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Review
. 2024 May 17;11(5):504.
doi: 10.3390/bioengineering11050504.

Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

Chih-Keng Wang  1   2 , Ting-Wei Wang  1   3 , Ya-Xuan Yang  3 , Yu-Te Wu  3
Affiliations
Review

Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

Chih-Keng Wang et al. Bioengineering (Basel). .

Abstract

Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice.

Keywords: convolutional neural networks (CNNs); deep learning (DL); magnetic resonance imaging (MRI); nasopharyngeal carcinoma (NPC); segmentation.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart for study selection.
Figure 2
Figure 2
Forest plot of Dice scores for deep learning algorithms in independent datasets [12,83,84,85,86,87,88,91,92,93,94,97].
See this image and copyright information in PMC

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