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Discrimination of Malignant versus Benign Mediastinal Lymph Nodes Using Diffusion MRI with an IVIM Model.
Qi, Li-Ping; Yan, Wan-Pu; Chen, Ke-Neng; Zhong, Zheng; Li, Xiao-Ting; Cai, Kejia; Sun, Ying-Shi; Zhou, Xiaohong Joe.
Afiliação
  • Qi LP; Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Rd, Hai Dian District, Beijing, 100142, China.
  • Yan WP; Center for MR Research, and Department of Radiology, University of Illinois at Chicago, 2242 West Harrison Street, Suite 103, M/C 831, Chicago, IL, 60612, USA.
  • Chen KN; Department of Thoracic Oncosurgery, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China.
  • Zhong Z; Department of Thoracic Oncosurgery, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China.
  • Li XT; Center for MR Research, and Department of Radiology, University of Illinois at Chicago, 2242 West Harrison Street, Suite 103, M/C 831, Chicago, IL, 60612, USA.
  • Cai K; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.
  • Sun YS; Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Rd, Hai Dian District, Beijing, 100142, China.
  • Zhou XJ; Center for MR Research, and Department of Radiology, University of Illinois at Chicago, 2242 West Harrison Street, Suite 103, M/C 831, Chicago, IL, 60612, USA.
Eur Radiol ; 28(3): 1301-1309, 2018 Mar.
Article em En | MEDLINE | ID: mdl-28929210
OBJECTIVES: To investigate the value of an intravoxel incoherent motion (IVIM) diffusion model for discriminating malignant versus benign mediastinal lymph nodes (MLN). METHODS: Thirty-five subjects with enlarged MLN were scanned at 1.5 Tesla. Diffusion-weighted imaging was performed with eight b-values. IVIM parameters D, D*, and f, as well as apparent diffusion coefficient (ADC) from a mono-exponential model were obtained. 91 nodes (49 malignant and 42 benign) were analysed with pathologic (n=90) or radiologic (n=1) confirmations. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance. RESULTS: The mean values of D, ADC, and f for the malignant group were significantly lower than those for the benign group (p<0.001), while D* showed no significant difference (p=0.281). In the ROC analysis, the combination of D and f produced the largest area under the curve (0.953) compared to ADC or other individual IVIM parameters, leading to the best specificity (92.9%) and diagnostic accuracy (90.1%). CONCLUSION: This study demonstrates that the combination of IVIM parameters can improve differentiation between malignant and benign MLN as compared to using ADC alone. KEY POINTS: • Diffusion MRI is useful for non-invasively discriminating malignant versus benign lymph nodes. • A mono-exponential model is not adequate to characterise diffusion process in lymph nodes. • IVIM model is advantageous over mono-exponential model for assessing lymph node malignancy. • Combination of IVIM parameters improves differentiation of malignant versus benign lymph nodes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imagem de Difusão por Ressonância Magnética / Linfadenopatia / Linfonodos / Neoplasias do Mediastino Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imagem de Difusão por Ressonância Magnética / Linfadenopatia / Linfonodos / Neoplasias do Mediastino Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2018 Tipo de documento: Article