Share：
Share this content in WeChat
X
[Chinese] [PDF] 27 1
• Clinical Article •
Application of MRI radiomics in predicting early response to IMRT combined with targeted therapy in locally advanced nasopharyngeal carcinoma
LIN Zijing  PAN Lingna  WANG Shuowen  YAN Huiru  XU Yiyu  PENG Xuhuai  LIN Fuxi  CHEN Zhiqiang 

Cite this article as LIN Z J, PAN L N, WANG S W, et al. Application of MRI radiomics in predicting early response to IMRT combined with targeted therapy in locally advanced nasopharyngeal carcinoma[J]. Chin J Magn Reson Imaging, 2026, 17(6): 63-70, 78. DOI:10.12015/issn.1674-8034.2026.06.008.


[Abstract] Objective This study aimed to develop and validate a multimodal combined model integrating clinical and radiomic features to predict the early response of patients with locally advanced nasopharyngeal carcinoma (LA-NPC) to intensity-modulated radiotherapy (IMRT) combined with targeted therapy.Materials and Methods A total of 121 patients with LA-NPC who received IMRT with concurrent targeted therapy were retrospectively enrolled. Patients were divided into an early responder group (n = 95) if their three-dimensional tumor volume reduction rate (TVRR) was ≥ 47%, and a non-responder group (n = 26) if TVRR < 47%. Pre-treatment baseline radiomics features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1WI + C) sequences. Strictly within a 5-fold cross-validation framework, feature selection and radiomics model construction were performed using variance thresholding, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) regression. Logistic regression was applied to select clinical indicators to develop a clinical model. Based on these features, the T2WI model, T1WI+C model, dual-sequence radiomics model, clinical model, and multimodal combined model were respectively constructed, and a nomogram was developed for the multimodal combined model. Model performance and clinical net benefit were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis, and the DeLong test was used to compare the AUCs among different models.Results Multivariate analysis ultimately retained 10 radiomics features and 4 clinical features (white blood cell count, platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio). The results of 5-fold cross-validation showed that the multimodal combined model exhibited the optimal predictive performance (mean AUC = 0.867), which was significantly superior to the clinical model alone (mean AUC = 0.612), the dual-sequence radiomics model (mean AUC = 0.760), the T2WI model (mean AUC = 0.773), and the T1WI+C model (mean AUC = 0.696) (all P < 0.05). The calibration curve demonstrated high consistency between the predicted probabilities of the nomogram and the actual observed probabilities. Decision curve analysis confirmed that the combined model yielded the best clinical net benefit across a wide range of threshold probabilities (0.1 ~ 0.8).Conclusions The multimodal combined model, integrating multimodal radiomics and clinical indicators, can non-invasively and effectively predict the early response to IMRT combined with targeted therapy in patients with LA-NPC. It holds promise to provide reliable decision support for the early identification of treatment-resistant populations and the formulation of individualized intervention strategies.
[Keywords] targeted therapy；radiomics；magnetic resonance imaging；treatment response prediction；nasopharyngeal carcinoma

LIN Zijing1   PAN Lingna1   WANG Shuowen1   YAN Huiru2   XU Yiyu2   PENG Xuhuai1   LIN Fuxi1   CHEN Zhiqiang1*  

1 Department of Radiology, the First Affiliated Hospital of Hainan Medical University, Key Laboratory of Emergency and Trauma of Ministry of Education, Haikou 570102, China

2 School of Clinical Medicine , Hainan Medical University; Haikou 570102, China

Corresponding author: CHEN Z Q, E-mail: zhiqiang_chen99@163.com

Conflicts of interest   None.

Received  2026-02-19
Accepted  2026-05-26
DOI: 10.12015/issn.1674-8034.2026.06.008
Cite this article as LIN Z J, PAN L N, WANG S W, et al. Application of MRI radiomics in predicting early response to IMRT combined with targeted therapy in locally advanced nasopharyngeal carcinoma[J]. Chin J Magn Reson Imaging, 2026, 17(6): 63-70, 78. DOI:10.12015/issn.1674-8034.2026.06.008.

