RuoFan Sheng1,2,3, YanHong Xie4, MengSu Zeng5,6,7, Yuan Ji4, ShengXiang Rao1,2,3, CaiZhong Chen1,2,3. 1. Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. 2. Shanghai Institute of Medical Imaging, Shanghai, China. 3. Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China. 4. Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. 5. Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. mengsuzeng@163.com. 6. Shanghai Institute of Medical Imaging, Shanghai, China. mengsuzeng@163.com. 7. Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China. mengsuzeng@163.com.
Abstract
PURPOSE: To investigate MR characteristics in differentiating primary hepatic neuroendocrine neoplasm (PHNEN) from metastatic hepatic neuroendocrine neoplasm (MHNEN). MATERIALS AND METHODS: Thirty-nine patients with histopathologically proven liver neuroendocrine neoplasm were retrospectively analyzed. The morphological and MR signal features on T1, T2-weighted, dynamic-enhanced, and diffusion-weighted imaging were evaluated and compared between the PHNEN group (n = 12) and the MHNEN group (n = 27). RESULTS: The tumor size (P = 0.0084), number (P = 0.017), distribution (P = 0.000), contour (P = 0.041), the presence of capsule-like enhancement (P = 0.034), tumor homogeneity (P = 0.018) and the apparent diffusion coefficient (ADC) values (P = 0.024) were different between PHNENs and MHNENs. Large, solitary or massive-growing nodules with lobulated or irregular contour, capsule-like enhancement, heterogeneous signals or lower ADC values supported the diagnosis of PHNEN compared with MHNEN. ROC analysis demonstrated an area under the curve of 0.746, when the optimal cutoff value of 1.049 × 10(-3) mm(2)/s was used, a sensitivity of 63.0 % (95 % CI, 44.2-79.4 %), a specitivity of 80.0 % (95 % CI, 50.1-96.4 %), a positive predictive value of 89.5 % (95 % CI, 70.9-98.2 %), and a negative predictive value of 44.4 % (95 % CI, 23.4-67.0 %) can be achieved. CONCLUSIONS: MRI may provide valuable information for the diagnosis and differential diagnosis of PHNENs and MHNENs.
PURPOSE: To investigate MR characteristics in differentiating primary hepatic neuroendocrine neoplasm (PHNEN) from metastatic hepatic neuroendocrine neoplasm (MHNEN). MATERIALS AND METHODS: Thirty-nine patients with histopathologically proven liver neuroendocrine neoplasm were retrospectively analyzed. The morphological and MR signal features on T1, T2-weighted, dynamic-enhanced, and diffusion-weighted imaging were evaluated and compared between the PHNEN group (n = 12) and the MHNEN group (n = 27). RESULTS: The tumor size (P = 0.0084), number (P = 0.017), distribution (P = 0.000), contour (P = 0.041), the presence of capsule-like enhancement (P = 0.034), tumor homogeneity (P = 0.018) and the apparent diffusion coefficient (ADC) values (P = 0.024) were different between PHNENs and MHNENs. Large, solitary or massive-growing nodules with lobulated or irregular contour, capsule-like enhancement, heterogeneous signals or lower ADC values supported the diagnosis of PHNEN compared with MHNEN. ROC analysis demonstrated an area under the curve of 0.746, when the optimal cutoff value of 1.049 × 10(-3) mm(2)/s was used, a sensitivity of 63.0 % (95 % CI, 44.2-79.4 %), a specitivity of 80.0 % (95 % CI, 50.1-96.4 %), a positive predictive value of 89.5 % (95 % CI, 70.9-98.2 %), and a negative predictive value of 44.4 % (95 % CI, 23.4-67.0 %) can be achieved. CONCLUSIONS: MRI may provide valuable information for the diagnosis and differential diagnosis of PHNENs and MHNENs.
Entities:
Keywords:
Liver; Magnetic resonance imaging; Neuroendocrine neoplasm
Authors: Marco Armbruster; Steven Sourbron; Alexander Haug; Christoph J Zech; Michael Ingrisch; Christoph J Auernhammer; Konstantin Nikolaou; Philipp M Paprottka; Carsten Rist; Maximilian F Reiser; Wieland H Sommer Journal: Invest Radiol Date: 2014-01 Impact factor: 6.016
Authors: F L Giesel; C Kratochwil; A Mehndiratta; S Wulfert; J H Moltz; C M Zechmann; H U Kauczor; U Haberkorn; S Ley Journal: Eur J Radiol Date: 2012-01-10 Impact factor: 3.528