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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Nasopharyngeal Carcinoma[original query]>>Original Studies[Product Type]
Early detection of nasopharyngeal carcinoma through machine-learning-driven prediction model in a population-based healthcare record database.
Jeng-Wen Chen et al. Cancer Med 2024 13(7) e7144
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Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.
Zhiwei Xiao et al. Transl Cancer Res 2024 12(12) 3581-3590
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[Construction and evaluation of an artificial intelligence-based risk prediction model for death in patients with nasopharyngeal cancer].
H Zhang et al. Nan fang yi ke da xue xue bao = Journal of Southern Medical University 2023 43(2) 271-279
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Integrative Scoring System for Survival Prediction in Patients With Locally Advanced Nasopharyngeal Carcinoma: A Retrospective Multicenter Study.
Bin Zhang et al. JCO clinical cancer informatics 2023 7e2200015
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Building practical risk prediction models for nasopharyngeal carcinoma screening with patient graph analysis and machine learning.
Chen Anjun et al. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2022
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Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.
Hua Hong-Li et al. Combinatorial chemistry & high throughput screening 2022
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Whole-exome sequencing study of familial nasopharyngeal carcinoma and its implication for identifying high-risk individuals.
Wang Tong-Min et al. Journal of the National Cancer Institute 2022
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Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma.
Hu Qiyi et al. Cancers 2022 14(13)
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Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment.
Bao Dan et al. Cancer imaging : the official publication of the International Cancer Imaging Society 2022 22(1) 10
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MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma.
Liao Hai et al. Journal of magnetic resonance imaging : JMRI 2021
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A convolutional neural network combined with positional and textural attention for the fully automatic delineation of primary nasopharyngeal carcinoma on non-contrast-enhanced MRI.
Wong Lun M et al. Quantitative imaging in medicine and surgery 2021 11(9) 3932-3944
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A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study.
Zhong Lianzhen et al. EBioMedicine 2021 70103522
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Radiomics Model to Predict Early Progression of Nonmetastatic Nasopharyngeal Carcinoma after Intensity Modulation Radiation Therapy: A Multicenter Study.
Du Richard et al. Radiology. Artificial intelligence 2019 1(4) e180075
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A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma.
Qiang Mengyun et al. Journal of the National Cancer Institute 2020 Sep
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Combination of RERG and ZNF671 methylation rates in circulating cell-free DNA: A novel biomarker for screening of nasopharyngeal carcinoma.
Xu Yifei et al. Cancer science 2020 Jul 111(7) 2536-2545
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Germline Polymorphisms and Length of Survival of Nasopharyngeal Carcinoma: An Exome-Wide Association Study in Multiple Cohorts.
Guo Yun-Miao et al. Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2020 May 7(10) 1903727
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Clinical utility of serial analysis of circulating tumour cells for detection of minimal residual disease of metastatic nasopharyngeal carcinoma.
Ko Josephine Mun-Yee et al. British journal of cancer 2020 May
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Computer-Aided Pathological Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning.
Diao Songhui et al. The American journal of pathology 2020 Apr
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Effect of machine learning re-sampling techniques for imbalanced datasets in 18 F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients.
Xie Chenyi et al. European journal of nuclear medicine and molecular imaging 2020 Apr
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Survival impact of radiotherapy interruption in nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: A big-data intelligence platform-based analysis.
Yao Ji-Jin et al. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2019 132178-187
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Fully-Automated Segmentation of Nasopharyngeal Carcinoma on Dual-Sequence MRI Using Convolutional Neural Networks.
Ye Yufeng et al. Frontiers in oncology 2020 10166
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Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis.
Cui Chunyan et al. BioMed research international 2020 20208068913
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Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.
Lin Li et al. Radiology 2019 291(3) 677-686
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Risk Assessment of Secondary Primary Malignancies in Nasopharyngeal Carcinoma: A Big-Data Intelligence Platform-Based Analysis of 6,377 Long-term Survivors from an Endemic Area Treated with Intensity-Modulated Radiation Therapy during 2003-2013.
Zhang Lu-Lu et al. Cancer research and treatment : official journal of Korean Cancer Association 2019 Jul 51(3) 982-991
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Competing risk nomograms for nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: A big-data, intelligence platform-based analysis.
Huang Xiao-Dan et al. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2018 129(2) 389-395
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Molecular Screening of Nasopharyngeal Carcinoma: Detection of LMP-1, LMP-2 Gene Expression in Vietnamese Nasopharyngeal Swab Samples.
Lao Thuan Duc et al. Asian Pacific journal of cancer prevention : APJCP 2019 20(9) 2757-2761
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The four‑microRNA signature identified by bioinformatics analysis predicts the prognosis of nasopharyngeal carcinoma patients.
Zhang Siwei et al. Oncology reports 2019 Sep
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Spontaneous remission of residual post-therapy plasma Epstein-Barr virus DNA and its prognostic implication in nasopharyngeal carcinoma: A large-scale, big-data intelligence platform-based analysis.
Zhang Yuan et al. International journal of cancer 2019 144(9) 2313-2319
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A Prognostic Bio-Model Based on SQSTM1 and N-Stage Identifies Nasopharyngeal Carcinoma Patients at High Risk of Metastasis for Additional Induction Chemotherapy.
Yang Qi et al. Clinical cancer research : an official journal of the American Association for Cancer Research 2018 24(3) 648-658
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Clinical features and survival outcomes between ascending and descending types of nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: A big-data intelligence platform-based analysis.
Yao Ji-Jin et al. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2019 May 137137-144
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 18, 2024
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Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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