[1]
CHEN Y P, CHAN A T C, LE Q T, et al. Nasopharyngeal carcinoma[J]. Lancet, 2019, 394(10192): 64-80. DOI: 10.1016/S0140-6736(19)30956-0.
[2]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[3]
YOUNG L S. A novel Epstein-Barr virus subtype associated with nasopharyngeal carcinoma found in South China[J]. Cancer Commun (Lond), 2020, 40(1): 60-62. DOI: 10.1002/cac2.12006.
[4]
BOSSI P, CHAN A T, LICITRA L, et al. Nasopharyngeal carcinoma: ESMO-EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2021, 32(4): 452-465. DOI: 10.1016/j.annonc.2020.12.007.
[5]
WANG F H, SHEN L, LI J, et al. The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer[J/OL]. Cancer Commun (Lond), 2019, 39(1): 10 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/30885279/. DOI: 10.1186/s40880-019-0349-9.
[6]
PAN J J, NG W T, ZONG J F, et al. Prognostic nomogram for refining the prognostication of the proposed 8th edition of the AJCC/UICC staging system for nasopharyngeal cancer in the era of intensity-modulated radiotherapy[J]. Cancer, 2016, 122(21): 3307-3315. DOI: 10.1002/cncr.30198.
[7]
CHUA D T T, SHAM J S T, AU G K H, et al. Concomitant chemoirradiation for stage III–IV nasopharyngeal carcinoma in Chinese patients: results of a matched cohort analysis[J]. Int J Radiat Oncol, 2002, 53(2): 334-343. DOI: 10.1016/S0360-3016(02)02710-4.
[8]
ZHU F B, WU Y D, WANG H. Advance in integrating platinum-based chemotherapy with radiotherapy for locally advanced nasopharyngeal carcinoma[J/OL]. Front Oncol, 2023, 13: 1259331 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/37860184/. DOI: 10.3389/fonc.2023.1259331.
[9]
MAO Y P, TANG L L, CHEN L, et al. Prognostic factors and failure patterns in non-metastatic nasopharyngeal carcinoma after intensity-modulated radiotherapy[J/OL]. Chin J Cancer, 2016, 35(1): 103 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/28031050/. DOI: 10.1186/s40880-016-0167-2.
[10]
WANG R J, KE R Q, YU Y F, et al. Addition of nimotuzumab to concurrent chemoradiotherapy after induction chemotherapy improves outcomes of patients with locally advanced nasopharyngeal carcinoma[J/OL]. Front Pharmacol, 2024, 15: 1366853 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/38576484/. DOI: 10.3389/fphar.2024.1366853.
[11]
WANG L L, ZHUANG H Z, XU X Y, et al. Efficacy and survival analysis of nimotuzumab combined with concurrent chemoradiotherapy in the treatment of locally advanced nasopharyngeal carcinoma[J/OL]. Front Oncol, 2023, 13: 1129649 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/36814816/. DOI: 10.3389/fonc.2023.1129649.
[12]
LU Y, CHEN D G, LIANG J H, et al. Administration of nimotuzumab combined with cisplatin plus 5-fluorouracil as induction therapy improves treatment response and tolerance in patients with locally advanced nasopharyngeal carcinoma receiving concurrent radiochemotherapy: a multicenter randomized controlled study[J/OL]. BMC Cancer, 2019, 19(1): 1262 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/31888551/. DOI: 10.1186/s12885-019-6459-6.
[13]
HONG X H, WANG G Y, XU G L, et al. Prognostic value of EGFR and p-EGFR in nasopharyngeal carcinoma: a systematic review and meta-analysis[J/OL]. Medicine, 2022, 101(3): e28507 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/35060503/. DOI: 10.1097/md.0000000000028507.
[14]
ZHANG Q, ZHANG N, XIAO H, et al. Small antibodies with big applications: nanobody-based cancer diagnostics and therapeutics[J/OL]. Cancers, 2023, 15(23): 5639 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/38067344/. DOI: 10.3390/cancers15235639.
[15]
CHENG Z L, PENG X H, WU S T, et al. Adiponectin Receptor 1-vascular endothelial growth factor axis mediates resistance to epidermal growth factor receptor-targeted therapy in nasopharyngeal carcinoma[J]. Anticancer Drugs, 2026, 37(2): 146-156. DOI: 10.1097/CAD.0000000000001766.
