C-fos is a proto-oncogene that is expressed within some neurons following depolarization. The protein product, c-fos protein, can be identified by immunohistochemical techniques. Therefore, c-fos expression might be used as a marker for neuronal activity throughout the neuraxis following peripheral stimulation. This study has analyzed patterns of c-fos expression in both control and anesthetized animals and in anesthetized rats subjected to various forms of peripheral stimulation. Labeled cells were counted in the spinal cord, brainstem, hypothalamus, and thalamus. Little c-fos immunoreactivity was found in control animals. Prolonged inhalational anesthesia increased the number of labeled cells at several brainstem sites. Noxious stimulation of anesthetized rats induced c-fos within the neuraxis in patterns consistent with data obtained from electrophysiological studies and in additional locations for which few direct electrophysiological data are available, such as the ventrolateral medulla, the posterior hypothalamic nucleus, and the reuniens and paraventricular thalamic nuclei. Gentle mechanical stimulation was ineffective in inducing c-fos-like protein. The data suggest that c-fos can be used as a transynaptic marker for neuronal activity following noxious stimulation. However, c-fos is expressed only in some kinds of neurons following peripheral stimulation, and it therefore may be an incomplete marker for nociresponsive activity. In addition, at least a few neurons express c-fos protein in the absence of noxious stimulation. Experiments analyzing c-fos expression must be designed with care, as both extraneous stimuli and anesthetic depth influence the results.

The extraction of the centerlines of tubular objects in two and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. In this paper, we evaluate the effects of initialization, noise, and singularities on intensity ridge traversal and present multiscale heuristics and optimal-scale measures that minimize these effects. Monte Carlo experiments using simulated and clinical data are used to quantify how these "dynamic-scale" enhancements address clinical needs regarding speed, accuracy, and automation. In particular, we show that dynamic-scale ridge traversal is insensitive to its initial parameter settings, operates with little additional computational overhead, tracks centerlines with subvoxel accuracy, passes branch points, and handles significant image noise. We also illustrate the capabilities of the method for medical applications involving a variety of tubular structures in clinical data from different organs, patients, and imaging modalities.

The clinical recognition of abnormal vascular tortuosity, or excessive bending, twisting, and winding, is important to the diagnosis of many diseases. Automated detection and quantitation of abnormal vascular tortuosity from three-dimensional (3-D) medical image data would, therefore, be of value. However, previous research has centered primarily upon two-dimensional (2-D) analysis of the special subset of vessels whose paths are normally close to straight. This report provides the first 3-D tortuosity analysis of clusters of vessels within the normally tortuous intracerebral circulation. We define three different clinical patterns of abnormal tortuosity. We extend into 3-D two tortuosity metrics previously reported as useful in analyzing 2-D images and describe a new metric that incorporates counts of minima of total curvature. We extract vessels from MRA data, map corresponding anatomical regions between sets of normal patients and patients with known pathology, and evaluate the three tortuosity metrics for ability to detect each type of abnormality within the region of interest. We conclude that the new tortuosity metric appears to be the most effective in detecting several types of abnormalities. However, one of the other metrics, based on a sum of curvature magnitudes, may be more effective in recognizing tightly coiled, "corkscrew" vessels associated with malignant tumors.

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Purpose-One third of women with advanced human epidermal growth factor receptor 2 (HER-2)-positive breast cancer develop brain metastases; a subset progress in the CNS despite standard approaches. Medical therapies for refractory brain metastases are neither well-studied nor Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTERESTAlthough all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a "U" are those for which no compensation was received; those relationships marked with a "C" were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Author Manuscript Author ManuscriptAuthor Manuscript Author Manuscript established. We evaluated the safety and efficacy of lapatinib, an oral inhibitor of epidermal growth factor receptor (EGFR) and HER-2, in patients with HER-2-positive brain metastases. Results-Thirty-nine patients were enrolled. All patients had developed brain metastases while receiving trastuzumab; 37 had progressed after prior radiation. One patient achieved a PR in the brain by RECIST (objective response rate 2.6%, 95% conditional CI, 0.21% to 26%). Seven patients (18%) were progression free in both CNS and non-CNS sites at 16 weeks. Exploratory analyses identified additional patients with some degree of volumetric reduction in brain tumor burden. The most common adverse events (AEs) were diarrhea (grade 3, 21%) and fatigue (grade 3, 15%). Patients and Methods-Patients Conclusion-The study did not meet the predefined criteria for antitumor activity in highly refractory patients with HER-2-positive brain metastases. Because of the volumetric changes observed in our exploratory analysis, further studies are underway utilizing volumetric changes as a primary end point.

