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• Clinical Article •
Clinical application value of MR-based radiomics for differentiation of benign and malignant of parotid gland
QI Jinbo  GAO Ankang  BAI Jie  CHENG Jingliang  WEN Baohong  WANG Feifei  ZHANG Zanxia  MA Xiaoyue 

Cite this article as: Qi JB, Gao AK, Bai J, et al. Clinical application value of MR-based radiomics for differentiation of benign and malignant of parotid gland[J]. Chin J Magn Reson Imaging, 2022, 13(5): 34-39. DOI:10.12015/issn.1674-8034.2022.05.007.


[Abstract] Objective To explore the clinical application value of radiomics model with multi-sequence combination in differentiating benign from malignant tumor of parotid gland and, among the former, differentiating pleomorphic adenomas from Warthin tumors.Materials and Methods The clinical data and preoperative MRI images of 124 patients with parotid benign tumors, including 64 cases of pleomorphic adenomas and 60 cases of Warthin tumors, and 52 patients with malignant tumors by pathology were analyzed retrospectively. The region of interest was created manually from fat saturated T2 weighted imaging (FS-T2WI) using ITK-SNAP software,then FS-T2WI was registered to the apparent diffusion coefficient (ADC) map and contrast enhanced T1 weighted imaging (CE-T1WI), respectively. The FAE software was used to extract 1316 radiomics features from FS-T2WI, ADC and CE-T1WI, respectively. Features were selected by using recursive feature elimination (RFE) method, and support vector machine, as the classifier, was used to develop radiomics model. The receiver operating characteristic (ROC) curves were drawn to evaluate differential diagnosis performance of each model and Delong's test was used to compare the differences between models.Results The MRI features of parotid tumors were as following: compared with parotid benign tumors, malignant tumors were mostly located in deep lobe or double lobes (P＜0.001), with less clear boundary (P＜0.001), heterogeneous appearance (P=0.003), more cystic degeneration or necrosis occurred (P=0.002) and infiltration of surrounding structures or lymph node metastasis suggests a greater possibility of malignancy (P＜0.001).The radiomics models were analyzed as follows: 7 radiomics models were constructed based on FS-T2WI, ADC and CE-TIWI sequence to distinguish benign from malignant tumors of parotid gland. The ROC analyses on 7 models resulted in an area under the curve (AUC) of 0.798 for FS-T2WI model, 0.838 for ADC model, 0.856 for CE-T1WI model, 0.815 for FS-T2WI+ADC model, 0.858 for FS-T2WI+CE-T1WI model, 0.863 for ADC+CE-T1WI model, and 0.878 for multi-sequence joint model. Seven radiomics models were constructed with the same method for differentiation between pleomorphic adenomas and Warthin tumor, the AUC were 0.724, 0.910, 0.848, 0.887, 0.876, 0.915 and 0.954, respectively.Conclusions The diagnostic performance of multi-sequence joint model in differentiating benign from malignant tumor of parotid gland and distinguishing pleomorphic adenoma from Warthin tumor are both better than that of single sequence and double sequence models, and CE-T1WI and ADC obtain the highest diagnostic efficiency among single sequences, respectively.
[Keywords] parotid tumors；magnetic resonance imaging；radiomics；Warthin tumors；pleomorphic adenomas

QI Jinbo   GAO Ankang   BAI Jie   CHENG Jingliang*   WEN Baohong   WANG Feifei   ZHANG Zanxia   MA Xiaoyue  

Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Cheng JL, E-mail: fccchengjl@zzu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS Joint Construction Project of Henan Medical Science and Technology Research Project (No. LHGJ20190157); Youth Project of Henan Medical Science and Technology Research Project (No. SBGJ202103078).
Received  2022-01-21
Accepted  2022-04-29
DOI: 10.12015/issn.1674-8034.2022.05.007
Cite this article as: Qi JB, Gao AK, Bai J, et al. Clinical application value of MR-based radiomics for differentiation of benign and malignant of parotid gland[J]. Chin J Magn Reson Imaging, 2022, 13(5): 34-39. DOI:10.12015/issn.1674-8034.2022.05.007.