[16]
KANG Y B, HE W H, REN C P, et al. Advances in targeted therapy mainly based on signal pathways for nasopharyngeal carcinoma[J/OL]. Sig Transduct Target Ther, 2020, 5(1): 245 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/33093441/. DOI: 10.1038/s41392-020-00340-2.
[17]
JIN X, LI W Z, GUO Y H, et al. Predicting progression-free survival using dynamic contrast-enhanced imaging-based radiomics in advanced nasopharyngeal carcinoma patients treated with nimotuzumab[J]. Eur Radiol, 2025, 35(8): 5135-5145. DOI: 10.1007/s00330-025-11433-3.
[18]
LEE H, AHN Y C, OH D, et al. Tumor volume reduction rate during adaptive radiation therapy as a prognosticator for nasopharyngeal cancer[J]. Cancer Res Treat, 2016, 48(2): 537-545. DOI: 10.4143/crt.2015.081.
[19]
TANG Q L, MEI C R, HUANG B, et al. Risk stratification of LA-NPC during chemoradiotherapy based on clinical classification and TVRR[J/OL]. Cancer Med, 2024, 13(3): e7029 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/38396378/. DOI: 10.1002/cam4.7029.
[20]
WANG Z P, FANG M J, ZHANG J, et al. Radiomics and deep learning in nasopharyngeal carcinoma: a review[J]. IEEE Rev Biomed Eng, 2024, 17: 118-135. DOI: 10.1109/RBME.2023.3269776.
[21]
LIU Z Y, WANG S, DONG D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303-1322. DOI: 10.7150/thno.30309.
[22]
GOROLAY V V, NILES N N, HUO Y R, et al. MRI detection of suspected nasopharyngeal carcinoma: a systematic review and meta-analysis[J]. Neuroradiology, 2022, 64(8): 1471-1481. DOI: 10.1007/s00234-022-02941-w.
[23]
WONG L M, AI Q Y H, ZHANG R L, et al. Radiomics for discrimination between early-stage nasopharyngeal carcinoma and benign hyperplasia with stable feature selection on MRI[J/OL]. Cancers, 2022, 14(14): 3433 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/35884494/. DOI: 10.3390/cancers14143433.
[24]
LIN D F, LI H L, LIU T, et al. Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma[J]. JNCI J Natl Cancer Inst, 2024, 116(8): 1294-1302. DOI: 10.1093/jnci/djae081.
[25]
JIAN Y J, PENG J X, YANG G, et al. Predictive performance of MRI and CT radiomics in predicting the response to induction chemotherapy in nasopharyngeal carcinoma: a network meta-analysis[J/OL]. Front Oncol, 2025, 15: 1590420 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/41323390/. DOI: 10.3389/fonc.2025.1590420.
[26]
ZHANG B, TIAN J, DONG D, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma[J]. Clin Cancer Res, 2017, 23(15): 4259-4269. DOI: 10.1158/1078-0432.ccr-16-2910.
[27]
LEE A W, NG W T, PAN J J, et al. International guideline for the delineation of the clinical target volumes (CTV) for nasopharyngeal carcinoma[J]. Radiother Oncol, 2018, 126(1): 25-36. DOI: 10.1016/j.radonc.2017.10.032.
[28]
ZENG Y Y, XIANG Z Z, HE T, et al. The comparison of prognostic value of tumour volumetric regression ratio and RECIST 1.1 criteria after induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma[J/OL]. Oral Oncol, 2020, 111: 104924 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/32736209/. DOI: 10.1016/j.oraloncology.2020.104924.
[29]
XIANG Z Z, HE T, ZENG Y Y, et al. Epstein–Barr virus DNA change level combined with tumor volume reduction ratio after inductive chemotherapy as a better prognostic predictor in locally advanced nasopharyngeal carcinoma[J]. Cancer Med, 2023, 12(2): 1102-1113. DOI: 10.1002/cam4.4964.
[30]
KWONG T S A, LEUNG H S, MO F K F, et al. Volumetric measurement to evaluate treatment response to induction chemotherapy on MRI outperformed RECIST guideline in outcome prediction in advanced nasopharyngeal carcinoma[J/OL]. ESMO Open, 2024, 9(10): 103933 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/39368415/. DOI: 10.1016/j.esmoop.2024.103933.
[31]
