Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients

Background: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. Methods: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. Results: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3. Conclusions: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients

Qualidade de vida em portadores de esclerose múltipla

Multiple sclerosis (MS) is a chronic disease which may exert significant effects on the life of patients. Traditional outcome measures in MS lack in consider the effects of the disease on health-related quality of life (HRQoL). The goal of this study is to measure HRQoL in MS patients in the city of Uberlân-dia, State of Minas Gerais, Brazil. The Brazilian version of the SF-36 was applied in 23 MS patients and in 69 subjects of general population (blood donors) in Uberlândia. MS patients scored lower in all SF-36 scales than do the general population, principally in physical function domains. Patients with EDSS scores ≤3.5 had higher mean scores in four domains than do the patients with EDSS scores ≥4.0, and lower in all domains than control group. Depressive symptoms and heat intolerance showed correlation with SF-36 domains and components. In conclusion, MS patients have a significant negative impact on all HRQoL domains measured by SF-36, compared with general population, even in the stages with lower disability

Consenso expandido do BCTRIMS para o tratamento da esclerose múltipla: III. Diretrizes baseadas em evidências e recomendações

Em razão dos avanços no conhecimento da esclerose múltipla (EM), dos novos critérios diagnósticos e ensaios terapêuticos publicados, a atualização e expansão das diretrizes para orientação do neurologista brasileiro em relação ao tratamento da EM se tornam necessárias. O Consenso Expandido do BCTRIMS enfatiza a individualização do tratamento a critério do neurologista, e a necessidade de informação do paciente quanto aos potenciais benefícios e riscos das drogas, assim como do Consentimento Informado e compromisso de realização de controles periódicos. Na ausência de evidências científicas favorecendo uma determinada droga o médico deve considerar o custo diferencial dos imunomoduladores ao fazer sua opção terapêutica. As várias situações clínicas e os diferentes agentes modificadores da doença, assim como as outras intervenções terapêuticas de eficácia no tratamento são consideradas à luz das classes de evidências científicas e dos tipos de recomendação, aceitos pela comunidade científica internacional. O Consenso Expandido do BCTRIMS pode servir de modelo para outros países em desenvolvimento

Consenso expandido do BCTRIMS para o tratamento da esclerose múltipla: II. As evidências para o uso de glicocorticóides e imunomoduladores The BCTRIMS expanded consensus on treatment of multiple sclerosis: II. The evidences for the use of glucocorticoids and immunomodulatory treatments

A esclerose múltipla (EM) é doença inflamatória em que mecanismos imunológicos desempenham importante papel na patogênese das lesões do sistema nervoso central. Drogas que agem em diferentes etapas destes mecanismos têm sido usadas em seu tratamento. O presente artigo analisa com base nas classes de evidências científicas e tipos de recomendações propostos pela comunidade científica, os mais importantes ensaios clínico-terapêuticos com os glicocorticóides e os imunomoduladores, incluindo a imunoglobulina humana endovenosa, no tratamento da esclerose múltipla. Ele tem por objetivo fornecer subsídios para a formulação do Consenso Expandido para o Tratamento da Esclerose Múltipla.<br>Multiple sclerosis is an inflammatory disease in which immunological mechanisms play an important role in causing demyelinating lesions in the central nervous system. A number of drugs acting in different stages of these mechanisms have been tested for its treatment. This paper analyses the most important clinical trials with glucocorticoids and immunomodulatory treatments, including human immunoglobulin, using the classes of evidences and types of recommendations as have been defined and widely accepted by the international scientific community. It aims to provide sufficient information to support the BCTRIMS Expanded Consensus on Treatment of MS