Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could m...
OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (te...
BACKGROUND: According to the 7th edition of the American Joint Committee on Cancer (AJCC) staging system, over 50% of patients with nasopharyngeal carcinoma (NPC) have N1 disease at initial diagnosis. However, patients with N1 NPC are relatively unde...
We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were ex...
Identifying patients at high risk of an elevated lymph node ratio (LNR) is critical for optimizing the management of nasopharyngeal carcinoma (NPC), as LNR, defined as the ratio of metastatic to examined lymph nodes, serves as a key prognostic indica...
Epstein-Barr Virus (EBV) is the first tumor virus discovered in humans, which is closely related to the occurrence of a variety of malignant tumors, especially Nasopharyngeal Carcinoma (NPC). Epstein-Barr Virus (EBV)-associated NPC accounts for appro...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
Nasopharyngeal carcinoma (NPC) is a malignant tumor that originates from the back of the nasal canal from above the soft palate to the upper larynx. Because the nasopharyngeal location is deeply hidden, it is often difficult for a single imaging mean...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Aug 1, 2025
PURPOSE: To develop a combined dosiomics and deep learning (DL) model for predicting radiation dermatitis (RD) of grade ≥ 2 in patients with nasopharyngeal carcinoma (NPC) after radiation therapy (RT) based on radiation dose distribution.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Aug 1, 2025
PURPOSE: To develop an integrative radiopathomic model based on deep learning to predict overall survival (OS) in locally advanced nasopharyngeal carcinoma (LANPC) patients.
PURPOSE: To evaluate the diagnostic performance of the PET Assisted Reporting System (PARS) in nasopharyngeal carcinoma (NPC) patients without distant metastasis, and to investigate the prognostic significance of the metabolic parameters.
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