[1]
Gao M, Hao Y, Huang MX, et al. Salivary gland tumours in a northern Chinese population: a 50-year retrospective study of 7190 cases[J]. Int J Oral Maxillofac Surg, 2017, 46(3): 343-349. DOI: 10.1016/j.ijom.2016.09.021.
[2]
Lewis AG, Tong T, Maghami E. Diagnosis and management of malignant salivary gland tumors of the parotid gland[J]. Otolaryngol Clin North Am, 2016, 49(2): 343-380. DOI: 10.1016/j.otc.2015.11.001.
[3]
Park YM, Kang MS, Kim DH, et al. Surgical extent and role of adjuvant radiotherapy of surgically resectable, low-grade parotid cancer[J]. Oral Oncol, 2020, 107: 104780. DOI: 10.1016/j.oraloncology.2020.104780.
[4]
Attyé A, Karkas A, Troprès I, et al. Parotid gland tumours: MR tractography to assess contact with the facial nerve[J]. Eur Radiol, 2016, 26(7): 2233-2241. DOI: 10.1007/s00330-015-4049-9.
[5]
Freling NJ, Molenaar WM, Vermey A, et al. Malignant parotid tumors: clinical use of MR imaging and histologic correlation[J]. Radiology, 1992, 185(3): 691-696. DOI: 10.1148/radiology.185.3.1438746.
[6]
Sarioglu O, Sarioglu FC, Akdogan AI, et al. MRI-based texture analysis to differentiate the most common parotid tumours[J]. Clin Radiol, 2020, 75(11): 877.e15-877.877.e23. DOI: 10.1016/j.crad.2020.06.018.
[7]
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[8]
Peng ZY, Wang YM, Wang YX, et al. Application of radiomics and machine learning in head and neck cancers[J]. Int J Biol Sci, 2021, 17(2): 475-486. DOI: 10.7150/ijbs.55716.
[9]
Gabelloni M, Faggioni L, Attanasio S, et al. Can magnetic resonance radiomics analysis discriminate parotid gland tumors? A pilot study[J]. Diagnostics (Basel), 2020, 10(11): E900. DOI: 10.3390/diagnostics10110900.
[10]
Shao S, Zheng N, Mao N, et al. A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging[J]. Clin Radiol, 2021, 76(6): 472.e11-472.e18. DOI: 10.1016/j.crad.2020.10.019.
[11]
Song Y, Zhang J, Zhang YD, et al. FeAture Explorer (FAE): a tool for developing and comparing radiomics models[J]. PLoS One, 2020, 15(8): e0237587. DOI: 10.1371/journal.pone.0237587.
[12]
Zheng YM, Xu WJ, Hao DP, et al. A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland[J]. Eur Radiol, 2021, 31(5): 2886-2895. DOI: 10.1007/s00330-020-07421-4.
[13]
Li QY, Jiang T, Zhang C, et al. A nomogram based on clinical information, conventional ultrasound and radiomics improves prediction of malignant parotid gland lesions[J]. Cancer Lett, 2022, 527: 107-114. DOI: 10.1016/j.canlet.2021.12.015.
[14]
Piludu F, Marzi S, Ravanelli M, et al. MRI-based radiomics to differentiate between benign and malignant parotid tumors with external validation[J]. Front Oncol, 2021, 11: 656918. DOI: 10.3389/fonc.2021.656918.
[15]
Zheng YM, Li J, Liu S, et al. MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland[J]. Eur Radiol, 2021, 31(6): 4042-4052. DOI: 10.1007/s00330-020-07483-4.
[16]
Zheng YM, Chen J, Xu Q, et al. Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland[J]. Dentomaxillofac Radiol, 2021, 50(7): 20210023. DOI: 10.1259/dmfr.20210023.
[17]
Song LL, Chen SJ, Chen W, et al. Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images[J]. BMC Med Imaging, 2021, 21(1): 54. DOI: 10.1186/s12880-021-00581-9.
