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Clinical Article
Radiomics analysis based on IVIM-DWI quantitative parameters to predict the short-term therapeutic effect of nasopharyngeal carcinoma
DAI Ganmian  WU Wenyuan  FU Lili  LI Tiansheng  YANG Qianyu  HUANG Weiyuan  GUO Yihao  CHEN Feng 

DAI G M, WU W Y, FU L L, et al. Radiomics analysis based on IVIM-DWI quantitative parameters to predict the short-term therapeutic effect of nasopharyngeal carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(9): 56-62, 69. DOI:10.12015/issn.1674-8034.2023.09.010.

[Abstract] Objective To establish a predictive model based on quantitative characteristics of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) radiomics to predict short-term treatment efficacy of nasopharyngeal carcinoma before treatment.Materials and Methods A retrospective study was conducted to collect 80 patients with nasopharyngeal carcinoma diagnosed pathologically at the first visit who were treated in the Radiotherapy Department of Hainan Provincial People's Hospital from January 2019 to August 2021. Before treatment, all subjects underwent MRI plain scan + enhanced examination and 11 b-value (interval 0-800 s/mm2) IVIM-DWI examination. After receiving comprehensive treatment based on radiotherapy, routine MRI follow-up of head and neck was conducted every 3 months. Use MRI follow-up images taken 6 months after the end of treatment for efficacy evaluation. According to respond evaluation criteriain solid tumors, version 1.1 standard, the patients were divided into complete remission group (n=62) and incomplete remission group (n=18). The real diffusion coefficient (D), perfusion related diffusion coefficient (D*) and perfusion fraction (f) were obtained by post-processing IVIM-DWI with a double exponential model. Itk-snap was used to delineate the region of interest (ROI) of the lesion layer by layer on the S0 image of IVIM-DWI, and the conventional and enhanced MRI images of nasopharyngeal carcinoma were used as the positioning reference. 3D slicer software was used to extract radiomics features, including histogram features, texture features and morphology features, from the corresponding ROI regions of D, D* and f quantitative parameter images. The least absolute shrinkage and selection operator algorithm was used to screen out the radiomics features that were highly correlated with the treatment effect. Logistic regression was used to construct radiomics prediction models based on D, D*, f, and joint parameters, and predictive performance was evaluated using ROC curves, area under the curve (AUC), and calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction models. A 10-fold cross-validation was used for internal model validation, and the average sensitivity and specificity were calculated.Results A total of 851 radiomics features were extracted, and after feature selection, two D-value features were selected to construct a radiomics model with a sensitivity of 60.0%, specificity of 79.6%, and an AUC value of 0.734. Two f-value features were selected to construct a radiomics model, with a sensitivity of 66.1%, specificity of 76.3%, and an AUC value of 0.747. One D*-value feature was selected to construct a radiomics model, with a sensitivity of 76.1%, specificity of 75.9%, and an AUC value of 0.726. The sensitivity, specificity and AUC of the radiomics model based on the three types of IVIM-DWI radiomics features were 81.7%, 80.6% and 0.827 respectively. The calibration curves showed good goodness-of-fit for all models, and the DCA demonstrated good clinical utility for all four models, with the IVIM joint model showing the highest clinical benefit.Conclusions The radiomics model based on IVIM-DWI parameters can predict the therapeutic response of nasopharyngeal carcinoma patients before treatment. Among them, the most effective model is the IVIM-DWI combined parameter model, which can assist in clinical decision-making for patients.
[Keywords] nasopharyngeal carcinoma;therapeutic effect;intravoxel incoherent motion imaging;radiomics;magnetic resonance imaging

DAI Ganmian   WU Wenyuan   FU Lili   LI Tiansheng   YANG Qianyu   HUANG Weiyuan   GUO Yihao   CHEN Feng*  

Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570100, China

Corresponding author: Chen F, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81971602; 82260339).
Received  2023-02-02
Accepted  2023-09-06
DOI: 10.12015/issn.1674-8034.2023.09.010
DAI G M, WU W Y, FU L L, et al. Radiomics analysis based on IVIM-DWI quantitative parameters to predict the short-term therapeutic effect of nasopharyngeal carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(9): 56-62, 69. DOI:10.12015/issn.1674-8034.2023.09.010.

