Gastrointestinal stromal tumor: a method for optimizing the timing of CT scans in the follow-up of cancer patients.

PURPOSE: To develop a mathematical model to adjust the timing of computed tomography (CT) scans with the hazard of cancer recurrence in time to facilitate early detection of cancer recurrence. MATERIALS AND METHODS: The clinical data were extracted from the randomized Scandinavian Sarcoma Group (SSG) XVIII/Arbeitsgemeinschaft Internistische Onkologie (AIO) trial database. The SSG XVIII/AIO trial was registered (trial no. NCT00116935) and approved by the national or institutional review boards. In the trial, 1- and 3-year durations of adjuvant imatinib mesylate in the treatment of patients with gastrointestinal stromal tumor (GIST) were compared. A nonhomogeneous Poisson model with a piecewise log-constant hazard in time that accounts for the nonlinear pattern of GIST recurrence was applied to tumor site, mitotic count, and recurrence data. The optimal times to obtain follow-up CT scans were computed by modifying the frequency of CT scans with the hazard of tumor recurrence in time. The hazard-adjusted follow-up schedules were compared with the National Comprehensive Cancer Network (NCCN) guidelines of the United States, which suggest imaging with CT at intervals of 3-6 months for 3-5 years and then annually. RESULTS: Optimized timing of CT scans on the basis of hazard of recurrence resulted in follow-up schedule options where CT is performed more sparsely than in the NCCN guidelines during adjuvant imatinib administration and more frequently, at approximately 3-month intervals, during the first 2 years that follow imatinib discontinuation when the risk of recurrence was the greatest. The number of CT scans could be reduced by a median of 31% (from 13 to nine) compared with the standard schedules within the first 6 years of follow-up without increasing the delay in recurrence detection. CONCLUSION: Detection of GIST recurrence may be enhanced by adjusting the timing of the CT scans with the hazard of recurrence. The method may be applicable to other human tumor types. Online supplemental material is available for this article.

A multi-metabolite analysis of serum by 1H NMR spectroscopy: early systemic signs of Alzheimer's disease.

A three-molecular-window approach for (1)H NMR spectroscopy of serum is presented to obtain specific molecular data on lipoproteins, various low-molecular-weight metabolites, and individual lipid molecules together with their degree of (poly)(un)saturation. The multiple data were analysed with self-organising maps, illustrating the strength of the approach as a holistic metabonomics framework in solely data-driven metabolic phenotyping. We studied 180 serum samples of which 30% were related to mild cognitive impairment (MCI), a neuropsychological diagnosis with severely increased risk for Alzheimer's disease (AD). The results underline the association between MCI and the metabolic syndrome (MetS). Additionally, the low relativeamount of omega-3 fatty acids appears more indicative of MCI than low serum omega-3 or polyunsaturated fatty acid concentration as such. The analyses also feature the role of elevated glycoproteins in the risk for AD, supporting the view that coexistence of inflammation and the MetS forms a high risk condition for cognitive decline.

Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles.

A recently introduced Bayesian model for magnetoencephalographic (MEG) data consistently localized multiple simulated dipoles with the help of marginalization of spatiotemporal background noise covariance structure in the analysis [Jun et al., (2005): Neuroimage 28:84-98]. Here, we elaborated this model to include subject's individual brain surface reconstructions with cortical location and orientation constraints. To enable efficient Markov chain Monte Carlo sampling of the dipole locations, we adopted a parametrization of the source space surfaces with two continuous variables (i.e., spherical angle coordinates). Prior to analysis, we simplified the likelihood by exploiting only a small set of independent measurement combinations obtained by singular value decomposition of the gain matrix, which also makes the sampler significantly faster. We analyzed both realistically simulated and empirical MEG data recorded during simple auditory and visual stimulation. The results show that our model produces reasonable solutions and adequate data fits without much manual interaction. However, the rigid cortical constraints seemed to make the utilized scheme challenging as the sampler did not switch modes of the dipoles efficiently. This is problematic in the presence of evidently highly multimodal posterior distribution, and especially in the relative quantitative comparison of the different modes. To overcome the difficulties with the present model, we propose the use of loose orientation constraints and combined model of prelocalization utilizing the hierarchical minimum-norm estimate and multiple dipole sampling scheme.