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Integrating Radiomics and Deep-Learning for Prognostic Evaluation in Nasopharyngeal Carcinoma.

Irina Maria Pușcaș¹, Anda Gâta¹, Alexandra Roman²

¹Department of Otorhinolaryngology, University of Medicine and Pharmacy "Iuliu Hatieganu", 400349 Cluj-Napoca, Romania.

Medicina (Kaunas, Lithuania)

|July 30, 2025

Summary

Artificial intelligence (AI) enhances nasopharyngeal carcinoma (NPC) prognosis using radiomics and deep learning on medical images. This approach promises more accurate predictions for survival and treatment response in NPC patients.

Keywords:

artificial intelligence deep learning nasopharyngeal carcinoma prognosis radiomics

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Nasopharyngeal carcinoma (NPC) is a common head and neck cancer requiring improved prognostic accuracy.
Current prognostic methods for NPC lack precision in predicting patient outcomes.
Advancements in AI and medical imaging offer new avenues for NPC prognostication.

Purpose of the Study:

To review prognostic advancements in NPC using radiomics and deep learning on imaging data.
To assess the potential of AI-driven imaging analysis for precise NPC prognoses.
To explore limitations and future directions for AI in NPC imaging.

Main Methods:

Review of studies integrating radiomics and deep learning for NPC prognostic assessment.
Analysis of AI applications in medical imaging for nasopharyngeal carcinoma.
Examination of imaging biomarkers and artificial neural network models in NPC.

Main Results:

Radiomics and deep learning show significant promise for precise NPC prognostication.
AI-based imaging analysis can improve predictions of survival and treatment response in NPC.
Studies highlight the potential for enhanced accuracy in NPC outcome prediction.

Conclusions:

AI, radiomics, and deep learning offer a powerful toolkit for improving NPC prognosis.
Further research requires comprehensive, labeled NPC image datasets for AI development.
Future efforts should focus on AI for NPC screening and early detection.