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Assessment of the Focal Hepatic Lesions Using Diffusion Tensor Magnetic Resonance Imaging
Siham Ait Oussous1, Saïd Boujraf2, Imane Kamaoui3
1Department of Radiology and Clinical Imaging, University Hospital of Fez, Fez, Morocco.
Abstract:
The goal is assessing the diffusion magnetic resonance imaging (dMRI) method efficiency in characterizing focal hepatic lesions (FHLs). About 28-FHL patients were studied in Radiology and Clinical Imaging Department of our University Hospital using 1.5 Tesla MRI system between January 2010 and June 2011. Patients underwent hepatic MRI consisting of dynamic T1- and T2-weighted imaging. The dMRI was performed with b-values of 200 s/mm(2) and 600 s/mm(2). About 42 lesions measuring more than 1 cm were studied including the variation of the signal according to the b-value and the apparent diffusion coefficient (ADC). The diagnostic imaging reference was based on standard MRI techniques data for typical lesions and on histology after surgical biopsy for atypical lesions. About 38 lesions were assessed including 13 benign lesions consisting of 1 focal nodular hyperplasia, 8 angiomas, and 4 cysts. About 25 malignant lesions included 11 hepatocellular carcinoma, 9 hepatic metastases, 1 cholangiocarcinoma, and 4 lymphomas. dMRI of soft lesions demonstrated higher ADC of 2.26 ± 0.75 mm(2)/s, whereas solid lesions showed lower ADC 1.19 ± 0.33 mm(2)/s with significant difference (P = 0.05). Discrete values collections were noticed. These results were correlated to standard MRI and histological findings. Sensitivity of 93% and specificity of 84% were found in diagnoses of malignant tumors with an ADC threshold of 1.6 × 10(-3) mm(2)/s. dMRI is important characterization method of FHL. However, it should not be used as single criteria of hepatic lesions malignity. MRI, clinical, and biological data must be correlated. Significant difference was found between benign and solid malignant lesions without threshold ADC values. Hence, it is difficult to confirm ADC threshold differentiating the lesion classification.
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