Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

被引:7
作者
Faggioni, Lorenzo [1 ]
Gabelloni, Michela [1 ]
De Vietro, Fabrizio [1 ]
Frey, Jessica [1 ]
Mendola, Vincenzo [1 ]
Cavallero, Diletta [1 ]
Borgheresi, Rita [1 ]
Tumminello, Lorenzo [1 ]
Shortrede, Jorge [1 ]
Morganti, Riccardo [2 ]
Seccia, Veronica [3 ]
Coppola, Francesca [4 ,5 ]
Cioni, Dania [1 ,5 ]
Neri, Emanuele [1 ,5 ]
机构
[1] Univ Pisa, Acad Radiol, Dept Translat Res, Via Roma 67, I-56126 Pisa, Italy
[2] Univ Pisa, Dept Clin & Expt Med, Sect Stat, Via Roma 67, I-56126 Pisa, Italy
[3] Univ Pisa, Azienda Osped Univ Pisana, Dept Surg Med Mol Pathol & Crit Care Med, Otolaryngol Audiol & Phoniatr Operat Unit, I-56124 Pisa, Italy
[4] IRCCS Azienda Osped Univ Bologna, Dept Radiol, I-40138 Bologna, Italy
[5] SIRM Fdn, Italian Soc Med & Intervent Radiol, Via Signora 2, I-20122 Milan, Italy
关键词
Warthin tumor; Pleomorphic adenoma; Head and neck cancer; Parotid neoplasm; Radiomics; Magnetic resonance imaging;
D O I
10.1016/j.ejro.2022.100429
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose:Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRIbased radiomic features in discriminating PA from WT in the preoperative setting. Methods:We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org). Results:The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA. Conclusion:Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.
引用
收藏
页数:6
相关论文
共 18 条
  • [1] Evaluation of jugular foramen nerves by using b-FFE, T2-weighted DRIVE, T2-weighted FSE and post-contrast T1-weighted MRI sequences
    Aydin, Hasan
    Altin, Elif
    Dilli, Alper
    Sipahioglu, Serdar
    Hekimoglu, Baki
    DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY, 2011, 17 (01) : 3 - 9
  • [2] Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid
    Hu, Zhenbin
    Guo, Junjie
    Feng, Jiajun
    Huang, Yuqian
    Xu, Honggang
    Zhou, Quan
    EUROPEAN RADIOLOGY, 2023, 33 (06) : 4453 - 4463
  • [3] Contrast Enrichment of Spinal Cord MR Imaging Using a Ratio of T1-Weighted and T2-Weighted Signals
    Teraguchi, Masatoshi
    Yamada, Hiroshi
    Yoshida, Munehito
    Nakayama, Yoshiaki
    Kondo, Tomoyoshi
    Ito, Hidefumi
    Terada, Masaki
    Kaneoke, Yoshiki
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2014, 40 (05) : 1199 - 1207
  • [4] The value of T1-and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor
    Sun, Hongbiao
    Sun, Zuoheng
    Wang, Wenwen
    Cha, Xudong
    Jiang, Qinling
    Wang, Xiang
    Li, Qingchu
    Liu, Shiyuan
    Liu, Huanhai
    Chen, Qi
    Yuan, Weimin
    Xiao, Yi
    TRANSLATIONAL ONCOLOGY, 2024, 49
  • [5] Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis
    de Causans, Alix
    Carre, Alexandre
    Roux, Alexandre
    Tauziede-Espariat, Arnault
    Ammari, Samy
    Dezamis, Edouard
    Dhermain, Frederic
    Reuze, Sylvain
    Deutsch, Eric
    Oppenheim, Catherine
    Varlet, Pascale
    Pallud, Johan
    Edjlali, Myriam
    Robert, Charlotte
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [6] Are T2-weighted images more useful than T1-weighted contrast-enhanced images in assessment of postoperative sella and parasellar region?
    Bladowska, Joanna
    Biel, Anna
    Zimny, Anna
    Lubkowska, Katarzyna
    Bednarek-Tupikowska, Grazyna
    Sozanski, Tomasz
    Zaleska-Dorobisz, Urszula
    Sasiadek, Marek
    MEDICAL SCIENCE MONITOR, 2011, 17 (10): : MT83 - MT90
  • [7] Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features
    Liping Yang
    Tianzuo Wang
    Jinling Zhang
    Shi Kang
    Shichuan Xu
    Kezheng Wang
    BMC Medical Imaging, 24
  • [8] Integrating radiomic features from T2-weighted and contrast-enhanced MRI to evaluate pathologic rectal tumor regression after chemoradiation
    Nanda, Siddhartha
    Antunes, Jacob T.
    Selvam, Amrish
    Bera, Kaustav
    Brady, Justin T.
    Gollamudi, Jayakrishna
    Friedman, Kenneth
    Willis, Joseph E.
    Delaney, Conor P.
    Paspulati, Raj M.
    Madabhushi, Anant
    Viswanath, Satish E.
    MEDICAL IMAGING 2019: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING, 2019, 10951
  • [9] Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features
    Yang, Liping
    Wang, Tianzuo
    Zhang, Jinling
    Kang, Shi
    Xu, Shichuan
    Wang, Kezheng
    BMC MEDICAL IMAGING, 2024, 24 (01)
  • [10] A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI
    Yin, Ping
    Mao, Ning
    Zhao, Chao
    Wu, Jiangfen
    Chen, Lei
    Hong, Nan
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2019, 49 (03) : 752 - 759