Literature DB >> 34427744

Dual-energy CT in differentiating benign sinonasal lesions from malignant ones: comparison with simulated single-energy CT, conventional MRI, and DWI.

Peng Wang1,2, Zuohua Tang3, Zebin Xiao1,4, Rujian Hong1, Rong Wang5, Yuzhe Wang1, Yang Zhan5.   

Abstract

OBJECTIVES: To explore the value of dual-energy CT (DECT) for differentiating benign sinonasal lesions from malignant ones, and to compare this finding with simulated single-energy CT (SECT), conventional MRI (cMRI), and diffusion-weighted imaging (DWI).
METHODS: Patients with sinonasal lesions (38 benign and 34 malignant) who were confirmed by histopathology underwent DECT, cMRI, and DWI. DECT-derived parameters (iodine concentration (IC), effective atomic number (Eff-Z), 40-180 keV (20-keV interval), virtual non-enhancement (VNC), slope (k), and linear-mixed 0.3 (Mix-0.3)), DECT morphological features, cMRI characteristics, and ADC value of benign and malignant tumors were compared using t test or chi-square test. Receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic performance, and the area under the ROC curve (AUC) was compared using the Z test to select the optimal diagnostic approach.
RESULTS: Significantly higher DECT-derived single parameters (IC, Eff-Z, 40 keV, 60 keV, 80 keV, slope (k), Mix-0.3) were found in malignant lesions than those of benign sinonasal lesions (all p < 0.004, Bonferroni correction). Combined quantitative parameters (IC, Eff-Z, 40 keV, 60 keV, 80 keV, slope (k)) can improve the diagnostic efficiency for discriminating these two entities. Combination of DECT quantitative parameters and morphological features can further improve the overall diagnostic performance, with AUC, sensitivity, specificity, and accuracy of 0.935, 96.67%, 90.00%, and 93.52%. Moreover, the AUC of DECT was higher than those of Mix-0.3 (simulated SECT), cMRI, DWI, and cMRI+DWI.
CONCLUSIONS: Compared with simulated SECT, cMRI, and DWI, DECT appears to be a more accurate imaging technique for differentiating benign from malignant sinonasal lesions. KEY POINTS: • DE can differentiate benign sinonasal lesions from malignant ones based on DECT-derived qualitative parameters. • DECT appears to be more accurate in the diagnosis of sinonasal lesions when compared with simulated SECT, cMRI, and DWI.
© 2021. European Society of Radiology.

Entities:  

Keywords:  Diffusion magnetic resonance imaging; Magnetic resonance imaging; Paranasal sinus; Radiography, dual-energy scanned projection

Mesh:

Year:  2021        PMID: 34427744     DOI: 10.1007/s00330-021-08159-3

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  38 in total

1.  Diffusion kurtosis imaging for differentiating between the benign and malignant sinonasal lesions.

Authors:  Jing Xuan Jiang; Zuo Hua Tang; Yu Feng Zhong; Jin Wei Qiang
Journal:  J Magn Reson Imaging       Date:  2016-10-19       Impact factor: 4.813

Review 2.  Endoscopic resection of sinonasal cancers.

Authors:  Shirley Y Su; Michael E Kupferman; Franco DeMonte; Nicholas B Levine; Shaan M Raza; Ehab Y Hanna
Journal:  Curr Oncol Rep       Date:  2014-02       Impact factor: 5.075

3.  Intravoxel Incoherent Motion MR Imaging in the Differentiation of Benign and Malignant Sinonasal Lesions: Comparison with Conventional Diffusion-Weighted MR Imaging.

Authors:  Z Xiao; Z Tang; J Qiang; S Wang; W Qian; Y Zhong; R Wang; J Wang; L Wu; W Tang; Z Zhang
Journal:  AJNR Am J Neuroradiol       Date:  2018-01-25       Impact factor: 3.825

4.  Whole-tumor histogram analysis of monoexponential and advanced diffusion-weighted imaging for sinonasal malignant tumors: Correlations with histopathologic features.

Authors:  Zebin Xiao; Zuohua Tang; Jing Zhang; Guang Yang; Wenjiao Zeng; Jianfeng Luo; Yang Song; Zhongshuai Zhang
Journal:  J Magn Reson Imaging       Date:  2019-07-04       Impact factor: 4.813

5.  Sinonasal inverted papilloma: value of convoluted cerebriform pattern on MR imaging.

Authors:  T Y Jeon; H-J Kim; S-K Chung; H-J Dhong; H Y Kim; Y J Yim; S T Kim; P Jeon; K H Kim
Journal:  AJNR Am J Neuroradiol       Date:  2008-05-22       Impact factor: 3.825

Review 6.  Paranasal sinus imaging.

Authors:  Roberto Maroldi; Marco Ravanelli; Andrea Borghesi; Davide Farina
Journal:  Eur J Radiol       Date:  2008-03-28       Impact factor: 3.528

Review 7.  Paranasal sinus cancer.

Authors:  Paolo Bossi; Davide Farina; Gemma Gatta; Davide Lombardi; Piero Nicolai; Ester Orlandi
Journal:  Crit Rev Oncol Hematol       Date:  2015-10-19       Impact factor: 6.312

8.  Improved performance in differentiating benign from malignant sinonasal tumors using diffusion-weighted combined with dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Xin-Yan Wang; Fei Yan; Hui Hao; Jian-Xing Wu; Qing-Hua Chen; Jun-Fang Xian
Journal:  Chin Med J (Engl)       Date:  2015-03-05       Impact factor: 2.628

Review 9.  Dual-Energy CT: New Horizon in Medical Imaging.

Authors:  Hyun Woo Goo; Jin Mo Goo
Journal:  Korean J Radiol       Date:  2017-05-19       Impact factor: 3.500

10.  Differentiating Benign from Malignant Sinonasal Lesions: Feasibility of Diffusion Weighted MRI.

Authors:  Khaled M El-Gerby; Mohammad Waheed El-Anwar
Journal:  Int Arch Otorhinolaryngol       Date:  2017-01-04
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  3 in total

1.  Preoperative Prediction of the Aggressiveness of Oral Tongue Squamous Cell Carcinoma with Quantitative Parameters from Dual-Energy Computed Tomography.

Authors:  Xieqing Yang; Huijun Hu; Fang Zhang; Dongye Li; Zehong Yang; Guangzi Shi; Guoxiong Lu; Yusong Jiang; Lingjie Yang; Yu Wang; Xiaohui Duan; Jun Shen
Journal:  Front Oncol       Date:  2022-06-23       Impact factor: 5.738

2.  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

3.  MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors.

Authors:  Jinming Gu; Qiang Yu; Quanjiang Li; Juan Peng; Fajin Lv; Beibei Gong; Xiaodi Zhang
Journal:  Front Oncol       Date:  2022-09-23       Impact factor: 5.738

  3 in total

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