No abstract

Imaging plays a critical role in diagnosing schwannomatosis, and a basic understanding of this syndrome is of interest to diagnostic radiologists. Moreover, it is imperative that radiologists be able to differentiate schwannomatosis from NF2 on imaging because there are significant differences in the management of these two diseases and clinical outcomes for affected patients.

Object Collectively, metastatic tumors are the most common malignancy encountered in the adult central nervous system (CNS), arising most often from lung, breast, skin, and gastrointestinal tract carcinomas. Limited information is available in the literature regarding solid nonhematopoietic CNS metastases in children. The authors carried out a retrospective study of pediatric metastatic neoplasms to the CNS treated in a 30-year period to characterize their frequency, common histological subtypes, and sites of origin. Methods The archival pathology files were searched (1981–2011) for metastatic tumors to the CNS in patients 21 years of age and younger. Pathology material was reviewed, tumors were classified by site of origin and histological subtype, and survival was evaluated. Results The authors identified 26 patients with solid nonhematopoietic CNS metastases out of 1135 pediatric CNS tumors diagnosed from 1981 to 2011. Patients ranged in age from 1.5 to 20.3 years and were equally divided between sexes. Most CNS metastases were supratentorial (85%) and solitary (65%). The mean interval from primary malignant diagnosis to CNS metastasis was 27 months. Sites of origin included kidney/adrenal, bone/soft tissue, gonads, head and neck, lung, and liver. Mean survival after CNS involvement was 36.6 months. Overall 1-year and 5-year survival rates were 52% and 16%, respectively. Conclusions In neuropathology practice, nonhematopoietic pediatric CNS metastases are far less common than are nonhematopoietic adult CNS metastases, accounting for approximately 2% of all pediatric CNS tumors. The most common tumors to exhibit CNS metastasis are of kidney/adrenal origin, followed by those from bone/soft tissue. As expected, prognosis is dismal, despite aggressive therapy.

It is well established that the combined del(1)(p36) and del(19)(q13) is a positive prognostic molecular event in oligodendroglial tumors. However, very little is known about the frequency or impact of polysomy status for chromosomes 1/19. We examined 84consecutive pure oligodendrogliomas (68 World Health Organization [WHO] grade II and 16 WHO grade III) and analyzed them for del(1)(p36) and del(19)(q13) by fluorescent in situ hybridization. Polysomy status was recorded with accompanying deletion status, WHO grade, recurrence-free survival, and overall survival. Codeletion of 1p/19q was detected in 48% of cases and correlated with superior patient survival (p < 0.01), as expected. Of 84 cases, 36 (43%) showed polysomy of chromosome 1, 30 (36%) demonstrated polysomy of chromosome 19, and 28 (33%) had copolysomies of chromosomes 1/19. The presence of polysomy of either/or both chromosomes, regardless of deletion status, correlated with younger patient age at initial diagnosis (p < 0.01). Combined polysomy was associated with higher histologic tumor grade (p = 0.04) and conferred poor survival likelihood (p = 0.03). We conclude that polysomy of 1 and/or 19 is a relatively frequent occurrence in oligodendrogliomas and usually confers an unfavorable outcome.

Gentropy is a genetic programming system that evolves two-dimensional procedural textures. It synthesizes textures by combining mathematical and image manipulation functions into formulas. A formula can be reevaluated with arbitrary texture-space coordinates, to generate a new portion of the texture in texture space. Most evolutionary art programs are interactive, and require the user to repeatedly choose the best images from a displayed generation. Gentropy uses an unsupervised approach, where one or more target texture image is supplied to the system, and represent the desired texture features, such as colour, shape and smoothness (contrast). Then, Gentropy evolves textures independent of any further user involvement. The evolved texture will not be identical to the target texture, but rather, will exhibit characteristics similar to it. When more than one texture is supplied as a target, multi-objective feature analysis is performed. These feature tests may be combined and given different priorities during evaluation. It is therefore possible to use several target images, each with its own fitness function measuring particular visual characteristics. Gentropy also permits the use of multiple subpopulations, each of which may use its own texture evaluation criteria and target texture. r