Computed tomography and magnetic resonance imaging features of lipid-rich neuroendocrine tumors of the pancreas

AIM:To clarify the computed tomography(CT) and magnetic resonance imaging(MRI) characteristics of lipid-rich pancreatic neuroendocrine tumors(Pan NETs).METHODS:Enhanced CT and MRI performed before pancreatectomy in 29 patients with 34 histologicallyconfirmed Pan NETs was retrospectively reviewed. Tumor attenuation on CT and signal intensities on conventional(T1- and T2-weighted) and chemical shift MRI were qualitatively analyzed and compared alongside adipose differentiation-related protein(ADRP) immunostaining(ADRP-positive:lipid-rich; ADRP-negative:non-lipid-rich) results using Fisher's exact test or the Mann-Whitney U test. Signal intensity index on chemical shift MRI was quantitatively assessed.RESULTS:There were 15 lipid-rich Pan NETs(44.1%) in 12 patients(41.4%). Tumor attenuation during the early,portal venous,and delayed phases of enhanced CT(P = 0.888,0.443,and 0.359,respectively) and signal intensities on conventional MRI(P = 0.698 and 0.798,respectively) were not significantly differentbetween lipid-rich and non-lipid-rich Pan NETs. Four of the 15 lipid-rich Pan NETs exhibited high signal intensity on subtraction chemical shift MRI,and the association of high signal intensity on subtraction imaging with lipid-rich Pan NETs was significant(4 of 15 lipid-rich Pan NETs,26.73%,vs 0 of 19 non-lipid-rich Pan NETs,0%,P = 0.029). Lipid-rich Pan NETs showed a significantly higher signal intensity index than non-lipidrich Pan NETs(0.6% ± 14.1% vs-10.4% ± 14.4%,P = 0.004). Eight of 15 lipid-rich Pan NETs,vs 0 of 19 nonlipid-rich Pan NETs,had positive signal intensity index values in concordance with lipid contents.C O N C L U S I O N :C T c o n t ra s t e n h a n c e m e n t a n d conventional MR signal intensities are similar in lipidrich and non-lipid-rich Pan NETs. Chemical shift MRI can demonstrate cytoplasmic lipids in Pan NETs.

Resolution of epidural hematoma related to osteoporotic fracture after percutaneous vertebroplasty

We are the first to report a case that showed spontaneous resolution of epidural hematoma which was related to a steroid-induced osteoporotic compression fracture.The patient had a painful fracture with an intravertebral cleft at L1 accompanying an epidural hematoma posteriorly.Immediate pain relief was achieved after percutaneous vertebroplasty.Complete resolution of hematoma was noted three months after procedure.We theorized that intravertebral stability after treatment might have played a role in this patient.

Balloon test occlusion of internal carotid artery: Angiographic findings predictive of results

AIM: To reveal angiographic findings to predict the re-sult of balloon test occlusion(BTO).METHODS: The cerebral angiograms of 42 consecu-tive patients who underwent cerebral angiography in-cluding both the Matas and Allcock maneuvers and BTO were retrospectively analyzed. Visualization of the an-terior cerebral artery(ACA) and the middle cerebral ar-tery(MCA) by the cross flow on the tested side during the Matas or Allcock maneuver was graded on a 5-point scale. Circle of Willis(COW) anatomy with respect to the presence/absence of a collateral path to reach the tested internal carotid artery(ICA) was classified intofour categories. A univariate logistic analysis was used to analyze the associations between each angiographic finding and the BTO result. Sensitivity, specificity, accu-racy, positive predictive value, and negative predictive value for each finding were calculated. RESULTS: Five patients(12%) were BTO-positive and the remaining 37 patients(88%) were BTO-negative. Visualizations of the ACA and MCA as well as the COW anatomy were significantly associated with the BTO re-sult(P = 0.0051 for ACA, P = 0.0002 for MCA, and P < 0.0001 for COW anatomy). In particular, good MCA vi-sualization and the presence of an anterior connection(collateral path to the tested ICA from the contralateral ICA via the anterior communicating artery) in the COW were highly predictive for negative BTO(negative pre-dictive value = 100% for both).

Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

Alzheimer’s disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (ANN) and a support vector machine (SVM), which were trained with 4 morphological and 6 functional image features, to 15 AD patients and 15 clinically normal (CN) subjects for classification of AD. The area under the receiver operating characteristic curve (AUC) values for the two systems based on the ANN and SVM with both image?features were 0.901 and 0.915, respectively. The AUC values for the ANN-and SVM-based systems with the morphological features were 0.710 and 0.660, respectively, and those with the functional features were 0.878 and 0.903, respectively. Our preliminary results suggest that the proposed method may have potential for assisting radiologists in the differential diagnosis of AD patients by using morphological and functional image features.

Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps

Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.