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. 2024 Apr 29;14(9):924.
doi: 10.3390/diagnostics14090924.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis

Chih-Keng Wang  1   2 , Ting-Wei Wang  1   3 , Chia-Fung Lu  4 , Yu-Te Wu  3 , Man-Wei Hua  2
Affiliations

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis

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

Abstract

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Keywords: meta-analysis; prognostic models; radiomics; survival.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart for the current meta-analysis.
Figure 2
Figure 2
The results of QUIPS quality assessment for included studies.
Figure 3
Figure 3
Forest plot of subgroup analysis of radiomics prognosis models’ c-indexes with endpoint as moderator [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35].
Figure 3
Figure 3
Forest plot of subgroup analysis of radiomics prognosis models’ c-indexes with endpoint as moderator [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35].
Figure 4
Figure 4
Bubble plot of feature number on radiomics prognosis models with Overall Survival as endpoint.
See this image and copyright information in PMC

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