AIMC Topic: Nasopharyngeal Neoplasms
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A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.
Medical physics
Feb 9, 2022
PURPOSE: To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy, including dose prediction via a deep learning and voxel-based optimization strategy.
DCNet: Densely Connected Deep Convolutional Encoder-Decoder Network for Nasopharyngeal Carcinoma Segmentation.
Sensors (Basel, Switzerland)
Nov 26, 2021
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractab...
Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Nov 24, 2021
BACKGROUND: Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the predictive performance of the computed tomography (CT)-based model using deep learning features for ide...
A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma.
Physics in medicine and biology
Nov 11, 2021
Megavoltage computed tomography (MV-CT) is used for setup verification and adaptive radiotherapy in tomotherapy. However, its low contrast and high noise lead to poor image quality. This study aimed to develop a deep-learning-based method to generate...
Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy.
The Laryngoscope
Oct 8, 2021
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasoph...
A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study.
EBioMedicine
Aug 11, 2021
BACKGROUND: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal ...
Deep Learning-Guided Fiberoptic Raman Spectroscopy Enables Real-Time Diagnosis and Assessment of Nasopharyngeal Carcinoma and Post-treatment Efficacy during Endoscopy.
Analytical chemistry
Jul 28, 2021
In this work, we develop a deep learning-guided fiberoptic Raman diagnostic platform to assess its ability of real-time nasopharyngeal carcinoma (NPC) diagnosis and post-treatment follow-up of NPC patients. The robust Raman diagnostic platform is es...
The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.
Radiation oncology (London, England)
Jun 23, 2021
PURPOSE: To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.
An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features.
Oral oncology
May 21, 2021
OBJECTIVES: We aimed to build a survival system by combining a highly-accurate machine learning (ML) model with explainable artificial intelligence (AI) techniques to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma (NPC...
DVHnet: A deep learning-based prediction of patient-specific dose volume histograms for radiotherapy planning.
Medical physics
Apr 18, 2021
PURPOSE: To develop a deep learning method to predict patient-specific dose volume histograms (DVHs) for radiotherapy planning.
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