Multidisciplinary approach to diagnosis and management of osteosarcoma – a review of the St Vincent's Hospital experience

BACKGROUND: Osteosarcoma is the most common primary malignant bone tumour in children and young adults. Despite advances in the diagnosis and management of osteosarcoma, there have been few recent studies describing the experiences of tertiary referral centres. This paper aims to describe and discuss the clinical features, pre-operative work-up, management and outcomes of these patients at St Vincent's Hospital (Melbourne, Australia). METHODS: Retrospective study of fifty-nine consecutive patients managed for osteosarcoma at St Vincent's Hospital between 1995 and 2005. RESULTS: Median age at diagnosis was 21 (range, 11–84) years. Gender distribution was similar, with thirty-one male and twenty-eight female patients. Twenty-five patients had osteosarcoma in the femur, eleven each were located in the humerus and tibia, six were identified in the pelvis, and one each in the clavicle, maxilla, fibula, sacrum, ulna and radius. Pre-operative tissue diagnosis of osteosarcoma was obtained through computed tomography-guided percutaneous biopsy in over ninety percent of patients. Following initial therapy, over fifty percent of patients remained relapse-free during the follow-up period, with twelve percent and twenty-seven percent of patients documented as having local and distant disease recurrence, respectively. Of patients with recurrent disease, sixty-two percent remained disease-free following subsequent surgical intervention (most commonly, pulmonary metastatectomy). CONCLUSION: Patient outcomes can be optimised through a multidisciplinary approach in a tertiary referral centre. At St Vincent's Hospital, survival and relapse rates of patients managed for osteosarcoma compare favourably with the published literature

Unusual manifestation of Erdheim-Chester disease

<p>Abstract</p> <p>Background</p> <p>Erdheim-Chester disease (ECD) is a rare multisystem non-Langerhans cell histiocytosis that is characterized histologically by xanthogranulomatous infiltrates and radiologically by symmetrical sclerosis of long bones. The xanthomatous process is characterized by prominent foamy histiocytes staining positive for CD68, occasionally for PS100 and negative for S100 and CD1a. Gastroenterological involvement is exceedingly rare.</p> <p>Case Presentation</p> <p>This case report describes the case of a 69-year-old man who presented otherwise well to the gastroenterology department with unspecific abdominal symptoms, nausea, vomiting and weight loss. ECD involving the gastrointestinal tract was confirmed clinically, radiologically and histologically.</p> <p>Conclusion</p> <p>Gastroenterological manifestation of ECD is rare but should be considered in the differential diagnosis in patients presenting with evidence of multi-organ disease and typical radiological features of Erdheim-Chester disease elsewhere.</p

The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input

Recently, we presented a study of adult neurogenesis in a simplified hippocampal memory model. The network was required to encode and decode memory patterns despite changing input statistics. We showed that additive neurogenesis was a more effective adaptation strategy compared to neuronal turnover and conventional synaptic plasticity as it allowed the network to respond to changes in the input statistics while preserving representations of earlier environments. Here we extend our model to include realistic, spatially driven input firing patterns in the form of grid cells in the entorhinal cortex. We compare network performance across a sequence of spatial environments using three distinct adaptation strategies: conventional synaptic plasticity, where the network is of fixed size but the connectivity is plastic; neuronal turnover, where the network is of fixed size but units in the network may die and be replaced; and additive neurogenesis, where the network starts out with fewer initial units but grows over time. We confirm that additive neurogenesis is a superior adaptation strategy when using realistic, spatially structured input patterns. We then show that a more biologically plausible neurogenesis rule that incorporates cell death and enhanced plasticity of new granule cells has an overall performance significantly better than any one of the three individual strategies operating alone. This adaptation rule can be tailored to maximise performance of the network when operating as either a short- or long-term memory store. We also examine the time course of adult neurogenesis over the lifetime of an animal raised under different hypothetical rearing conditions. These growth profiles have several distinct features that form a theoretical prediction that could be tested experimentally. Finally, we show that place cells can emerge and refine in a realistic manner in our model as a direct result of the sparsification performed by the dentate gyrus layer

Odor supported place cell model and goal navigation in rodents

Experiments with rodents demonstrate that visual cues play an important role in the control of hippocampal place cells and spatial navigation. Nevertheless, rats may also rely on auditory, olfactory and somatosensory stimuli for orientation. It is also known that rats can track odors or self-generated scent marks to find a food source. Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. We emphasize the importance of olfactory cues in place cell formation and show that the utility of environmental and self-generated olfactory cues, together with a mixed navigation strategy, improves goal directed navigation

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of spatial navigation, examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL. Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location, revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics. Rather than localization, the key problem in learning is generalization between past and present situations, which may not match perfectly. Accordingly, although neural populations collectively offer a precise representation of position, our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons, which allows learning about rewards to generalize over longer distances given fewer training experiences. However, work on generalization in RL suggests the underlying representation should respect the environment's layout. In particular, although it is often assumed that neurons track location in Euclidean coordinates (that a place cell's activity declines “as the crow flies” away from its peak), the relevant metric for value is geodesic: the distance along a path, around any obstacles. We formalize this intuition and present simulations showing how Euclidean, but not geodesic, representations can interfere with RL by generalizing inappropriately across barriers. Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields, which provides a new viewpoint on data concerning both spatial codes

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition