Objective Bayesian fMRI analysis-a pilot study in different clinical environments.

Functional MRI (fMRI) used for neurosurgical planning delineates functionally eloquent brain areas by time-series analysis of task-induced BOLD signal changes. Commonly used frequentist statistics protect against false positive results based on a p-value threshold. In surgical planning, false negative results are equally if not more harmful, potentially masking true brain activity leading to erroneous resection of eloquent regions. Bayesian statistics provides an alternative framework, categorizing areas as activated, deactivated, non-activated or with low statistical confidence. This approach has not yet found wide clinical application partly due to the lack of a method to objectively define an effect size threshold. We implemented a Bayesian analysis framework for neurosurgical planning fMRI. It entails an automated effect-size threshold selection method for posterior probability maps accounting for inter-individual BOLD response differences, which was calibrated based on the frequentist results maps thresholded by two clinical experts. We compared Bayesian and frequentist analysis of passive-motor fMRI data from 10 healthy volunteers measured on a pre-operative 3T and an intra-operative 1.5T MRI scanner. As a clinical case study, we tested passive motor task activation in a brain tumor patient at 3T under clinical conditions. With our novel effect size threshold method, the Bayesian analysis revealed regions of all four categories in the 3T data. Activated region foci and extent were consistent with the frequentist analysis results. In the lower signal-to-noise ratio 1.5T intra-operative scanner data, Bayesian analysis provided improved brain-activation detection sensitivity compared with the frequentist analysis, albeit the spatial extents of the activations were smaller than at 3T. Bayesian analysis of fMRI data using operator-independent effect size threshold selection may improve the sensitivity and certainty of information available to guide neurosurgery.

Imaging in sepsis-associated encephalopathy--insights and opportunities.

Sepsis-associated encephalopathy (SAE) refers to a clinical spectrum of acute neurological dysfunction that arises in the context of sepsis. Although the pathophysiology of SAE is incompletely understood, it is thought to involve endothelial activation, blood-brain barrier leakage, inflammatory cell migration, and neuronal loss with neurotransmitter imbalance. SAE is associated with a high risk of mortality. Imaging studies using MRI and CT have demonstrated changes in the brains of patients with SAE that are also seen in disorders such as stroke. Next-generation imaging techniques such as magnetic resonance spectroscopy, diffusion tensor imaging and PET, as well as experimental imaging modalities, provide options for early identification of patients with SAE, and could aid in identification of pathophysiological processes that represent possible therapeutic targets. In this Review, we explore the recent literature on imaging in SAE, relating the findings of these studies to pathological data and experimental studies to obtain insights into the pathophysiology of sepsis-associated neurological dysfunction. Furthermore, we suggest how novel imaging technologies can be used for early-stage proof-of-concept and proof-of-mechanism translational studies, which may help to improve diagnosis in SAE.

Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors.

Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit "extra prior" for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic.

FIGO staging system for endometrial cancer: added benefits of MR imaging.

Endometrial cancer is the most commonly diagnosed gynecologic malignancy in the United States. This pathologic condition is staged with the International Federation of Gynecology and Obstetrics (FIGO) system. The FIGO staging system recently underwent significant revision, which has important implications for radiologists. Key changes incorporated into the 2009 FIGO staging system include simplification of stage I disease and removal of cervical mucosal invasion as a distinct stage. Magnetic resonance (MR) imaging is essential for the preoperative staging of endometrial cancer because it can accurately depict the depth of myometrial invasion, which is the most important morphologic prognostic factor and correlates with tumor grade, presence of lymph node metastases, and overall patient survival. Diffusion-weighted MR imaging and dynamic contrast medium-enhanced MR imaging are useful adjuncts to standard morphologic imaging and may improve overall staging accuracy.

Evaluation of depth of myometrial invasion and overall staging in endometrial cancer: comparison of diffusion-weighted and dynamic contrast-enhanced MR imaging.

PURPOSE: To compare the diagnostic performance of diffusion-weighted (DW) magnetic resonance (MR) imaging with that of dynamic contrast material-enhanced (DCE) MR imaging in evaluating the depth of myometrial invasion and overall stage in patients with endometrial cancer. MATERIALS AND METHODS: The institutional review board approved this retrospective study; patient consent was not required. From May 2008 to February 2010, 48 women with endometrial cancer underwent preoperative MR imaging, including T1- and T2-weighted imaging, DW MR imaging (b=0 and 800 sec/mm2) and DCE MR imaging. Two radiologists independently interpreted the depth of myometrial invasion, overall stage, and presence of pitfalls associated with inaccurate assessment of myometrial invasion at T1- and T2-weighted imaging, DW MR imaging, and DCE MR imaging. Myometrial invasion and overall stage were compared by using the McNemar test, and κ statistics were used for reader agreement. RESULTS: For assessing the depth of myometrial invasion, diagnostic accuracy, sensitivity, and specificity, respectively, were as follows: DW MR imaging-reader 1, 90%, 84%, and 100%; reader 2, 85%, 84%, and 88%; DCE MR imaging-reader 1, 71%, 61%, and 88%; reader 2, 79%, 77%, and 82%. The improvement in diagnostic accuracy for reader 1 was significant (P=.035). For myometrial invasion, κ values were 0.75 with DW MR imaging and 0.26 with DCE MR imaging. There was no association between inaccurate assessment of myometrial invasion and standard pitfalls with DW MR imaging. Readers 1 and 2 correctly staged more patients by using DW MR imaging (39 and 38 patients, respectively) than by using DCE MR imaging (29 and 30 patients, respectively) (P<.05). For overall stage, κ values were 0.74 with DW MR imaging and 0.22 with DCE MR imaging. CONCLUSION: DW MR imaging has superior diagnostic accuracy in the assessment of myometrial invasion and significantly higher staging accuracy compared with DCE MR imaging.