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Dual-energy CT in differentiating benign sinonasal lesions from malignant ones: comparison with simulated single-energy CT, conventional MRI, and DWI.
Wang, Peng; Tang, Zuohua; Xiao, Zebin; Hong, Rujian; Wang, Rong; Wang, Yuzhe; Zhan, Yang.
Afiliação
  • Wang P; Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
  • Tang Z; Department of Radiology, The Affiliated Renmin Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People's Republic of China.
  • Xiao Z; Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China. tzh518sunny@163.com.
  • Hong R; Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
  • Wang R; Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, 19104, USA.
  • Wang Y; Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
  • Zhan Y; The Shanghai Institution of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.
Eur Radiol ; 32(2): 1095-1105, 2022 Feb.
Article em En | MEDLINE | ID: mdl-34427744
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Imagem de Difusão por Ressonância Magnética Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Imagem de Difusão por Ressonância Magnética Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article