AIMC Topic: Nasopharyngeal Carcinoma
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Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.
World journal of surgical oncology
Jun 18, 2025
BACKGROUND: Nasopharyngeal carcinoma (NPC) exhibits unique histopathological characteristics compared to other head and neck cancers. The prognosis of NPC patients after intensity-modulated radiation therapy (IMRT) has not been fully studied, and the...
A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning.
eLife
Jun 18, 2025
Radiotherapy resistance in nasopharyngeal carcinoma (NPC) is a major cause of recurrence and metastasis. Identifying radiotherapy-related biomarkers is crucial for improving patient survival outcomes. This study developed the nasopharyngeal carcinoma...
Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma.
Science progress
May 8, 2025
ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of mult...
Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Apr 8, 2025
BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy (RIHN) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).
An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating.
Medical physics
Apr 4, 2025
BACKGROUND: Previous knowledge-based planning studies have demonstrated the feasibility of predicting three-dimensional photon dose distributions and subsequently generating treatment plans. The steepness of dose fall-off represents a critical metric...
Control of dental calculus Prevents severe Radiation-Induced oral mucositis in patients undergoing radiotherapy for nasopharyngeal carcinoma.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Mar 27, 2025
PURPOSE: This study aims to develop an artificial intelligence model to predict severe radiation-induced oral mucositis (RIOM) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) and verify the risk factors associated with severe RIOM...
Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 24, 2025
BACKGROUND: To investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MRI) and optimize a layered management strategy for follow-up with a deep learning model.
CQENet: A segmentation model for nasopharyngeal carcinoma based on confidence quantitative evaluation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 13, 2025
Accurate segmentation of the tumor regions of nasopharyngeal carcinoma (NPC) is of significant importance for radiotherapy of NPC. However, the precision of existing automatic segmentation methods for NPC remains inadequate, primarily manifested in t...
Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma.
PloS one
Jan 27, 2025
BACKGROUND: Ras-GTPase-activating protein (GAP)-binding protein 1 (G3BP1) emerges as a pivotal oncogenic gene across various malignancies, notably including nasopharyngeal carcinoma (NPC). The use of automated image analysis tools for immunohistochem...
Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods.
Scientific reports
Jan 13, 2025
The present study analyzed the impact of age on the causes of death (CODs) in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy (CRT) using machine learning approaches. A total of 2841 patients (1037 classified as older, ≥ 60 ...
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