Literature DB >> 30769222

Application of diffusion-weighted MR imaging with ADC measurement for distinguishing between the histopathological types of sinonasal neoplasms.

Mehmet Gencturk1, Kerem Ozturk2, Emiro Caicedo-Granados3, Faqian Li4, Zuzan Cayci5.   

Abstract

PURPOSE: To evaluate the potential contribution of quantitative DWI parameters including ADCmean and ADCratio values to help in distinguishing the histopathological types of sinonasal neoplasms.
METHODS: This retrospective study included 83 patients (50 males, 33 females; mean age 61 years) with pathologically proven untreated sinonasal neoplasms who have undergone diffusion-weighted MRI imaging from February 2010 to August 2017. Diffusion-weighted MRI was performed on a 3 T unit with b factors of 0 and 1000 s/mm2, and ADC maps were generated. Mean ADC values of sinonasal tumors and ADC ratios (ADCmean of the tumor to ADCmean of pterygoid muscles) were compared with the histopathological diagnosis by utilizing the Kruskal-Wallis non-parametric test.
RESULTS: Mean ADCmean and ADCratio were 0.8 (SD, ±0.4) × (10-3 mm2/s) and 1.2 (SD, ±0.5), respectively, and each parameter was significantly different between histopathological types (p < 0.05). Mean ADCmean and ADCratio were higher in adenoid cystic carcinoma (ACC) than in SCC, lymphoma, neuroendocrine carcinoma and sinonasal undifferentiated carcinoma (SNUC) (p < 0.05). Optimized ADCmean thresholds of 0.79, 0.81, 0.74 and 0.78 (10-3 mm2/s) achieved maximal discriminatory accuracies of 100%, 79%, 100% and 89% for ACC/SNUC, ACC/SCC, ACC/neuroendocrine carcinoma, and ACC/lymphoma, respectively.
CONCLUSIONS: The optimized ADCmean threshold of 0.80 (10-3 mm2/s) could be used to differentiate ACC from non-ACC sinonasal neoplasms with maximal discriminatory accuracy (82%) and sensitivity of 100%. However, there is considerable overlapping of the ADCmean and ADCratio values among non-ACC sinonasal neoplasms hence surgical biopsy is still needed.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Apparent diffusion coefficient (ADC); Diffusion-weighted imaging (DWI); Magnetic resonance imaging (MRI); Sinonasal neoplasm; Squamous cell carcinoma

Mesh:

Year:  2019        PMID: 30769222     DOI: 10.1016/j.clinimag.2019.02.004

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  9 in total

1.  Positron emission computed tomography and magnetic resonance imaging features of sinonasal small round blue cell tumors.

Authors:  Kerem Ozturk; Mehmet Gencturk; Emiro Caicedo-Granados; Faqian Li; Zuzan Cayci
Journal:  Neuroradiol J       Date:  2019-08-28

2.  Quantitative Analysis of DCE-MRI and RESOLVE-DWI for Differentiating Nasopharyngeal Carcinoma from Nasopharyngeal Lymphoid Hyperplasia.

Authors:  J Y Yu; D Zhang; X L Huang; J Ma; C Yang; X J Li; H Xiong; B Zhou; R K Liao; Z Y Tang
Journal:  J Med Syst       Date:  2020-02-26       Impact factor: 4.460

3.  CT and MRI Findings of Glomangiopericytoma in the Head and Neck: Case Series Study and Systematic Review.

Authors:  C H Suh; J H Lee; M K Lee; S J Cho; S R Chung; Y J Choi; J H Baek
Journal:  AJNR Am J Neuroradiol       Date:  2019-12-05       Impact factor: 3.825

4.  Texture analysis of conventional magnetic resonance imaging and diffusion-weighted imaging for distinguishing sinonasal non-Hodgkin's lymphoma from squamous cell carcinoma.

Authors:  Guo-Yi Su; Jun Liu; Xiao-Quan Xu; Mei-Ping Lu; Min Yin; Fei-Yun Wu
Journal:  Eur Arch Otorhinolaryngol       Date:  2022-06-22       Impact factor: 2.503

5.  A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study.

Authors:  Naier Lin; Sihui Yu; Mengyan Lin; Yiqian Shi; Wei Chen; Zhipeng Xia; Yushu Cheng; Yan Sha
Journal:  Front Oncol       Date:  2022-05-16       Impact factor: 5.738

6.  MRI features of different types of sinonasan rhabdomyosarcomas: a series of eleven cases.

Authors:  Junjie Zeng; Lan Liu; Jiayong Li; Qiling Huang; Leiming Pi; Ke Jin
Journal:  Dentomaxillofac Radiol       Date:  2021-04-09       Impact factor: 2.419

7.  Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values.

Authors:  Chen Chen; Yuhui Qin; Haotian Chen; Junying Cheng; Bo He; Yixuan Wan; Dongyong Zhu; Fabao Gao; Xiaoyue Zhou
Journal:  Eur Radiol       Date:  2022-01-14       Impact factor: 7.034

8.  Small Cell Neuroendocrine Carcinoma of Paranasal Sinuses: Radiologic Features in 14 Cases.

Authors:  Naier Lin; Meng Qi; Zhengyue Wang; Siqi Luo; Yucheng Pan; Fang Zhang; Yan Sha
Journal:  J Comput Assist Tomogr       Date:  2021 Jan-Feb 01       Impact factor: 1.826

9.  Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors.

Authors:  Chen Chen; Yuhui Qin; Junying Cheng; Fabao Gao; Xiaoyue Zhou
Journal:  Front Oncol       Date:  2021-12-13       Impact factor: 6.244

  9 in total

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