AIMC Topic: Nasopharyngeal Neoplasms

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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.
Humans Radiotherapy Dosage Radiotherapy, Intensity-Modulated Nasopharyngeal Neoplasms Deep Learning Organs at Risk Radiotherapy Planning, Computer-Assisted
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Verification of the machine delivery parameters of a treatment plan via deep learning.

Physics in medicine and biology Sep 30, 2020
We developed a generative adversarial network (GAN)-based deep learning approach to estimate the multileaf collimator (MLC) aperture and corresponding monitor units (MUs) from a given 3D dose distribution. The proposed design of the adversarial netwo...
Phantoms, Imaging Nasopharyngeal Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Radiotherapy, Intensity-Modulated Reproducibility of Results Deep Learning Lung Neoplasms Rectal Neoplasms
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Editorial for "MRI-Based Deep Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma".

Journal of magnetic resonance imaging : JMRI Sep 17, 2020
Humans Nasopharyngeal Neoplasms Disease-Free Survival Chemoradiotherapy Deep Learning Nasopharyngeal Carcinoma Magnetic Resonance Imaging
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MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

Journal of magnetic resonance imaging : JMRI Aug 9, 2020
BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC).
Nasopharyngeal Neoplasms Deep Learning Epstein-Barr Virus Infections Herpesvirus 4, Human Magnetic Resonance Imaging Chemoradiotherapy Nasopharyngeal Carcinoma Humans Retrospective Studies
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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.
Retrospective Studies Knowledge Bases Organs at Risk Humans Machine Learning Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated
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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 Neoplasms Magnetic Resonance Imaging Nasopharyngeal Carcinoma Humans Retrospective Studies Deep Learning
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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).
Magnetic Resonance Spectroscopy Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Humans Prognosis Deep Learning Nomograms
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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 Carcinoma Nasopharyngeal Neoplasms Humans Deep Learning Magnetic Resonance Imaging Retrospective Studies Neoplasm Staging
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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.
Predictive Value of Tests Aftercare Models, Biological Image Interpretation, Computer-Assisted Radiation Injuries Nasopharyngeal Carcinoma Longitudinal Studies Humans Magnetic Resonance Imaging Brain Injuries Middle Aged Female Nasopharyngeal Neoplasms ROC Curve Adult Temporal Lobe Machine Learning Male Algorithms Retrospective Studies
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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...
Diagnosis, Computer-Assisted Databases, Factual Deep Learning Reproducibility of Results Nasopharyngeal Carcinoma Sensitivity and Specificity Humans Nasopharyngeal Neoplasms Neural Networks, Computer
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