AIMC Topic: Nasopharyngeal Carcinoma

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Characterization of unique pattern of immune cell profile in patients with nasopharyngeal carcinoma through flow cytometry and machine learning.

Journal of cellular and molecular medicine Jun 1, 2024
In patients with nasopharyngeal carcinoma (NPC), the alteration of immune responses in peripheral blood remains unclear. In this study, we established an immune cell profile for patients with NPC and used flow cytometry and machine learning (ML) to i...
Adult Male Middle Aged Machine Learning Female Aged Programmed Cell Death 1 Receptor Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Case-Control Studies CD8-Positive T-Lymphocytes Flow Cytometry Humans
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Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Technology in cancer research & treatment Jan 1, 2024
Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (...
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Radiometry Deep Learning Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Neural Networks, Computer
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Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.

Combinatorial chemistry & high throughput screening Jan 1, 2023
IMPORTANCE: Accurate pre-treatment prediction of distant metastasis in patients with Nasopharyngeal Carcinoma (NPC) enables the implementation of appropriate treatment strategies for high-risk individuals.
Humans Lymphatic Metastasis Magnetic Resonance Imaging Nasopharyngeal Carcinoma Retrospective Studies Reproducibility of Results Deep Learning Nasopharyngeal Neoplasms
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[Application of artificial intelligence in nasopharyngeal carcinoma].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery May 7, 2022
Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Humans Artificial Intelligence
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Quantitative Comparisons of Deep-learning-based and Atlas-based Auto- segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma.

Current medical imaging Jan 1, 2022
BACKGROUND: Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem.
Nasopharyngeal Neoplasms Humans Deep Learning Organs at Risk Radiotherapy Planning, Computer-Assisted Nasopharyngeal Carcinoma
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RE: A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma.

Journal of the National Cancer Institute Nov 29, 2021
Nasopharyngeal Neoplasms Deep Learning Carcinoma Nasopharyngeal Carcinoma Humans Prognosis
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A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma.

Journal of the National Cancer Institute May 4, 2021
BACKGROUND: Images from magnetic resonance imaging (MRI) are crucial unstructured data for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated a prognostic system based on the MRI features and clinical data of locoregi...
Induction Chemotherapy Retrospective Studies Deep Learning Antineoplastic Combined Chemotherapy Protocols Chemoradiotherapy Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Humans Prognosis
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Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy.

Technology in cancer research & treatment Jan 1, 2021
To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the Cycle...
Deep Learning Adolescent Cone-Beam Computed Tomography Image Processing, Computer-Assisted Neoplasm Staging Neoplasm Grading Adult Male Middle Aged Female Aged Young Adult Radiotherapy, Image-Guided Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Nasopharyngeal Carcinoma Humans Algorithms
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Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.

Molecular imaging and biology Jun 1, 2020
PURPOSE: To identify optimal machine learning methods for radiomics-based differentiation of local recurrence versus inflammation from post-treatment nasopharyngeal positron emission tomography/X-ray computed tomography (PET/CT) images.
Image Processing, Computer-Assisted Inflammation Diagnosis, Differential Female Male ROC Curve Nasopharyngeal Neoplasms Positron Emission Tomography Computed Tomography Neoplasm Recurrence, Local Nasopharyngeal Carcinoma Humans Machine Learning Algorithms Retrospective Studies
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[Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Feb 25, 2020
The segmentation of organs at risk is an important part of radiotherapy. The current method of manual segmentation depends on the knowledge and experience of physicians, which is very time-consuming and difficult to ensure the accuracy, consistency a...
Head Nasopharyngeal Carcinoma Image Processing, Computer-Assisted Neck Organs at Risk Humans Neural Networks, Computer Tomography, X-Ray Computed
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