Faculty Bibliography

An arteriovenous malformation is a tangle of dysplastic vessels (nidus) fed by arteries and drained by veins without intervening capillaries, forming a high-flow, low-resistance shunt between the arterial and venous systems. Arteriovenous malformations in the brain have a low estimated prevalence but are an important cause of intracerebral haemorrhage in young adults. For previously unruptured malformations, bleeding rates are approximately 1% per year. Once ruptured, the subsequent risk increases fivefold, depending on associated aneurysms, deep locations, deep drainage and increasing age. Recent findings from novel animal models and genetic studies suggest that arteriovenous malformations, which were long considered congenital, arise from aberrant vasculogenesis, genetic mutations and/or angiogenesis after injury. The phenotypical characteristics of arteriovenous malformations differ among age groups, with fistulous lesions in children and nidal lesions in adults. Diagnosis mainly involves imaging techniques, including CT, MRI and angiography. Management includes observation, microsurgical resection, endovascular embolization and stereotactic radiosurgery, alone or in any combination. There is little consensus on how to manage patients with unruptured malformations; recent studies have shown that patients managed medically fared better than those with intervention at short-term follow-up. By contrast, interventional treatment is preferred following a ruptured malformation to prevent rehaemorrhage. Management continues to evolve as new mechanistic discoveries and reliable animal models raise the possibility of developing drugs that might prevent the formation of arteriovenous malformations, induce obliteration and/or stabilize vessels to reduce rupture risk. For an illustrated summary of this Primer, visit: http://go.nature.com/TMoAdn.

The Leksell GammaPlan software version 10 introduces a CT image-based segmentation tool for automatic skull definition and a convolution dose calculation algorithm for tissue inhomogeneity correction. The purpose of this work was to evaluate the impact of these new approaches on routine clinical Gamma Knife treatment planning. Sixty-five patients who underwent CT image-guided Gamma Knife radiosurgeries at the University of Pittsburgh Medical Center in recent years were retrospectively investigated. The diagnoses for these cases include trigeminal neuralgia, meningioma, acoustic neuroma, AVM, glioma, and benign and metastatic brain tumors. Dose calculations were performed for each patient with the same dose prescriptions and the same shot arrangements using three different approaches: 1) TMR 10 dose calculation with imaging skull definition; 2) convolution dose calculation with imaging skull definition; 3) TMR 10 dose calculation with conventional measurement-based skull definition. For each treatment matrix, the total treatment time, the target coverage index, the selectivity index, the gradient index, and a set of dose statistics parameters were compared between the three calculations. The dose statistics parameters investigated include the prescription isodose volume, the 12 Gy isodose volume, the minimum, maximum and mean doses on the treatment targets, and the critical structures under consideration. The difference between the convolution and the TMR 10 dose calculations for the 104 treatment matrices were found to vary with the patient anatomy, location of the treatment shots, and the tissue inhomogeneities around the treatment target. An average difference of 8.4% was observed for the total treatment times between the convolution and the TMR algorithms. The maximum differences in the treatment times, the prescription isodose volumes, the 12 Gy isodose volumes, the target coverage indices, the selectivity indices, and the gradient indices from the convolution and the TMR 10 calculations are 14.9%, 16.4%, 11.1%, 16.8, 6.9%, and 11.4%, respectively. The maximum differences in the minimum and the mean target doses between the two calculation algorithms are 8.1% and 4.2% of the corresponding prescription doses. The maximum differences in the maximum and the mean doses for the critical structures between the two calculation algorithms are 1.3 Gy and 0.7 Gy. The results from the two skull definition methods with the TMR 10 algorithm agree either within ± 2.5% or 0.3 Gy for the dose values, except for a 4.9% difference in the treatment times for a lower cerebellar lesion. The imaging skull definition method does not affect Gamma Knife dose calculation considerably when compared to the conventional measurement-based skull definition method, except in some extreme cases. Large differences were observed between the TMR 10 and the convolution calculation method for the same dose prescription and the same shot arrangements, indicating that the implementation of the convolution algorithm in routine clinical use might be desirable for optimal dose calculation results.

Physicians are being challenged to obtain data for outcomes research and measures of quality practice in medicine. We developed a prospective data collection system (registry) that provides data points across all elements of a neurosurgical stereotactic radiosurgery practice. The registry architecture is scalable and suitable for any aspect of neurosurgical practice. Our purpose was to outline the challenges in creating systems for high quality data acquisition and describe experiences in initial testing and use. Over a two year period, a multicenter team working with software engineers developed a comprehensive radiosurgery registry based on a MS-Sequel(R) server platform. Three neurosurgeons at one center were responsible for final editing. Alpha testing began in September 2012 and server-based beta testing began in February 2013. The major elements included demographics, disease-based items (47 categories for different brain tumors, vascular malformations, and functional disorders) with relevant clinical grading systems, treatment-based items (imaging, physics, clinical), and follow-up data (clinical, imaging, subsequent therapeutics). Nine hundred patients were entered into the registry at one test center, with new entries and follow-up data entered daily at the point of contact. With experience, the mean time for one new entry was 6 minutes. Mean time for one follow-up entry was 45 seconds. The system was made secure for individual use and amenable for both data entry and research. Analytics used different filters to create customized outcomes charts as selected by the user (e.g., survival, neurologic function, complications). A local or multicenter prospective data collection registry was created for use across 47 clinical indications for stereotactic cranial radiosurgery. Further refinement of fields and logic is ongoing. The system is reliable, robust, and allows use of rapid analytical tools. Large medical registries will become widely used for collection and analysis of large data sets and should have broad applicability to many other elements of neurosurgical and medical practice.