Antonio Di Ieva

Aggressive pituitary adenomas—diagnosis and emerging treatments

The WHO categorizes pituitary tumours as typical adenomas, atypical adenomas and pituitary carcinomas, with typical adenomas constituting the major class. However, the WHO classification does not provide an accurate correlation between histopathological findings and clinical behaviour. Tumours lacking typical histological features are classified as atypical, but not all are clinically atypical or exhibit aggressive behaviour. Pituitary carcinomas, by definition, have craniospinal or systemic metastases, although not all display classical cytological features of malignancy. Aggressive pituitary adenomas, defined from a clinical perspective, have earlier and more frequent recurrences and can be resistant to conventional treatments. Specific biomarkers have not yet been identified that can distinguish between clinically aggressive and nonaggressive pituitary adenomas, although the antigen Ki-67 proliferation index might be of value. This Review highlights the need to develop new biomarkers to facilitate the early detection of clinically aggressive pituitary adenomas and discusses emerging markers that hold promise for their identification. Defining aggressiveness is of crucial importance for improving the management of patients by enhancing prognostic predictions and effectiveness of treatment. New drugs, such as temozolomide, have potential use in the management of these patients; anti-VEGF therapy, mTOR and tyrosine kinase inhibitors are also potentially useful in managing selected patients.

Fractals in the Neurosciences, Part II Clinical Applications and Future Perspectives

It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.

Fractal dimension as a quantitator of the microvasculature of normal and adenomatous pituitary tissue.

It is well known that angiogenesis is a complex process that accompanies neoplastic growth, but pituitary tumours are less vascularized than normal pituitary glands. Several analytical methods aimed at quantifying the vascular system in two‐dimensional histological sections have been proposed, with very discordant results. In this study we investigated the non‐Euclidean geometrical complexity of the two‐dimensional microvasculature of normal pituitary glands and pituitary adenomas by quantifying the surface fractal dimension that measures its space‐filling property. We found a statistical significant difference between the mean vascular surface fractal dimension estimated in normal versus adenomatous tissues (P = 0.01), normal versus secreting adenomatous tissues (P = 0.0003), and normal versus non‐secreting adenomatous tissues (P = 0.047), whereas the difference between the secreting and non‐secreting adenomatous tissues was not statistically significant. This study provides the first demonstration that fractal dimension is an objective and valid quantitator of the two‐dimensional geometrical complexity of the pituitary gland microvascular network in physiological and pathological states. Further studies are needed to compare the vascular surface fractal dimension estimates in different subtypes of pituitary tumours and correlate them with clinical parameters in order to evaluate whether the distribution pattern of vascular growth is related to a particular state of the pituitary gland.

Fractals in the Neurosciences, Part I General Principles and Basic Neurosciences

The natural complexity of the brain, its hierarchical structure, and the sophisticated topological architecture of the neurons organized in micronetworks and macronetworks are all factors contributing to the limits of the application of Euclidean geometry and linear dynamics to the neurosciences. The introduction of fractal geometry for the quantitative analysis and description of the geometric complexity of natural systems has been a major paradigm shift in the last decades. Nowadays, modern neurosciences admit the prevalence of fractal properties such as self-similarity in the brain at various levels of observation, from the microscale to the macroscale, in molecular, anatomic, functional, and pathological perspectives. Fractal geometry is a mathematical model that offers a universal language for the quantitative description of neurons and glial cells as well as the brain as a whole, with its complex three-dimensional structure, in all its physiopathological spectrums. For a holistic view of fractal geometry of the brain, we review here the basic concepts of fractal analysis and its main applications to the basic neurosciences.

Magnetic resonance elastography: a general overview of its current and future applications in brain imaging

Magnetic resonance elastography (MRE) has been developed over the last few years as a non-invasive means of evaluating the elasticity of biological tissues. The presence of the skull has always prevented semeiotic palpation of the brain, but MRE now offers the possibility of “palpating by imaging” in order to detect brain consistency under physiological and pathological conditions. The aim of this article is to review the current state-of-the-art of MRE imaging and discuss its possible future diagnostic applications in neuroscience.

Sperm protein 17 is expressed in human nervous system tumours

BackgroundHuman sperm protein 17 (Sp17) is a highly conserved protein that was originally isolated from a rabbit epididymal sperm membrane and testis membrane pellet. It has recently been included in the cancer/testis (CT) antigen family, and shown to be expressed in multiple myeloma and ovarian cancer. We investigated its immunolocalisation in specimens of nervous system (NS) malignancies, in order to establish its usefulness as a target for tumour-vaccine strategies.MethodsThe expression of Sp17 was assessed by means of a standardised immunohistochemical procedure [(mAb/antigen) MF1/Sp17] in formalin-fixed and paraffin embedded surgical specimens of NS malignancies, including 28 neuroectodermal primary tumours (6 astrocytomas, 16 glioblastoma multiforme, 5 oligodendrogliomas, and 1 ependymoma), 25 meningeal tumours, and five peripheral nerve sheath tumours (4 schwannomas, and 1 neurofibroma),.ResultsA number of neuroectodermal (21%) and meningeal tumours (4%) were found heterogeneously immunopositive for Sp17. None of the peripheral nerve sheath tumours was immunopositive for Sp17. The expression pattern was heterogeneous in all of the positive samples, and did not correlate with the degree of malignancy.ConclusionThe frequency of expression and non-uniform cell distribution of Sp17 suggest that it cannot be used as a unique immunotherapeutic target in NS cancer. However, our results do show the immunolocalisation of Sp17 in a proportion of NS tumour cells, but not in their non-pathological counterparts. The emerging complex function of Sp17 makes further studies necessary to clarify the link between it and immunopositive cells.

