Temporal prediction of multiple sclerosis evolution from patient-centered outcomes

Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Despite the high variability of its clinical presentation, relapsing and progressive multiple sclerosis are considered the two main disease types, with the former possibly evolving into the latter. Recently, the attention of the medical community toward the use of patient-centered outcomes in multiple sclerosis has significantly increased. Such patient-friendly measures are devoted to the assessment of the impact of the disease on several domains of the patient life. In this work, we investigate on use of patient-centered outcomes to predict the evolution of the disease and to assess its impact on patients\u201a\uc4\uf4 lives. To this aim, we build a novel temporal model based on gradient boosting classification and multiple-output elastic-net regression. The model provides clinically interpretable results along with accurate predictions of the disease course evolution

The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach

Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsing-remitting (RR) and secondary progressive (SP) form of multiple sclerosis (MS), to promptly identifying information useful to predict disease progression. For our analysis, a dataset of 3398 evaluations from 810 persons with MS (PwMS) was adopted. Three steps were provided: course classification; extraction of the most relevant predictors at the next time point; prediction if the patient will experience the transition from RR to SP at the next time point. The Current Course Assignment (CCA) step correctly assigned the current MS course with an accuracy of about 86.0%. The MS course at the next time point can be predicted using the predictors selected in CCA. PROs/CAOs Evolution Prediction (PEP) followed by Future Course Assignment (FCA) was able to foresee the course at the next time point with an accuracy of 82.6%. Our results suggest that PROs and CAOs could help the clinician decision-making in their practice

Challenges in biomedical data science: data-driven solutions to clinical questions

Data are influencing every aspect of our lives, from our work activities, to our spare time and even to our health. In this regard, medical diagnosis and treatments are often supported by quantitative measures and observations, such as laboratory tests, medical imaging or genetic analysis. In medicine, as well as in several other scientific domains, the amount of data involved in each decision-making process has become overwhelming. The complexity of the phenomena under investigation and the scale of modern data collections has long superseded human analysis and insights potential

Computational methods for new clinical applications using imaging techniques

Esta tesis tiene por objetivo desarrollar diferentes métodos computacionales con aplicación clínica en varias enfermedades. De este modo, la investigación aquí presentada pretende aumentar el conocimiento sobre cómo el análisis y el estudio de los datos procedentes de técnicas de imagen pueden convertirse en un gran valor clínico para los profesionales de la medicina. Por lo tanto, dichos métodos pueden ser incorporados en la práctica clínica, lo que supone un beneficio para el paciente.Por un lado, la mejora de los diferentes dispositivos de imagen aumenta el abanico de posibilidades de análisis y presentación de los datos. Algunas técnicas de imagen arrojan directamente datos numéricos que tradicionalmente sólo se usaban para la monitorización de enfermedades. Sin embargo, dichos datos pueden ser empleados como biomarcadores tanto para el diagnóstico como para la predicción de enfermedades mediante la inteligencia artificial. Hoy en día, la inteligencia artificial se utiliza en muchos campos ya que todo lo que proporciona datos es abordable por estas nuevas tecnologías. Parece que no hay límite y se están desarrollando nuevas aplicaciones que hace sólo unas décadas parecían imposibles.Por otro lado, las técnicas de imagen nos permiten analizar diferentes partes del cuerpo humano en los respectivos pacientes y compararlas con controles sanos. Del mismo modo, con las imágenes se puede realizar el seguimiento de los tratamientos aplicados en dichos pacientes y, así, verificar su eficacia. Además, estas tecnologías, que proporcionan imágenes de alta resolución, son fáciles de usar, rentables y objetivas.Para resumir, esta tesis se ha centrado en desarrollar varias aplicaciones clínicas, basadas en los métodos numéricos descritos, que podrían ser una poderosa herramienta para aportar mayor información que ayude a los clínicos en la toma de decisiones.This thesis aims to develop different computational methods with clinical application in various diseases. In this way, the research presented here aims to increase knowledge on how the analysis and study of data from imaging techniques can be of great clinical value to medical professionals. Therefore, these methods can be incorporated into clinical practice, which is of benefit to the patient. On the one hand, the improvement of different imaging devices increases the range of possibilities for data analysis and presentation. Some imaging techniques directly yield numerical data that were traditionally only used for disease monitoring. However, these data can be used as biomarkers for both diagnosis and disease prediction using artificial intelligence. Today, artificial intelligence is used in many fields as everything that provides data can be addressed by these new technologies. There seems to be no limit and new applications are being developed that only a few decades ago seemed impossible. On the other hand, imaging techniques allow us to analyse different parts of the human body in the respective patients and compare them with healthy controls. In the same way, imaging can be used to monitor the treatments applied to these patients and, thus, verify their efficacy. Moreover, these technologies, which provide high-resolution images, are easy to use, cost-effective and objective. To summarise, this thesis has focused on developing several clinical applications, based on the described numerical methods, which could be a powerful tool to provide further information to help clinicians in decision making.<br /