MOONS K G M, ALTMAN D G, REITSMA J B, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration[J/OL]. Ann Intern Med, 2015, 162(1): W1-W73 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/25560730/. DOI: 10.7326/m14-0698.
[32]
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[33]
ZHAO L N, GONG J, XI Y B, et al. MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma[J]. Eur Radiol, 2020, 30(1): 537-546. DOI: 10.1007/s00330-019-06211-x.
[34]
ZHAO W Y, LI X F, LV L, et al. Systematic review and metanalysis of neutrophil to lymphocyte ratio and prognosis in patients with nasopharyngeal carcinoma[J]. Laryngoscope Investig Otolaryngol, 2023, 8(6): 1522-1531. DOI: 10.1002/lio2.1161.
[35]
YANG D, LI P, MENG Z, et al. Combined pretreatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio predicts survival and prognosis in patients with non-metastatic nasopharyngeal carcinoma: a retrospective study[J/OL]. Sci Rep, 2024, 14: 9898 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/38688967/. DOI: 10.1038/s41598-024-59131-2.
[36]
JIAN L, SHENG C, LIU H P, et al. Early prediction of progression-free survival of patients with locally advanced nasopharyngeal carcinoma using multi-parametric MRI radiomics[J/OL]. BMC Cancer, 2025, 25(1): 519 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/40119284/. DOI: 10.1186/s12885-025-13899-2.
[37]
WU M X, XU W L, FEI Y J, et al. MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma[J/OL]. Front Oncol, 2023, 13: 1192953 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/37256173/. DOI: 10.3389/fonc.2023.1192953.
[38]
LIU X, ZHANG Y, YANG K Y, et al. Induction-concurrent chemoradiotherapy with or without sintilimab in patients with locoregionally advanced nasopharyngeal carcinoma in China (CONTINUUM): a multicentre, open-label, parallel-group, randomised, controlled, phase 3 trial[J]. Lancet, 2024, 403(10445): 2720-2731. DOI: 10.1016/S0140-6736(24)00594-4.
[39]
LIU X H, SHANG Z Y, GAO J W, et al. PD-1/PD-L1 inhibitors for locally advanced nasopharyngeal carcinoma: a systematic review and meta-analysis based on randomized controlled trials[J/OL]. BMC Cancer, 2025, 26(1): 1 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/41286686/. DOI: 10.1186/s12885-025-15229-y.
[40]
XU H B, ZHANG Y J, HUANG Z F, et al. Analysis of the short-term efficacy and side effects of concurrent chemoradiotherapy combined with nimotuzumab in patients with locally advanced nasopharyngeal carcinoma[J]. Chin J Gen Pract, 2024, 22(2): 206-208, 339. DOI: 10.16766/j.cnki.issn.1674-4152.003366.
[41]
VICKERS A J, ELKIN E B. Decision curve analysis: a novel method for evaluating prediction models[J]. Med Decis Making, 2006, 26(6): 565-574. DOI: 10.1177/0272989x06295361.
[42]
GU B X, MENG M Y, XU M Z, et al. Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma[J]. Eur J Nucl Med Mol Imaging, 2023, 50(13): 3996-4009. DOI: 10.1007/s00259-023-06399-7.
[43]
XI Y Z, DING Y H, ZHANG Y J, et al. Early prediction of death risk in progressive nasopharyngeal carcinoma using radiomics nomogram based on clinical semantic multi-parameter magnetic resonance imaging[J]. Br J Hosp Med (Lond), 2025, 86(2): 1-18. DOI: 10.12968/hmed.2024.0658.
[44]
BIN X, ZHU C, TANG Y, et al. Nomogram based on clinical and radiomics data for predicting radiation-induced temporal lobe injury in patients with non-metastatic stage T4 nasopharyngeal carcinoma[J/OL]. Clin Oncol (R Coll Radiol), 2022, 34(12): e482-e492 [2026-02-18]. https://pubmed.ncbi.nlm.nih.gov/36008245/. DOI: 10.1016/j.clon.2022.07.007.

PREV Quantitative microstructural analysis of the ulnar nerve in cubital tunnel syndrome using high-resolution MRI and DTI
NEXT Value of cardiac magnetic resonance in patients with late-onset cardiac phenotype fabry disease carrying the c.640-801G &gt; A mutation
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn