Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

Unraveling the pathophysiological mechanisms of visceral pain in the murine model of Fabry disease

Fabry disease (FD) is an X‐linked inherited, lysosomal storage disorder characterized by a deficient activity of the enzyme α-Galactosidase A (α-Gal A). This deficiency causes an accumulation of globotriaosylceramide 3 (Gb3), in nearly all organs. Gastrointestinal (GI) symptoms are among the earliest and most frequent symptoms of FD. It has been hypothesized that Gb3 accumulation is the leading cause of these, but their pathophysiology is complex and still poorly understood. Here, we aim at understanding the molecular mechanisms underpinning the GI symptoms of FD. For this purpose, we used the α‐Gal A (-/0) male mouse, a murine model of FD, to characterize morphological and molecular features of the colon tract. Our results show that α‐Gal A (-/0) mice display a thickening of the muscular layer due to a hypertrophic state of myenteric plexus ganglia, caused by an accumulation of Gb3 in neurons. Also, α-Gal A (-/0) mice present a decreased density of mucosal nerve fibres. Furthermore, α-Gal A (-/0) mice presented visceral hyperalgesia, by showing greater visceromotor response (VMR) values and obtaining higher abdominal withdrawal reflex (AWR) scores, following colorectal distension (CRD). Subsequently, the immunoreactivity of the pain-related ion channels TRPV1, TRPV4, TRPA1 and TRPM8 was detected at level of myenteric and submucosal plexus ganglia of both the genotypes. Further studies are required to assess differences of expression between α-Gal A (-/0) and control mice. Finally, we optimized the protocols to obtain three types of primary cultures from mouse intestine to be tested electrophysiologically: a mixed culture containing neurons and glia, an enriched culture of neurons, and one of glia. In summary, we revealed alterations that are likely to be part of the pathophysiological causes of FD GI symptoms. Therefore, together with further studies, this work could help identify new therapeutic targets for the treatment of visceral pain in FD.

Development of a patient-specific model for stroke risk assessment in atrial fibrillation patients

Atrial fibrillation is associated with a five-fold increase in the risk of cerebrovascular events,being responsible of 15-18% of all strokes.The morphological and functional remodelling of the left atrium caused by atrial fibrillation favours blood stasis and, consequently, stroke risk. In this context, several clinical studies suggest that stroke risk stratification could be improved by using haemodynamic information on the left atrium (LA) and the left atrial appendage (LAA). The goal of this study was to develop a personalized computational fluid-dynamics (CFD) model of the left atrium which could clarify the haemodynamic implications of atrial fibrillation on a patient specific basis. The developed CFD model was first applied to better understand the role of LAA in stroke risk. Infact, the interplay of the LAA geometric parameters such as LAA length, tortuosity, surface area and volume with the fluid-dynamics parameters and the effects of the LAA closure have not been investigated. Results demonstrated the capabilities of the CFD model to reproduce the real physiological behaviour of the blood flow dynamics inside the LA and the LAA. Finally, we determined that the fluid-dynamics parameters enhanced in this research project could be used as new quantitative indexes to describe the different types of AF and open new scenarios for the patient-specific stroke risk stratification.