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

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2024 May 11

PMID 38732337

Authors

Chih-Keng Wang

Ting-Wei Wang

Chia-Fung Lu

Yu-Te Wu

Man-Wei Hua

Affiliations

Soon will be listed here.

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.

References

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

Yang K, Tian J, Zhang B, Li M, Xie W, Zou Y . A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma. Oral Oncol. 2019; 98:85-91. DOI: 10.1016/j.oraloncology.2019.09.022. View

Liu J, Mao Y, Li Z, Zhang D, Zhang Z, Hao S . Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging. 2016; 44(2):445-55. DOI: 10.1002/jmri.25156. View

Zhang J, Lam S, Teng X, Ma Z, Han X, Zhang Y . Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopharyngeal carcinoma patients. Radiother Oncol. 2023; 183:109578. DOI: 10.1016/j.radonc.2023.109578. View

Mao Y, Wang S, Lydiatt W, Shah J, Colevas A, Lee A . Unambiguous advanced radiologic extranodal extension determined by MRI predicts worse outcomes in nasopharyngeal carcinoma: Potential improvement for future editions of N category systems. Radiother Oncol. 2021; 157:114-121. DOI: 10.1016/j.radonc.2021.01.015. View

Nair J, Vallieres M, Mascarella M, El Sabbagh N, Duchatellier C, Zeitouni A . Magnetic Resonance Imaging Texture Analysis Predicts Recurrence in Patients With Nasopharyngeal Carcinoma. Can Assoc Radiol J. 2019; 70(4):394-402. DOI: 10.1016/j.carj.2019.06.009. View

Deng Y, Huang Y, Jing B, Wu H, Qiu W, Chen H . Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter study. Eur J Radiol. 2023; 168:111084. DOI: 10.1016/j.ejrad.2023.111084. View

Khongwirotphan S, Oonsiri S, Kitpanit S, Prayongrat A, Kannarunimit D, Chakkabat C . Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma. PLoS One. 2024; 19(2):e0298111. PMC: 10861073. DOI: 10.1371/journal.pone.0298111. View

Wang A, Xu H, Zhang C, Ren J, Liu J, Zhou P . Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients. Clin Radiol. 2023; 78(9):e644-e653. DOI: 10.1016/j.crad.2023.05.012. View

10.

Luo X, Liao W, He Y, Tang F, Wu M, Shen Y . Deep learning-based accurate delineation of primary gross tumor volume of nasopharyngeal carcinoma on heterogeneous magnetic resonance imaging: A large-scale and multi-center study. Radiother Oncol. 2023; 180:109480. DOI: 10.1016/j.radonc.2023.109480. View

11.

Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L . Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett. 2017; 403:21-27. DOI: 10.1016/j.canlet.2017.06.004. View

12.

Zhang F, Zhong L, Zhao X, Dong D, Yao J, Wang S . A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study. Ther Adv Med Oncol. 2021; 12:1758835920971416. PMC: 7739087. DOI: 10.1177/1758835920971416. View

13.

Wang Y, Li C, Yin G, Wang J, Li J, Wang P . Extraction parameter optimized radiomics for neoadjuvant chemotherapy response prognosis in advanced nasopharyngeal carcinoma. Clin Transl Radiat Oncol. 2022; 33:37-44. PMC: 8728047. DOI: 10.1016/j.ctro.2021.12.005. View

14.

Pei W, Wang C, Liao H, Chen X, Wei Y, Huang X . MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma. BMC Cancer. 2022; 22(1):739. PMC: 9261049. DOI: 10.1186/s12885-022-09832-6. View

15.

Ouyang F, Guo B, Zhang B, Dong Y, Zhang L, Mo X . Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma. Oncotarget. 2017; 8(43):74869-74879. PMC: 5650385. DOI: 10.18632/oncotarget.20423. View

16.

Zhao L, Gong J, Xi Y, Xu M, Li C, Kang X . MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma. Eur Radiol. 2019; 30(1):537-546. DOI: 10.1007/s00330-019-06211-x. View

17.

Wu S, Li H, Dong A, Tian L, Ruan G, Liu L . Differences in Radiomics Signatures Between Patients with Early and Advanced T-Stage Nasopharyngeal Carcinoma Facilitate Prognostication. J Magn Reson Imaging. 2021; 54(3):854-865. DOI: 10.1002/jmri.27633. View

18.

Chen X, Li Y, Li X, Cao X, Xiang Y, Xia W . An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features. Oral Oncol. 2021; 118:105335. DOI: 10.1016/j.oraloncology.2021.105335. View

19.

Bologna M, Corino V, Calareso G, Tenconi C, Alfieri S, Iacovelli N . Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients. Cancers (Basel). 2020; 12(10). PMC: 7601980. DOI: 10.3390/cancers12102958. View

20.

Liu T, Dong D, Zhao X, Ou X, Yi J, Guan J . Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study. BMC Med. 2023; 21(1):464. PMC: 10683300. DOI: 10.1186/s12916-023-03164-3. View