[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
IJRR Information::
For Authors::
For Reviewers::
Subscription::
News & Events::
Web Mail::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
ISSN
Hard Copy 2322-3243
Online 2345-4229
..
Online Submission
Now you can send your articles to IJRR office using the article submission system.
..

AWT IMAGE

AWT IMAGE

:: Volume 23, Issue 2 (5-2025) ::
Int J Radiat Res 2025, 23(2): 311-316 Back to browse issues page
The value of imaging techniques based on enhanced magnetic resonance imaging in the diagnosis and prediction response of radiotherapy for nasopharyngeal carcinoma
X. Liu , H. Pang , H. Zhou , J. Li , X. Lu , Y.R. Wang , P. Zhou , Y. Zhang
Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou City, China , zhouping11@swmu.edu.cn
Abstract:   (543 Views)
Background: The To evaluate the value of imaging techniques based on enhanced magnetic resonance imaging (MRI) in diagnosing and predicting radiotherapy for nasopharyngeal carcinoma (NPC). Materials and Methods: This study analyzed 142 hospitalized patient's clinical data with nasopharyngeal carcinoma from March 2020 to December 2021. The patients were divided into valid and invalid groups. Preoperative T2 weighted image (T2WI) images were used for imaging analysis, feature texture parameters were screened based on R language, and a short-term efficacy prediction model for the training group and the validation group was constructed. Results: A total of 432 different texture parameters were obtained by T2WI image analysis of each patient and 5 characteristic texture parameters were obtained by Least Absolute Shrinkage and Selection Operator (LASSO) dimensionality reduction and 10-fold cross-validation screening. Specific examples were standard deviation, Cluster Prominence Angle 135 Offset 4, Correlation Angle 135 offset 4, Inertia Angle 135 Offset 4. The prediction models were constructed and the area under the receiver operating characteristic (ROC) curve of the training group model was 0.826 (95% Confidence intervals (CI): 0.708-0.944). The sensitivity and specificity were 83.67% and 69.14%, respectively. The area under the ROC curve of the validation group model was 0.810 (95% CI: 0.682-0.938) and the sensitivity and specificity were 89.46% and 63.29%, respectively. Conclusion: The prediction model constructed has high predictive accuracy, sensitivity and specificity for diagnosing nasopharyngeal carcinoma. The use of enhanced magnetic resonance imaging (MRI)-based imaging to predict the short-term outcome after radiotherapy for NPC was feasible and the prediction model was stable and reliable.
Keywords: Magnetic resonance imaging, nasopharyngeal carcinoma, diagnosis, radiotherapy.
Full-Text [PDF 875 kb]   (149 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. Tsang CM, Lui VW, Bruce JP, et al. (2020) Translational genomics of nasopharyngeal cancer. Semin Cancer Biol, 61: 84-100. [DOI:10.1016/j.semcancer.2019.09.006]
2. Lee A, Chow JC, Lee NY (2021) Treatment deescalation strategies for nasopharyngeal cancer: a review. JAMA Oncol, 7: 445-53. [DOI:10.1001/jamaoncol.2020.6154]
3. Salehiniya H, Mohammadian M, Mohammadian-Hafshejani A, et al. (2018) Nasopharyngeal cancer in the world: epidemiology, incidence, mortality and risk factors. WCRJ, 5: e1046. [DOI:10.15419/bmrat.v5i6.447]
4. Chan AT, Hui EP, Ngan RK, et al. (2018) Analysis of plasma Epstein-Barr virus DNA in nasopharyngeal cancer after chemoradiation to identify high-risk patients for adjuvant chemotherapy: a randomized controlled trial. J Clin Oncol, 36: 3091-100. [DOI:10.1200/JCO.2018.77.7847]
5. Dong D, Zhang F, Zhong LZ, et al. (2019) Development and validation of a novel MR imaging predictor of response to induction chemotherapy in locoregionally advanced nasopharyngeal cancer: a randomized controlled trial substudy (NCT01245959). BMC Med, 17(1): 190. [DOI:10.1186/s12916-019-1422-6]
6. Li S, Xiao J, He L, et al. (2019) The tumor target segmentation of nasopharyngeal cancer in CT images based on deep learning methods. Technol Cancer Res Treat, 18: 1533033819884561. [DOI:10.1177/1533033819884561]
7. Lin D, Wu Q, Qiu S, et al. (2019) Label-free liquid biopsy based on blood circulating DNA detection using SERS-based nanotechnology for nasopharyngeal cancer screening. Nanomedicine, 22: 102100. [DOI:10.1016/j.nano.2019.102100]
8. Spadarella G, Calareso G, Garanzini E, et al. (2021) MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol, 140: 109744. [DOI:10.1016/j.ejrad.2021.109744]
9. Yarza R, Bover M, Agulló-Ortuño MT, et al. (2021) Current approach and novel perspectives in nasopharyngeal carcinoma: The role of targeting proteasome dysregulation as a molecular landmark in nasopharyngeal cancer. J Exp Clin Cancer Res, 40(1): 1-8. [DOI:10.1186/s13046-021-02010-9]