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Personalized Diagnosis and Therapy for Multiple Sclerosis

We all agree that people with MS need to be cared in a profoundly personalized way. The care of the patient with MS is still based on the presence of relapses, so their successful diagnosis and treatment is fundamental and will condition the therapeutic strategies to follow with the patient. The treatment strategies are a highly controversial topic of debate that is increasingly supported by robust objective biological markers of response and that also increasingly take into account the dynamics and predictors of cognitive impairment along the disease course, which includes the adoption of new trends in the field of machine learning techniques. However, we all know that patient care goes beyond being treated with drugs and we cannot overlook reminding patients of the importance of their lifestyle behaviors that vary according to the MS phenotype, in order to improve their quality of life. Teleconsultation is a new care strategy proved to be feasible and well-received by patients with MS that will undoubtedly become reinforced because it will allow a closer follow-up of the patient without the need for displacement

Cognitive alterations in Multiple Sclerosis patients: diagnostic, prognostic, and rehabilitation aspects

Cognitive impairment is frequent in most patients with Multiple Sclerosis (MS) and affects several cognitive domains, having a significant impact on their quality of life and on their personal, social and work dimensions. An early and comprehensive neuropsychological assessment may provide relevant diagnostic, prognostic, and rehabilitative implications. The first chapter highlights the diagnostic and the prognostic aspects, with the description of a multicentric project, conducted in collaboration with MS centers of Bergamo, Montichiari, and Modena, in which were included newly-diagnosed MS patients and were evaluated their neurological, neuropsychological, neuroradiological and bioumoral outcomes. Results of this project have allowed the preparation of several sub-studies with important results: the first study highlighted how MS patients at the time of diagnosis, even in the absence of an evident cognitive impairment as clinically defined, are characterized by slight cognitive alterations as compared to healthy controls, both considering global cognitive functioning level and also specific cognitive domains. The second study has allowed the identification of two biomarkers present in the cerebrospinal fluid that are associated with cognitive alterations: the first (LIGHT) is associated with the inflammatory phase of the disease, while the second (parvalbumin) is associated with the neurodegenerative phase of the disease and also correlates with cortical thinning and physical disability, moreover with a stronger association compared to the one found with the level of neurofilament light chain (NF-L, a well-known biomarker of neurodegeneration). The third study has allowed to describe the predictive role of some inflammatory cytokines in the cerebrospinal fluid (CXCL13, CXCL12, IFNγ, TNF, TWEAK, LIGHT, sCD163) in discriminating, since the time of diagnosis, those MS patients that were more likely to develop neurologic and neuroradiologic worsening after 4-years follow-up. The second chapter addresses the importance of assessing MS patients not only with the classical neuropsychological tests but also with experimental paradigms. The first study, conducted in collaboration with the University of Florence and the University of Padua, investigated the phenomena of false memories, using a paradigm that induces memory distortions due to the strong connection between words associated with a same semantic category. Results showed that MS patients were not characterized by the expected memory distortions, probably due to weak association between nodes that compose semantic memory, because of neurodegenerative events. The second study, conducted in collaboration with the Kessler Foundation (West Orange, NJ, USA), focused on social cognition abilities: in a group of MS patients without evidence of cognitive impairment as traditionally defined was observed a performance significantly lower compared to healthy controls in tests of facial emotion recognition, theory of mind, and empathy. Moreover, it was demonstrated that these social cognition alterations were correlated specifically with the cortical lesions volume in both the amygdalae of MS patients, while no significant correlation was found with other measures of brain damage included in the study (cortical thickness and cortical lesion load in all the cerebral cortex). The third and last chapter focuses on the rehabilitative aspects, showing results from a study carried at the Buffalo Neuroimaging Analysis Center (Buffalo, NY, USA) on a group of MS patients that performed a cognitive training by using a telerehabilitation approach. The project aimed to identify neurological, psychological and neuroradiological variables able to characterize patients that can benefit more from the rehabilitation. Results showed that a relapsing-remitting disease phenotype (as compared with progressive patients), a higher personality trait of conscientiousness, a higher gray matter volume, a lower tract disruption in a network centered on precuneus and posterior cingulate, and a higher deviation in functional brain connectivity compared to healthy controls, play a key role to achieve a greater cognitive amelioration after the rehabilitative treatment

