Computed Reproducibility of Results Humans Neural Networks, Computer Observer Variation Radiotherapy, Intensity-Modulated Radiotherapy, Image-Guided
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Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network.

Physics in medicine and biology Oct 26, 2020
This study proposes a near-real-time spot-scanning proton dose calculation method with probabilistic uncertainty estimation using a three-dimensional convolutional neural network (3D-CNN). CT images and clinical target volume contours of 215 head and...
Humans Neural Networks, Computer Uncertainty Tomography, X-Ray Computed Head and Neck Neoplasms Proton Therapy
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