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Review
. 2025 Jul 21;61(7):1310.
doi: 10.3390/medicina61071310.

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

Irina Maria Pușcaș  1 , Anda Gâta  1 , Alexandra Roman  2 , Silviu Albu  1 , Vlad Alexandru Gâta  3 , Alexandru Irimie  3
Affiliations
Review

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

Irina Maria Pușcaș et al. Medicina (Kaunas). .

Abstract

Nasopharyngeal carcinoma (NPC) represents a prevalent malignant tumor within the head and neck region, and enhancing the precision of prognostic assessments is a critical objective. Recent advancements in the integration of artificial intelligence (AI) and medical imaging have spurred a surge in research focusing on NPC image analysis through AI applications, particularly employing radiomics and artificial neural network approaches. This review provides a detailed examination of the prognostic advancement in NPC, utilizing imaging studies based on radiomics and deep learning techniques. The findings from these studies offer a promising outlook for achieving exceptionally precise prognoses regarding survival and treatment responses in NPC. The limitations of existing research and the potential for further application of radiomics and deep learning in NPC imaging are explored. It is recommended that future research efforts should aim to develop a comprehensive, labeled dataset of NPC images and prioritize studies that leverage AI for NPC screening.

Keywords: artificial intelligence; deep learning; nasopharyngeal carcinoma; prognosis; radiomics.

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Conflict of interest statement

The authors declare no conflict of interest.

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