Research graph
References from Integrating computed tomography image features improves clinical prediction models for outcomes in nasopharyngeal carcinoma patients treated with (chemo)radiation. Local targets link to admitted publications; unresolved targets remain external evidence.
Prospective randomized study of intensity-modulated radiotherapy on salivary gland function in early-stage nasopharyngeal carcinoma patients
10.1200/jco.2007.11.5501 · 2007 · External reference
How does intensity-modulated radiotherapy versus conventional two-dimensional radiotherapy influence the treatment results in nasopharyngeal carcinoma patients?
10.1016/j.ijrobp.2010.03.024 · 2011 · External reference
Evolution of treatment for nasopharyngeal cancer – success and setback in the intensity-modulated radiotherapy era
10.1016/j.radonc.2014.02.003 · 2014 · External reference
The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging
2017 · External reference
Bioinformatics analysis based on ferroptosis-related lncRNAs: construction of a clinical prognostic model for nasopharyngeal carcinoma and correlation analysis
10.21037/tcr-22-1196 · 2022 · External reference
Radiomics: extracting more information from medical images using advanced feature analysis
10.1016/j.ejca.2011.11.036 · 2012 · External reference
Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters
10.1016/j.radonc.2017.07.013 · 2017 · External reference
CT-based radiomics nomogram for prediction of progression-free survival in locoregionally advanced nasopharyngeal carcinoma
10.2147/cmar.s325373 · 2021 · External reference
A clinical-radiomics nomogram based on computed tomography for predicting risk of local recurrence after radiotherapy in nasopharyngeal carcinoma
2021 · External reference
Tumor prognostic prediction of nasopharyngeal carcinoma using CT-based radiomics in non-Chinese patients
10.3389/fonc.2022.775248 · 2022 · External reference
Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma
10.1007/s00066-021-01874-2 · 2022 · External reference
Prediction of treatment outcome in nasopharyngeal carcinoma patients
10.1186/s12885-026-15570-w · 2026 · External reference
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · 2017 · External reference
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
10.1038/ncomms5006 · 2014 · External reference
The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients
10.1016/j.phro.2025.100733 · 2025 · External reference
A systematic study of the class imbalance problem in convolutional neural networks
10.1016/j.neunet.2018.07.011 · 2018 · External reference
Deep learning in medical image analysis
10.1146/annurev-bioeng-071516-044442 · 2017 · External reference
The addition of pretreatment plasma Epstein–Barr virus DNA into the eighth edition of nasopharyngeal cancer TNM stage classification
10.1002/ijc.31856 · 2019 · External reference