[18]
Xia W, Hu B, Li HQ, et al. Deep learning for automatic differential diagnosis of primary central nervous system lymphoma and glioblastoma: multi-parametric magnetic resonance imaging based convolutional neural network model[J]. J Magn Reson Imaging, 2021, 54(3): 880-887. DOI: 10.1002/jmri.27592.
[19]
Wei JW, Yang GQ, Hao XH, et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication[J]. Eur Radiol, 2019, 29(2): 877-888. DOI: 10.1007/s00330-018-5575-z.
[20]
Luo ZD, Li J, Liao YT, et al. Radiomics analysis of multiparametric MRI for prediction of synchronous lung metastases in osteosarcoma[J]. Front Oncol, 2022, 12: 802234. DOI: 10.3389/fonc.2022.802234.
[21]
Tan Y, Zhang ST, Wei JW, et al. A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery[J]. Eur Radiol, 2019, 29(7): 3325-3337. DOI: 10.1007/s00330-019-06056-4.
[22]
Dong JY, Yu MM, Miao YW, et al. Differential diagnosis of solitary fibrous tumor/hemangiopericytoma and angiomatous meningioma using three-dimensional magnetic resonance imaging texture feature model[J]. Biomed Res Int, 2020, 2020: 5042356. DOI: 10.1155/2020/5042356.
[23]
Liu YJ, Lee YH, Chang HC, et al. Imaging quality of PROPELLER diffusion-weighted MR imaging and its diagnostic performance in distinguishing pleomorphic adenomas from Warthin tumors of the parotid gland[J]. NMR Biomed, 2020, 33(5): e4282. DOI: 10.1002/nbm.4282.
[24]
Hu PA, Chen L, Zhou ZR. Machine learning in the differentiation of soft tissue neoplasms: comparison of fat-suppressed T2WI and apparent diffusion coefficient (ADC) features-based models[J]. J Digit Imaging, 2021, 34(5): 1146-1155. DOI: 10.1007/s10278-021-00513-7.
[25]
Mao JJ, Fang J, Duan XH, et al. Predictive value of pretreatment MRI texture analysis in patients with primary nasopharyngeal carcinoma[J]. Eur Radiol, 2019, 29(8): 4105-4113. DOI: 10.1007/s00330-018-5961-6.
[26]
Fruehwald-Pallamar J, Czerny C, Holzer-Fruehwald L, et al. Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla[J]. NMR Biomed, 2013, 26(11): 1372-1379. DOI: 10.1002/nbm.2962.
[27]
Wada T, Yokota H, Horikoshi T, et al. Diagnostic performance and inter-operator variability of apparent diffusion coefficient analysis for differentiating pleomorphic adenoma and carcinoma ex pleomorphic adenoma: comparing one-point measurement and whole-tumor measurement including radiomics approach[J]. Jpn J Radiol, 2020, 38(3): 207-214. DOI: 10.1007/s11604-019-00908-1.
[28]
Hernandez-Prera JC, Skálová A, Franchi A, et al. Pleomorphic adenoma: the great mimicker of malignancy[J]. Histopathology, 2021, 79(3): 279-290. DOI: 10.1111/his.14322.
[29]
Bhatlawande H, Desai KM, Kale AD, et al. Co-occurrence of Warthin's tumor with oral squamous cell carcinoma - Overlapping risk factors and implications[J]. Oral Oncol, 2020, 100: 104449. DOI: 10.1016/j.oraloncology.2019.104449.
[30]
Zhang Q, Peng YS, Liu W, et al. Radiomics based on multimodal MRI for the differential diagnosis of benign and malignant breast lesions[J]. J Magn Reson Imaging, 2020, 52(2): 596-607. DOI: 10.1002/jmri.27098.

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