ZHANG Z M, BAO Y, ZHOU L X, et al. Preliminary application of endonasopharyngeal ultrasound-guided transnasopharyngeal needle aspiration in the diagnosis of submucosal nasopharyngeal carcinoma[J]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi, 2019, 54(1): 46-49. DOI: 10.3760/cma.j.issn.1673-0860.2019.01.010.
MASTERSON L, AGALATO E, PEARSON C. Image-guided sinus surgery: practical and financial experiences from a UK centre 2001-2009[J]. J Laryngol Otol, 2012, 126(12): 1224-1230. DOI: 10.1017/S002221511200223X.
LU Y Q, HU T, LIN C Y, et al. Evaluation of nasopharyngeal carcinoma screening scheme in Sihui city, Guangdong Province[J]. China Cancer, 2018, 27(11): 842-846. DOI: 10.11735/j.issn.1004-0242.2018.11.A005.
LEE A W, NG W T, CHAN L L, et al. Evolution of treatment for nasopharyngeal cancer: success and setback in the intensity-modulated radiotherapy era[J]. Radiother Oncol, 2014, 110(3): 377-384. DOI: 10.1016/j.radonc.2014.02.003.
LIN G W, WANG L X, JI M, et al. The use of MR imaging to detect residual versus recurrent nasopharyngeal carcinoma following treatment with radiation therapy[J]. Eur J Radiol, 2013, 82(12): 2240-2246. DOI: 10.1016/j.ejrad.2013.09.014.
HUANG Y Y, CAO X, CAI Z C, et al. Short-term efficacy and long-term survival of nasopharyngeal carcinoma patients with radiographically visible residual disease following observation or additional intervention: a real-world study in China[J]. Laryngoscope Investig Otolaryngol, 2022, 7(6): 1881-1892. DOI: 10.1002/lio2.980.
VAUPEL P, MAYER A. Hypoxia in cancer: significance and impact on clinical outcome[J]. Cancer Metastasis Rev, 2007, 26(2): 225-239. DOI: 10.1007/s10555-007-9055-1.
DZOBO K, SENTHEBANE D A, DANDARA C. The tumor microenvironment in tumorigenesis and therapy resistance revisited[J/OL]. Cancers, 2023, 15(2): 376[2023-02-01]. DOI: 10.3390/cancers15020376.
ZHU Y F, LI X Y, WANG L, et al. Metabolic reprogramming and crosstalk of cancer-related fibroblasts and immune cells in the tumor microenvironment[J/OL]. Front Endocrinol, 2022, 13: 988295 [2023-02-01]. DOI: 10.3389/fendo.2022.988295.
XIAO Y P, PAN J J, CHEN Y B, et al. Intravoxel incoherent motion-magnetic resonance imaging as an early predictor of treatment response to neoadjuvant chemotherapy in locoregionally advanced nasopharyngeal carcinoma[J/OL]. Medicine, 2015, 94(24): e973 [2023-02-01]. DOI: 10.1097/MD.0000000000000973.
QIN Y H, YU X P, HOU J, et al. Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging[J/OL]. Medicine, 2018, 97(30): e11676 [2023-02-01]. DOI: 10.1097/MD.0000000000011676.
MUI A W L, LEE A W M, LEE V H F, et al. Prognostic and therapeutic evaluation of nasopharyngeal carcinoma by dynamic contrast-enhanced (DCE), diffusion-weighted (DW) magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS)[J/OL]. Magn Reson Imaging, 2021, 83: 50-56 [2023-02-19]. DOI: 10.1016/j.mri.2021.07.003.
BIHAN D L, TURNER R. The capillary network: a link between IVIM and classical perfusion[J]. Magn Reson Med, 1992, 27(1): 171-178. DOI: 10.1002/mrm.1910270116.
QAMAR S, KING A D, AI Q H, et al. Pre-treatment intravoxel incoherent motion diffusion-weighted imaging predicts treatment outcome in nasopharyngeal carcinoma[J/OL]. Eur J Radiol, 2020, 129: 109127 [2023-02-01]. DOI: 10.1016/j.ejrad.2020.109127.
NOIJ D P, MARTENS R M, MARCUS J T, et al. Intravoxel incoherent motion magnetic resonance imaging in head and neck cancer: a systematic review of the diagnostic and prognostic value[J/OL]. Oral Oncol, 2017, 68: 81-91 [2023-02-01]. DOI: 10.1016/j.oraloncology.2017.03.016.
CHEN H, HE Y Y, ZHAO C C, et al. Reproducibility of radiomics features derived from intravoxel incoherent motion diffusion-weighted MRI of cervical cancer[J]. Acta Radiol, 2021, 62(5): 679-686. DOI: 10.1177/0284185120934471.
GANESHAN B, GOH V, MANDEVILLE H C, et al. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT[J]. Radiology, 2013, 266(1): 326-336. DOI: 10.1148/radiol.12112428.
