Fluorodeoxyglucose-positron emission tomography/computed tomography in the staging and evaluation of treatment response in a patient with Castleman's disease: a case report

<p>Abstract</p> <p>Introduction</p> <p>Castleman's disease is a rare lymphatic polyclonal disorder that is characterised by unicentric or multicentric lymph node hyperplasia and non-specific symptoms and signs including fever, asthenia, weight loss, enlarged liver and abnormally high blood levels of antibodies.</p> <p>Case presentation</p> <p>We present the case of a 74-year-old man with Castleman's disease. The disease was detected with a contrast-enhanced computed tomography (CT) scan and a fluorodeoxyglucose (FDG)-positron emission tomography (PET)/CT study; diagnosis was made with histopathology. After treatment with surgical excision followed by chemotherapy, the disease response was evaluated using both diagnostic techniques. However, only the PET study was able to identify the spread of the disease to the abdominal lymph nodes, which were both enlarged and normal size, and, after treatment, to evaluate the disease response.</p> <p>Conclusion</p> <p>Based on the results of previous case reports and on those of the present study, it seems that Castleman's disease has a high glucose metabolic activity. Therefore, the use of PET can be considered appropriate in order to stage or restage the disease and to evaluate the response of the disease to treatment.</p

Jalul 2017

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regression models with errors--in--variables, in the case where various data sets are merged into a single analysis and the observable variables deviate possibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possible non--normality of the data, normal--theory methods yield correct inferences for the parameters of interest and for the goodness--of--fit test. The theory described encompasses both the functional and structural model cases, and can be implemented using standard software for structural equations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented

Functional competence and cognition in individuals with amnestic mild cognitive impairment

OBJECTIVE The objective of this study is to characterize functional competence (measure of assistance needed for independence) on Performance Assessment of Self-Care Skills (PASS) Cognitively Mediated Instrumental Activities of Daily Living (C-IADL), in individuals with amnestic mild cognitive impairment (aMCI). It aims to determine: (1) the association of functional competence on PASS C-IADL tasks with neurocognitive test performance in aMCI, (2) its ability to discriminate individuals with aMCI from healthy control (HC) individuals, and (3) its added value in discriminating aMCI from HC individuals when combined with neurocognitive test performance. DESIGN Cross-sectional secondary analysis of baseline data from a cohort of individuals enrolled in a clinical trial (NCT02386670). SETTING Five university-affiliated outpatient clinics in Toronto, Canada. PARTICIPANTS aMCI (N = 137) and HC (N = 51) participants, all aged 60 years or older. METHODS We assessed the relationship between functional competence on three C-IADL PASS tasks (shopping, bill paying, and checkbook balancing) and neurocognitive tests in 137 participants with aMCI using multiple linear regressions. Additionally, we constructed receiver operating characteristic curves to assess the role of PASS functional competence in discriminating between 137 aMCI and 51 HC participants. RESULTS Functional competence on PASS was significantly associated with tests of verbal memory, information processing speed, and executive function. It demonstrated 79% accuracy in discriminating aMCI from HC participants. Combining functional competence on PASS with individual neurocognitive tests significantly increased the discriminant accuracy of individual tests, and neurocognitive test scores combined with functional competence on PASS had the highest discriminant accuracy (94%). CONCLUSION Functional competence on PASS is predicted by the underlying cognitive deficits and possibly captures additional element of effort that could improve the diagnostic accuracy of aMCI when combined with neurocognitive tests. Thus, PASS appears to be a promising tool for assessment of functional competence in aMCI in clinical or research settings