10. Huang S, Cao B, Zhang J, et al. (2021) Induction of ferroptosis in human nasopharyngeal cancer cells by cucurbitacin B: molecular mechanism and therapeutic potential. Cell Death Dis, 12(3): 237. [DOI:10.1038/s41419-021-03516-y]
11. Hui EP, Ma BB, Lam WJ, et al. (2021) Dynamic Changes of Post-Radiotherapy Plasma Epstein-Barr virus DNA in a randomized trial of adjuvant chemotherapy versus observation in nasopharyngeal cancer. Clin Cancer Res, 27(10): 2827-2836. [DOI:10.1158/1078-0432.CCR-20-3519]
12. Jiří K, Vladimír V, Michal A, et al. (2021) Proton pencil-beam scanning radiotherapy in the treatment of nasopharyngeal cancer: dosimetric parameters and 2-year results. Eur Arch Otorhinolaryngol, 278(3): 763-769. [DOI:10.1007/s00405-020-06175-5]
13. Blanchard P, Biau J, Huguet F, et al. (2022) Radiotherapy for nasopharyngeal cancer. Cancer Radiother, 26(1-2): 168-173. [DOI:10.1016/j.canrad.2021.08.009]
14. Liao KC, Chuang HC, Chien CY, et al. (2021) Quality of life as a mediator between cancer stage and long-term mortality in nasopharyngeal cancer patients treated with intensity-modulated radiotherapy. Cancers, 13(20): 5063. [DOI:10.3390/cancers13205063]
15. Spadarella G, Calareso G, Garanzini E, et al. (2021) MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol, 140: 109744. [DOI:10.1016/j.ejrad.2021.109744]
16. Aerts HJ, Velazquez ER, Leijenaar RT, et al. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 5: 4006. [DOI:10.1038/ncomms5006]
17. Coroller TP, Grossmann P, Hou Y, et al. (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol, 114(3): 345-350. [DOI:10.1016/j.radonc.2015.02.015]
18. Li H, Zhu Y, Burnside ES, et al. (2016) MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology, 281(2): 382-391. [DOI:10.1148/radiol.2016152110]
19. Allam AP, Ali Hassan HG, Mohamed BA. (2021) Role of CT and modified response evaluation criteria in solid tumors (RECIST criteria) in response evaluation of malignant pleural mesothelioma. QJM, 114(Supplement-1): hcab106-018. [DOI:10.1093/qjmed/hcab106.018]
20. Lee S, Choi Y, Seo MK, et al. (2022) Magnetic resonance imaging-based radiomics for the prediction of progression-free survival in patients with nasopharyngeal carcinoma: A systematic review and meta-analysis. Cancers, 14: 653. [DOI:10.3390/cancers14030653]
21. Hou J, Li H, Zeng B, et al. (2022) MRI-based radiomics nomogram for predicting temporal lobe injury after radiotherapy in nasopharyngeal carcinoma. Eur Radiol, 32(2): 1106-1114. [DOI:10.1007/s00330-021-08254-5]
22. Wu X, Dong D, Zhang L, et al. (2021) Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: a multicenter study. Med Phys, 48(5): 2374-2385. [DOI:10.1002/mp.14767]
23. Bao D, Zhao Y, Liu Z, et al. (2021) Prognostic and predictive value of radiomics features at MRI in nasopharyngeal carcinoma. Discov Oncol, 12(1): 63. [DOI:10.1007/s12672-021-00460-3]
24. Duan W, Xiong B, Tian T, et al. (2022) Radiomics in nasopharyngeal carcinoma. Clin Med Insights Oncol, 16: 11795549221079186. [DOI:10.1177/11795549221079186]
25. Yan C, Shen DS, Chen XB, et al. (2021) CT-based radiomics nomogram for prediction of progression-free survival in locoregionally advanced nasopharyngeal carcinoma. Cancer Manag Res, 2021: 6911-23. [DOI:10.2147/CMAR.S325373]
26. Lam SK, Zhang J, Zhang YP, et al. (2022) A multi-center study of CT-based neck nodal radiomics for predicting an adaptive radiotherapy trigger of ill-fitted thermoplastic masks in patients with nasopharyngeal carcinoma. Life, 12: 241. [DOI:10.3390/life12020241]
27. Wang Y, Li C, Yin G, et al. (2022) Extraction parameter optimized radiomics for neoadjuvant chemotherapy response prognosis in advanced nasopharyngeal carcinoma. Clin Transl Radiat Oncol, 33:37-44. [DOI:10.1016/j.ctro.2021.12.005]
28. Wu S, Li H, Dong A, et al. (2021) Differences in radiomics signatures between patients with early and advanced T‐stage nasopharyngeal carcinoma facilitate prognostication. J Magn Reson Imaging, 56(1): 221-222. [DOI:10.1002/jmri.27633]
29. Wu S, Li H, Dong A, et al. (2021) Differences in radiomics signatures between patients with early and advanced T‐stage nasopharyngeal carcinoma facilitate prognostication. Magn Reson Imaging, 56(1): 221-222. [DOI:10.1002/jmri.27633]
30. Bao D, Liu Z, Geng Y, et al. (2022) Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment. Cancer Imaging, 22(1): 10. [DOI:10.1186/s40644-022-00448-4]
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML     Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Liu X, Pang H, Zhou H, Li J, Lu X, Wang Y, et al . The value of imaging techniques based on enhanced magnetic resonance imaging in the diagnosis and prediction response of radiotherapy for nasopharyngeal carcinoma. Int J Radiat Res 2025; 23 (2) :311-316
URL: http://ijrr.com/article-1-6233-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 23, Issue 2 (5-2025) Back to browse issues page
International Journal of Radiation Research
Persian site map - English site map - Created in 0.04 seconds with 50 queries by YEKTAWEB 4714