DeSSciphering systemic sclerosis : the skin and its thickness

Systemic sclerosis (SSc) is a rare, clinically heterogeneous, severe multisystem disorder characterised by autoimmunity, fibrosis and vasculopathy [Rodnan et al., 1979; Gabrielli et al., 2009]. It is one of the most disabling and disfiguring diseases among the systemic diseases and compared to other rheumatic diseases, SSc is associated with a high loss of life expectancy [Mok et al., 2011]. Raynaud's phenomenon (RP) as an abnormality of the microcirculation is the initial and heralding symptom of SSc in over 95% of patients. Skin sclerosis and internal organ involvement then mostly manifest with a variable temporal interval after the onset of RP [Walker et al., 2007; Varga et al., 2012]. Aside from the skin, multiple organ systems can be damaged by fibrotic and/or vascular complications including the gastrointestinal tract, the pulmonary parenchyma and circulation, the heart, kidney and the joints [Medsger, 1997; Gabrielli et al., 2009]. Although skin fibrosis is the cardinal feature of the disease, the progressive deterioration of internal organs determines the clinical outcome [Walker et al., 2007; Domsic et al., 2014; Nihtyanova et al., 2014]. The aims of this thesis are (1) to map the time after disease onset in terms of RP to the onset of organ manifestations in SSc and to identify predictors of an early onset of manifestations; (2) to assess the effect of smoking on the manifestation and worsening of SSc organ manifestations and (3) to assess the level of functional ability and to identify factors associated with disability. This thesis is based on the largest worldwide database for SSc, the European Scleroderma Trials and Research group (EUSTAR) registry. By today, more than 15,000 SSc patients are followed prospectively in more than 200 expert centres within the EUSTAR network. We found that organ manifestations exhibit rapid kinetics early after the onset of RP, implying that there is only a short ‘window of opportunity’ to prevent incident organ damage. Furthermore, in every organ system, half of all organ manifestations become evident rather early in the disease, i.e. within the first two years. This implies that severe complications, for instance pulmonary hypertension and interstitial lung disease, are not restricted to late disease. Risk factors, such as the SSc subtype, autoantibody profile and the patient's sex do modify the cumulative incidences of the organ manifestations but do not substantially modify the steep increase in organ complication rates during the first two years after RP onset. These results are of great importance for clinicians, who need to counsel, risk stratify and treat SSc patients early on after the diagnosis. Furthermore, the findings are of great significance for the design of therapeutics aimed to ‘widen’ the still very narrow ‘window of opportunity’. We demonstrated that the known adverse effect of smoking on the bronchial airways and alveoli is also observed in SSc patients. However, we did not observe robust adverse effects of smoking on the progression of SSc-specific pulmonary or cutaneous manifestations. This finding argues against a major role of tobacco-associated free radicals, vasoconstrictor and immunomodulatory effects in the pathogenesis of SSc vasculopathy and fibrosis. Regarding the functional ability, we found that there is a major difference between the factors driving patient perceived levels of disability and those emphasized by physicians in their disease evaluation. The patients perceive dyspnoea, gastrointestinal symptoms, pain, muscle weakness and the presence of digital ulcers as the main factors driving their level of disability. These results that objective disease severity measures as assessed by the physicians do not correlate with patient-perceived disability indicate that the many and multi-faced aetiologies of disability and quality of life in SSc are poorly understood and are therefore a clarion call to further research