AIMC Topic: Nasopharyngeal Carcinoma

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Machine Learning Accelerates Discovery of High-Performance Corrole Photosensitizers for Optical Imaging Diagnosis and Photodynamic Therapeutics of Nasopharyngeal Carcinoma.

The journal of physical chemistry letters
This study centers on corrole, an emerging photosensitizer with great application potential, and innovatively develops an intelligent machine learning-based screening strategy. Through integrating molecular descriptor generation, feature engineering,...

Integrated machine learning and single-cell analysis identify chromatin-remodeling gene signature for diagnosis and prognosis in nasopharyngeal carcinoma.

Clinical and experimental medicine
This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple data...

Development and validation of a machine learning-based model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma.

Radiation oncology (London, England)
BACKGROUND AND PURPOSE: This study aims to develop a robust and user-friendly prediction model for radiation-induced hypothyroidism (RIHT) in nasopharyngeal carcinoma (NPC) patients.

Genetic algorithm-optimized neural network outperforms TNM staging in predicting rapidly progressive nasopharyngeal carcinoma: Reassessing adjuvant chemotherapy benefit via propensity score matching.

European journal of cancer (Oxford, England : 1990)
PURPOSE: To establish machine learning-based predictive models for rapidly progressive nasopharyngeal carcinoma (RP-NPC), defined as disease progression within 24 months post-initial treatment, and to assess differential survival benefits of adjuvant...

A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation.

Scientific data
Multi-modality magnetic resonance imaging(MRI) data facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC). The lack of publicly available, comprehensive datasets limits advancement...

Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.

World journal of surgical oncology
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
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
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...

Nasopharyngeal cancer screening and immunotherapy efficacy evaluation based on plasma separation combined with label-free SERS technology.

Analytica chimica acta
BACKGROUND: In recent years, significant progress has been made in the treatment of nasopharyngeal carcinoma (NPC). The application of immunotherapy, especially the use of Programmed Cell Death Protein 1 inhibitors, has demonstrated excellent therape...