Angioarchitectural heterogeneity in human glioblastoma multiforme: A fractal-based histopathological assessment

There is currently no standard technique to objectively quantify the microvascularization of brain tumors. Fractal analysis has been proposed as a useful descriptor of tumor microvascularity. Standardization of the fractal analysis methodology could offer a new tool for this type of characterization. In this study, we applied fractal analysis to the characterization of the different angioarchitectures found in specimens of glioblastoma multiforme (GBM), the most common and most malignant type of human brain tumor. A retrospective series of 114 primary GBM specimens was carried out. To quantify neoplastic microvascularity, the level of two-dimensional geometrical complexity of the microvascular patterns was assessed using the box-counting algorithm, which estimates the microvascular fractal dimension (mvFD). mvFD makes information on the non-Euclidean space filled by vessels embedded in the tumor microenvironment available because it depends on vessel number, shape, magnitude and distribution pattern. A mean mvFD value of 1.44 ± 0.17 (range: 1.06-1.87) was found. The coefficient of variation was 44%. The high geometric variability, found objectively, in these samples reflects the angioarchitectural heterogeneity underlying GBM. The present study shows that angioarchitectural subtypes can be identified by mvFD, making this parameter a potential tool for quantifying different neoplastic microvascular patterns.

Autophagy in the endocrine glands

Autophagy is an important cellular process involving the degradation of intracellular components. Its regulation is complex and while there are many methods available, there is currently no single effective way of detecting and monitoring autophagy. It has several cellular functions that are conserved throughout the body, as well as a variety of different physiological roles depending on the context of its occurrence in the body. Autophagy is also involved in the pathology of a wide range of diseases. Within the endocrine system, autophagy has both its traditional conserved functions and specific functions. In the endocrine glands, autophagy plays a critical role in controlling intracellular hormone levels. In peptide-secreting cells of glands such as the pituitary gland, crinophagy, a specific form of autophagy, targets the secretory granules to control the levels of stored hormone. In steroid-secreting cells of glands such as the testes and adrenal gland, autophagy targets the steroid-producing organelles. The dysregulation of autophagy in the endocrine glands leads to several different endocrine diseases such as diabetes and infertility. This review aims to clarify the known roles of autophagy in the physiology of the endocrine system, as well as in various endocrine diseases.

Fractal Analysis of the Susceptibility Weighted Imaging Patterns in Malignant Brain Tumors During Antiangiogenic Treatment: Technical Report on Four Cases Serially Imaged by 7 T Magnetic Resonance During a Period of Four Weeks

OBJECTIVE The need for new and objective indexes for the neuroradiologic follow-up of brain tumors and for monitoring the effects of antiangiogenic strategies in vivo led us to perform a technical study on four patients who received computerized analysis of tumor-associated vasculature with ultra-high-field (7 T) magnetic resonance imaging (MRI). The image analysis involved the application of susceptibility weighted imaging (SWI) to evaluate vascular structures. METHODS Four patients affected by recurrent malignant brain tumors were enrolled in the present study. After the first 7-T SWI MRI procedure, the patients underwent antiangiogenic treatment with bevacizumab. The imaging was repeated every 2 weeks for a period of 4 weeks. The SWI patterns visualized in the three MRI temporal sequences were analyzed by means of a computer-aided fractal-based method to objectively quantify their geometric complexity. RESULTS In two clinically deteriorating patients we found an increase of the geometric complexity of the space-filling properties of the SWI patterns over time despite the antiangiogenic treatment. In one patient, who showed improvement with the therapy, the fractal dimension of the intratumoral structure decreased, whereas in the fourth patient, no differences were found. CONCLUSIONS The qualitative changes of the intratumoral SWI patterns during a period of 4 weeks were quantified with the fractal dimension. Because SWI patterns are also related to the presence of vascular structures, the quantification of their space-filling properties with fractal dimension seemed to be a valid tool for the in vivo neuroradiologic follow-up of brain tumors.

Euclidean and fractal geometry of microvascular networks in normal and neoplastic pituitary tissue

In geometrical terms, tumour vascularity is an exemplary anatomical system that irregularly fills a three-dimensional Euclidean space. This physical characteristic and the highly variable shapes of the vessels lead to considerable spatial and temporal heterogeneity in the delivery of oxygen, nutrients and drugs, and the removal of metabolites. Although these biological characteristics are well known, quantitative analyses of newly formed vessels in two-dimensional histological sections still fail to view their architecture as a non-Euclidean geometrical entity, thus leading to errors in visual interpretation and discordant results from different laboratories concerning the same tumour. We here review the literature concerning microvessel density estimates (a Euclidean-based approach quantifying vascularity in normal and neoplastic pituitary tissues) and compare the results. We also discuss the limitations of Euclidean quantitative analyses of vascularity and the helpfulness of a fractal geometry-based approach as a better means of quantifying normal and neoplastic pituitary microvasculature.