SAGAERT X, VANSTAPEL A, VERBEEK S. Tumor heterogeneity in colorectal cancer: what do we know so far?[J]. Pathobiology, 2018, 85(1/2): 72-84. DOI: 10.1159/000486721.
SPADARELLA G, CALARESO G, GARANZINI E, et al. MRI based radiomics in nasopharyngeal cancer: systematic review and perspectives using radiomic quality score (RQS) assessment[J/OL]. Eur J Radiol, 2021, 140: 109744 [2023-02-01]. DOI: 10.1016/j.ejrad.2021.109744.
KIM M J, CHOI Y, SUNG Y E, et al. Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics[J/OL]. Transl Oncol, 2021, 14(10): 101180 [2023-02-19]. DOI: 10.1016/j.tranon.2021.101180.
XU H, LIU J K, HUANG Y, et al. MRI-based radiomics as response predictor to radiochemotherapy for metastatic cervical lymph node in nasopharyngeal carcinoma[J/OL]. Br J Radiol, 2021, 94(1122): 20201212 [2023-02-01]. DOI: 10.1259/bjr.20201212.
BORDRON A, RIO E, BADIC B, et al. External validation of a radiomics model for the prediction of complete response to neoadjuvant chemoradiotherapy in rectal cancer[J/OL]. Cancers, 2022,14(4): 1079 [2023-02-01]. DOI: 10.3390/cancers14041079.
WANG G Y, HE L, YUAN C, et al. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma[J/OL]. Eur J Radiol, 2018, 98: 100-106 [2023-02-01]. DOI: 10.1016/j.ejrad.2017.11.007.
YU X P, HOU J, LI F P, et al. Intravoxel incoherent motion MRI for predicting early response to induction chemotherapy and chemoradiotherapy in patients with nasopharyngeal carcinoma[J]. J Magn Reson Imaging, 2016, 43(5): 1179-1190. DOI: 10.1002/jmri.25075.
ZONARI P, BARALDI P, CRISI G. Multimodal MRI in the characterization of glial neoplasms: the combined role of single-voxel MR spectroscopy, diffusion imaging and echo-planar perfusion imaging[J]. Neuroradiology, 2007, 49(10): 795-803. DOI: 10.1007/s00234-007-0253-x.
TRAN D, NGUYEN D H, NGUYEN H K, et al. Diagnostic performance of MRI perfusion and spectroscopy for brainstem glioma grading[J]. Eur Rev Med Pharmacol Sci, 2022, 26(21): 7938-7948. DOI: 10.26355/eurrev_202211_30145.
ZHANG L, YANG L Q, WEN L, et al. Noninvasively evaluating the grading of glioma by multiparametric magnetic resonance imaging[J/OL]. Acad Radiol, 2021, 28(5): e137-e146 [2023-02-01]. DOI: 10.1016/j.acra.2020.03.035.
ZHU Q Q, ZHU W R, WU J T, et al. Comparative study of conventional diffusion-weighted imaging and introvoxel incoherent motion in assessment of pathological grade of clear cell renal cell carcinoma[J/OL]. Br J Radiol, 2022, 95(1133): 20210485 [2023-02-01]. DOI: 10.1259/bjr.20210485.
BISDAS S, KIRKPATRICK M, GIGLIO P, et al. Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease?[J]. AJNR Am J Neuroradiol, 2009, 30(4): 681-688. DOI: 10.3174/ajnr.A1465.
ZHU L N, WU J, ZHANG H, et al. The value of intravoxel incoherent motion imaging in predicting the survival of patients with astrocytoma[J]. Acta Radiol, 2021, 62(3): 423-429. DOI: 10.1177/0284185120926907.
QIN Y H, CHEN C, CHEN H T, et al. The value of intravoxel incoherent motion model-based diffusion-weighted imaging for predicting long-term outcomes in nasopharyngeal carcinoma[J/OL]. Front Oncol, 2022, 12: 902819 [2023-02-01]. DOI: 10.3389/fonc.2022.902819.
CHEN W B, ZHANG B, LIANG L, et al. To predict the radiosensitivity of nasopharyngeal carcinoma using intravoxel incoherent motion MRI at 3.0 T[J]. Oncotarget, 2017, 8(32): 53740-53750. DOI: 10.18632/oncotarget.17367.
LU L Y, LI Y H, LI W B. The role of intravoxel incoherent motion MRI in predicting early treatment response to chemoradiation for metastatic lymph nodes in nasopharyngeal carcinoma[J]. Adv Ther, 2016, 33(7): 1158-1168. DOI: 10.1007/s12325-016-0352-3.
ZHANG Y, ZHANG K Y, JIA H D, et al. IVIM-DWI and MRI-based radiomics in cervical cancer: prediction of concurrent chemoradiotherapy sensitivity in combination with clinical prognostic factors[J/OL]. Magn Reson Imaging, 2022, 91: 37-44 [2023-02-01]. DOI: 10.1016/j.mri.2022.05.005.

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