Person:

Black, Peter

BACKGROUND: Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE: This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. METHODS: We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. RESULTS: Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. CONCLUSION: The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

Objective: To quantify size and localization differences between tumors presenting with seizures vs nonseizure neurological symptoms. Design: Retrospective imaging survey. We performed magnetic resonance imaging–based morphometric analysis and nonparametric mapping in patients with brain tumors. Setting: University-affiliated teaching hospital. Patients or Other Participants: One hundred twenty-four patients with newly diagnosed supratentorial glial tumors. Main Outcome Measures: Volumetric and mapping methods were used to evaluate differences in size and location of the tumors in patients who presented with seizures as compared with patients who presented with other symptoms. Results: In high-grade gliomas, tumors presenting with seizures were smaller than tumors presenting with other neurological symptoms, whereas in low-grade gliomas, tumors presenting with seizures were larger. Tumor location maps revealed that in high-grade gliomas, deep-seated tumors in the pericallosal regions were more likely to present with nonseizure neurological symptoms. In low-grade gliomas, tumors of the temporal lobe as well as the insular region were more likely to present with seizures. Conclusions: The influence of size and location of the tumors on their propensity to cause seizures varies with the grade of the tumor. In high-grade gliomas, rapidly growing tumors, particularly those situated in deeper structures, present with non–seizure-related symptoms. In low-grade gliomas, lesions in the temporal lobe or the insula grow large without other symptoms and eventually cause seizures. Quantitative image analysis allows for the mapping of regions in each group that are more or less susceptible to seizures. Seizures are encountered in a majority of patients with primary brain tumors and are a major cause of morbidity in these patients.1,2 Thirty percent to 50% of patients experience a seizure by the time their tumors are diagnosed, and an additional 6% to 45% of patients who do not initially present with seizures eventually develop them.3- 5 Characteristics of brain tumors and their mechanism in causing seizures in patients are incompletely understood.4,6 Low-grade, well-differentiated gliomas,1,6- 9 cortically located tumors,3,10- 14 and location in the temporal/frontal and motor/sensory cortices6,8,15- 17 are more frequently associated with seizures. Although there is a high incidence of seizures in these patients, treatment strategies remain poorly defined. Prophylactic anticonvulsant therapy, shown to be ineffective in preventing seizures in patients with brain tumors in multiple large-scale studies,12,18- 20 is not recommended by the American Academy of Neurology.5 Nonetheless, prophylaxis remains a widespread practice21 because of difficulty in determining which patients are at greatest risk for seizures. Determination of morphometric factors influencing seizures would help in identifying patients at greatest risk for early, targeted treatment and prevent potentially toxic, unnecessary treatment in patients at minimal risk. Although studies examining brain tumors in relationship to epilepsy have localized tumors to a particular lobe,10 few studies have performed quantitative volumetric or spatial mapping analysis of tumors in relation to their epileptogenic potential. Regions within a particular lobe are likely to exhibit different epileptogenic potential to tumor invasion and tumors frequently affect multiple contiguous lobes.6 Modern imaging techniques allow for analysis of lesions over a large group of subjects through registration and mapping techniques. In this study, we used these techniques to examine the size and location of primary supratentorial glial brain tumors and characterized their propensity to cause seizures at presentation.

Our purpose was to develop temperature sensitive MR sequences and image-processing techniques to asses their potential of monitoring interstitial laser therapy (ILT) in brain tumors (n = 3) and liver tumors (n = 7). ILT lasted 2 to 26 minutes, while images from T1-weighted Fast-Spin-Echo (FSE)- or Spoiled Gradient-Recalled (SPGR) sequences were acquired within 5 - 13 seconds.

Pixel subtraction and visualization of T1-weighted images or optical flow computation was done within less than 110 msec. Alternating, phase-mapping of real- and imaginary components of SPGR sequences was performed within 220 msec.

Pixel subtraction of T1-weighted images identified thermal changes in liver and brain tumors but could not evaluate the temperature values as chemical-shift based imaging, which was however, more affected by susceptibility effects and motion. Optical flow computation displayed the predicted course of thermal changes and revealed that the rate of heat deposition can be anisotropic, which may be related to heterogeneous tumor structure and/or vascularization.