Rationale-Malignancy provokes regional changes to vessel shape. Characteristic vessel tortuosity abnormalities appear early during tumor development, affect initially healthy vessels, spread beyond the confines of tumor margins, and do not simply mirror tissue perfusion. The ability to detect and quantify tortuosity abnormalities on high-resolution MRA images offers a new approach to the noninvasive diagnosis of malignancy. This report evaluates a computerized, statistical method of analyzing the shapes of vessels extracted from MRA in diagnosing cancer.Materials and Methods-The regional vasculature of 34 healthy subjects was compared to the tumor-associated vasculature of 30 brain tumors prior to surgical resection. The operator performing the analysis was blinded to the diagnosis. Vessels were segmented from an MRA of each subject, a region of interest was defined in each tumor patient and was mapped to all healthy controls, and a statistical analysis of vessel shape measures was then performed over the region of interest. Many difficult cases were included, such as pinpoint, hemorrhagic, and irradiated tumors, as were hypervascular benign tumors. Tumors were identified as benign or malignant on the basis of histological evaluation.Results-A discriminant analysis performed at the study's conclusion successfully classified all but one of the 30 tumors as benign or malignant on the basis of vessel tortuosity.Discussion-Quantitative, statistical measures of vessel shape offer a new approach to the diagnosis and staging of disease. Although the methods developed under the current report must be tested against a new series of cases, initial results are promising.

Rationale and Objectives-Manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. The authors have developed an automated system for brain tumor segmentation that provides objective, reproducible segmentations that are close to the manual results. Additionally, the method segments white matter, grey matter, cerebrospinal fluid, and edema. The segmentation of pathology and healthy structures is crucial for surgical planning and intervention.Material and Methods-The method performs the segmentation of a registered set of MR images using an Expectation-Maximization scheme. The segmentation is guided by a spatial probabilistic atlas that contains expert prior knowledge about brain structures. This atlas is modified with the subject specific brain tumor prior that is computed based on contrast enhancement. Conclusion-The automated method can be applied to different types of tumors. Although the semi-automated method generates results that have higher level of agreement with the manual raters, the automatic method has the advantage of requiring no user supervision. Results-Five

Abstract-This paper discusses the development of a new method for the automatic segmentation of anatomical structures from volumetric medical images. Driving application is the segmentation of 3-D tumor structures from magnetic resonance images (MRI), which is known to be a very challenging segmentation problem due to the variability of tumor geometry and intensity patterns. Level set evolution combining global smoothness with the flexibility of topology changes offers significant advantages over conventional statistical classification followed by mathematical morphology. Level set evolution with constant propagation needs to be initialized either completely inside or outside and can leak through weak or missing boundary parts. Replacing the constant propagation term by a signed local statistical force overcomes these limitations and results in a region competition method that converges to a stable solution.Applied to MR images presenting tumors, probabilities for background and tumor regions are calculated from a preand post-contrast difference image and mixture-modelling fit of the histogram. The whole image is used for initialization of the level set evolution to segment the blobby-shaped tumor boundaries. Preliminary results on five cases presenting different tumors with significant shape and intensity variability demonstrate that the new method might become a powerful and efficient tool for the clinic. Validity is demonstrated by comparison with manual expert segmentation.