AIMC Topic: Nasopharyngeal Carcinoma

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A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.

Radiation oncology (London, England) Aug 3, 2020
BACKGROUND: To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy.
Machine Learning Retrospective Studies Knowledge Bases Humans Radiotherapy, Intensity-Modulated Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
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Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs.

Computer methods and programs in biomedicine Aug 2, 2020
BACKGROUND: Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited ...
Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Humans Retrospective Studies Deep Learning Magnetic Resonance Imaging
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A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Jul 4, 2020
PURPOSE: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT).
Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Deep Learning Nomograms Magnetic Resonance Spectroscopy Humans Prognosis
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Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images.

Journal of magnetic resonance imaging : JMRI Jun 24, 2020
BACKGROUND: Recent studies have shown that deep learning can help tumor staging automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due to the lack of large and slice-level annotated datasets.
Nasopharyngeal Neoplasms Neoplasm Staging Nasopharyngeal Carcinoma Humans Retrospective Studies Deep Learning Magnetic Resonance Imaging
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Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BMC cancer Jun 1, 2020
BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.
Brain Injuries Predictive Value of Tests Aftercare ROC Curve Longitudinal Studies Magnetic Resonance Imaging Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Temporal Lobe Models, Biological Image Interpretation, Computer-Assisted Radiation Injuries Algorithms Retrospective Studies Female Adult Male Middle Aged Humans Machine Learning
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Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning.

The American journal of pathology Apr 29, 2020
The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is often inefficient and inconsistent. We have therefore introduced a deep learning algorithm into this process and compared the performance of the model with that o...
Databases, Factual Reproducibility of Results Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning Nasopharyngeal Carcinoma Humans Neural Networks, Computer Nasopharyngeal Neoplasms
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Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.

Medical physics Feb 21, 2020
PURPOSE: To develop and evaluate a novel method for pseudo-CT generation from multi-parametric MR images using multi-channel multi-path generative adversarial network (MCMP-GAN).
Tumor Burden Nasopharyngeal Carcinoma Tomography, X-Ray Computed Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Neural Networks, Computer
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Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis.

BioMed research international Feb 21, 2020
We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification sys...
Nasopharynx Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Prognosis Retrospective Studies Female Humans Machine Learning Magnetic Resonance Imaging Neoplasm Staging Image Interpretation, Computer-Assisted Adult Male Middle Aged
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Cervical-transoral robotic nasopharyngectomy: A preclinical study.

Head & neck Nov 21, 2019
BACKGROUND: We performed a preclinical study to assess the feasibility of the cervical-transoral robotic pharyngectomy procedure in surgery for nasopharyngeal cancer, where deep margins and vascular safety are key issues.
Humans Robotics Robotic Surgical Procedures Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Pharyngectomy
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3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jul 31, 2019
In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images,...
Nasopharyngeal Carcinoma Deep Learning Humans Imaging, Three-Dimensional Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted
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